Head-to-head comparisons
Head-to-head Comparisons
Direct provider and coding-tool comparisons in one place, with each article linked by bookmark.
Head-to-head comparison
GPT vs Claude pricing
How OpenAI and Anthropic API pricing compares at a moderate production workload, tier for tier.
GPT and Claude are the two API families most teams evaluate first, and "which is cheaper" rarely has one answer, because both providers ship a frontier tier and a lighter balanced tier with materially different per-token rates. Comparing the wrong tiers against each other — a frontier GPT model against a balanced Claude model, say — is the single most common mistake in informal pricing comparisons floating around forums and blog posts.
This page pins the comparison to tier-matched pairs (frontier vs frontier, balanced vs balanced) at a single, disclosed workload: 20M input and 4M output tokens a month, roughly what a moderate-traffic production API integration burns through. Both providers offer prompt-cache discounts on repeated context, which we apply here since most real integrations re-send system prompts and tool schemas on every call.
The table below pulls current catalogue prices at render time — it is not a snapshot baked into this page — and computes monthly cost with the same calculation engine used by the interactive Compare tool, so the numbers you see here and the numbers you get by adjusting the workload yourself always agree.
Live pricing snapshot
Workload: 20M input tokens, 4M output tokens per month · Last verified Jul 14, 2026| Quality signal | Unit price | Blended $/1M | vs cheapest | ||||
|---|---|---|---|---|---|---|---|
| Anthropic | Claude Sonnet 5 Cheapestsource | balanced | Not verified | In $2.00 / Out $10.00 per 1M tokens | $3.33/1M | $80 | — |
| OpenAI | GPT-5.6 Terrasource | balanced | 55 Artificial Analysis Intelligence Index | In $2.50 / Out $15.00 per 1M tokens | $4.58/1M | $110 | 1.4× (+$30) |
| Anthropic | Claude Opus 4.8source | frontier | 88.6% SWE-bench Verified | In $5.00 / Out $25.00 per 1M tokens | $4.58/1M | $110 | 1.4× (+$30) |
| OpenAI | GPT-5.6 Solsource | frontier | 94.6% GPQA Diamond | In $5.00 / Out $30.00 per 1M tokens | $5.42/1M | $130 | 1.6× (+$50) |
Prices are read live from the AICostCompass catalogue at page load, not hardcoded on this page, and reflect the workload above. Adjust the workload in the Compare tool to price your own usage instead.
Quality signal links to its source and is whatever third-party benchmark is actually published for that model (SWE-bench Verified, GPQA Diamond, an Elo rating, etc.) — different metrics aren't directly comparable across rows, and "Not verified" means we found no credible published score rather than that the model scores zero.
Cost by workload shape
Input and output tokens are priced separately, so the cheapest model at one token mix isn't always the cheapest at another. Same candidates, three workload shapes:
| Model | Input-heavy (RAG) | Balanced | Output-heavy (long-form) |
|---|---|---|---|
| GPT-5.6 Sol | $85 | $130 | $602.5 |
| GPT-5.6 Terra | $155 | $110 | $312.5 |
| Claude Opus 4.8 | $75cheapest | $110 | $502.5 |
| Claude Sonnet 5 | $120 | $80cheapest | $210cheapest |
What actually decides it
At this workload shape, output tokens dominate the bill for both families because output pricing runs several times input pricing on every tier. If your actual traffic is more input-heavy (large retrieved context, small answers) or more output-heavy (long-form generation), the gap between GPT and Claude can move more than the headline per-tier price difference suggests — the table below shows exactly how much, and use the Compare tool link if you want to re-run this with your own token split.
- Input-heavy (RAG, large cached context): Claude Opus 4.8 wins here — a ~90% cache-read discount on a large, mostly-repeated input volume makes the frontier tier cheaper than every balanced-tier candidate, including GPT-5.6 Terra.
- Balanced production traffic: Claude Sonnet 5 is cheapest at a typical 20M-in/4M-out mix. It has no cache discount modeled in the catalogue today, but its base per-token rate is still low enough to win outright.
- Output-heavy (long-form generation): Claude Sonnet 5 pulls further ahead — frontier output pricing on GPT-5.6 Sol and Claude Opus runs 2.5-3x Sonnet's rate, and that gap compounds fast once output tokens dominate the bill.
Cache and batch pricing mechanics
OpenAI
Cache: Cached input reads price roughly 90% below the standard input rate on models that support it (GPT-5.6 Sol does; Terra and Luna currently show no cache discount in this catalogue). Explicit cache writes carry about a 1.25x premium over the input rate rather than a discount.
Batch: Batch-API discount rates aren't yet populated in this catalogue for the OpenAI models compared here — check platform.openai.com/docs/pricing directly for current batch terms before budgeting around it.
Anthropic
Cache: Cached input reads price roughly 90% below the standard input rate on cache-enabled tiers (Claude Opus 4.8 has this modeled; Claude Sonnet 5 does not yet).
Batch: Batch processing (asynchronous, non-interactive requests) prices at roughly 50% off the standard rate on both Claude tiers compared here.
Rate limits, SLA & data residency
Cheapest-per-token is moot if you're throttled. Cost table above, capacity/policy context below.
OpenAI
Rate limits: Five spend-based usage tiers (Tier 1 at $5 spent up to Tier 5 at $1,000+ cumulative spend and 30+ days), enforced on requests and tokens per minute and per day. A new account starts at Tier 1 with meaningfully lower limits than an established Tier 5 or enterprise account.
SLA: No published uptime SLA on standard pay-as-you-go API access. Enterprise / high-commitment customers can negotiate priority processing and an SLA via sales.
Data residency: Official data residency program for at-rest processing/storage in specific regions (EU, UK, Japan, Canada, South Korea, Singapore, Australia, India, UAE), plus a Zero Data Retention option — both sales-gated for eligible enterprise customers.
Source · checked 2026-07-18Anthropic
Rate limits: Organization-level usage tiers (Start, Build, Scale, plus custom enterprise), tied to spend, with separate request- and token-per-minute ceilings. Self-service increase requests unlock once you hit roughly 50% of your current limit.
SLA: No uptime guarantee on the default Standard tier. A Priority Tier product targeted 99.5% uptime for committed-capacity contracts, though Anthropic's docs currently note it's no longer available for new purchases (existing contracts continue through term).
Data residency: Zero Data Retention available for eligible orgs. The direct API only supports "us" or "global" inference routing today — dedicated EU-only residency requires routing through AWS Bedrock or Google Vertex AI's EU endpoints instead.
Source · checked 2026-07-18Methodology
Costs are computed from each model's current per-1M-token input/output rates against the stated workload, with prompt-cache discounts applied where the provider offers them. Prices are read live from the AICostCompass catalogue (sourced from official provider pricing pages, refreshed hourly) rather than hardcoded on this page.
Frequently asked questions
Is GPT or Claude cheaper per API call?
It depends on the tier and the input/output token split, not on the provider alone — both ship a wide price range across tiers. Compare tier-matched models (frontier-to-frontier, balanced-to-balanced) at a workload close to your own rather than comparing headline "starting at" prices.
Does this include ChatGPT Plus or Claude Pro subscription pricing?
No — this page compares metered API pricing. If you are weighing consumer chat subscriptions instead of API access, see the ChatGPT Plus vs Claude Pro comparison.
Why does the ranking change when I change the token split?
Because input and output tokens are priced separately and output usually costs several times more than input, a workload with a different input/output ratio than the one shown here can flip which model is cheaper, even at the same total token count.
Related comparisons
Related guides
Head-to-head comparison
ChatGPT Plus vs Claude Pro
A flat monthly seat price is only half the comparison — here is the other half.
Both ChatGPT Plus and Claude Pro are flat-rate monthly subscriptions, which makes the sticker-price comparison trivial and also the least useful part of the decision. Subscription tiers are gated on usage limits and rate ceilings that never show up in a price table: message caps per rolling window, which model variant you actually get routed to under load, and how generous the context window is for pasted documents.
This page shows the current flat monthly price for each side, computed the same way the rest of AICostCompass prices seat-based tools, plus the questions worth answering before either price actually matters: what you're using the subscription for (day-to-day chat and writing vs. code-heavy work), how often you hit rate limits on your current plan if you already have one, and whether the difference between the two prices is large enough at your usage to justify switching.
For teams — where the seat price multiplies across a headcount — the calculation changes again, since procurement, SSO, and admin controls on business tiers can matter more than the per-seat delta. Use the live comparison below as a starting price check, not a final verdict.
Tied at this workload
Live pricing snapshot
Workload: 1 seat per month · Last verified Jul 14, 2026| Unit price | vs cheapest | ||||
|---|---|---|---|---|---|
| ChatGPT (OpenAI) | ChatGPT Plus Cheapestsource | balanced | $20.00 / seat month | $20 | — |
| Claude (Anthropic) | Claude Prosource | balanced | $20.00 / seat month | $20 | tie (1.0×) |
Prices are read live from the AICostCompass catalogue at page load, not hardcoded on this page, and reflect the workload above. Adjust the workload in the Compare tool to price your own usage instead.
Quality signal links to its source and is whatever third-party benchmark is actually published for that model (SWE-bench Verified, GPQA Diamond, an Elo rating, etc.) — different metrics aren't directly comparable across rows, and "Not verified" means we found no credible published score rather than that the model scores zero.
When the price gap actually matters
For a single individual subscriber, the monthly price gap between these two plans is usually smaller than what a single rate-limit-driven bad week costs you in workarounds. Weigh the price shown here against your actual usage pattern — heavy daily use that regularly hits caps on a cheaper plan can make the pricier plan the better economic choice, not the worse one.
Methodology
Prices shown are each provider's current published flat monthly seat price for the individual-tier plan, pulled live from the AICostCompass catalogue rather than hardcoded here. This page does not model usage limits, rate ceilings, or feature gating — only the subscription price itself.
Frequently asked questions
Which is cheaper, ChatGPT Plus or Claude Pro?
Check the live price comparison above — providers adjust subscription pricing periodically, so this page always reads the current catalogue value rather than a fixed number.
Do these subscriptions include API access?
No. ChatGPT Plus and Claude Pro are consumer chat subscriptions with their own usage limits, separate from each provider's metered API pricing. See the GPT vs Claude API pricing comparison for that.
Is a team plan a better deal than multiple individual seats?
Usually yes once you need centralized billing, SSO, or admin controls — team/business tiers on both platforms bundle those in at a per-seat price that is often close to the individual tier. Check each provider's current business pricing directly before committing a headcount.
Related guides
Head-to-head comparison
Cursor vs GitHub Copilot
Seat pricing for a 12-developer team, plus what the price alone won't tell you about either tool.
Cursor and GitHub Copilot are usually compared as if they compete on price, but they compete more on workflow: Copilot lives inside your existing editor as an assistant, while Cursor is a full editor fork built around agentic, multi-file edits. That difference shows up in how a 12-person engineering team actually uses either tool day to day, and it matters more to total cost of ownership than the per-seat gap.
This page prices both at 12 seats — a common small-team size — using each provider's current published Pro-tier rate. The number is a straightforward multiplication, but the decision underneath it is not: switching an existing team's workflow to a new editor (Cursor) carries onboarding time that a Copilot extension added to an existing editor does not, and that ramp cost rarely shows up in anyone's pricing model even though it is real spend.
If your team is choosing between the two primarily on capability rather than price, treat the seat-cost comparison here as a tiebreaker, not the deciding factor — see the build-vs-buy guide linked below for how to weigh switching cost against a tool's marginal capability gain.
Tied at this workload
Live pricing snapshot
Workload: 12 seats per month · Last verified Jul 14, 2026| Unit price | vs cheapest | ||||
|---|---|---|---|---|---|
| Cursor (Anysphere) | Cursor Pro Cheapestsource | balanced | $20.00 / seat month | $240 | — |
| GitHub Copilot | Copilot Prosource | balanced | $20.00 / seat month | $240 | tie (1.0×) |
Prices are read live from the AICostCompass catalogue at page load, not hardcoded on this page, and reflect the workload above. Adjust the workload in the Compare tool to price your own usage instead.
Quality signal links to its source and is whatever third-party benchmark is actually published for that model (SWE-bench Verified, GPQA Diamond, an Elo rating, etc.) — different metrics aren't directly comparable across rows, and "Not verified" means we found no credible published score rather than that the model scores zero.
Reading the seat-price gap
At 12 seats, most of the total cost is the seat count, not the per-seat price difference between these two tools — the plans sit close enough that team size, not vendor choice, drives the bulk of the annual spend. The workflow fit (in-editor assistant vs. dedicated agentic editor) is worth weighing at least as heavily as the number below.
Included usage, model choice & admin controls
A near-tied seat price rarely decides between these tools — included usage caps, model choice, and editor lock-in usually do. Cost table above, the rest of the picture below.
Cursor
Included usage: Pro tier includes a monthly allowance of premium-model requests, plus effectively unlimited use of a faster "auto" model-selection mode for routine completions.
Overage: Additional premium-model requests beyond the included allowance are billed pay-as-you-go once the allowance is used, rather than hard-capping access.
Model choice: Multiple frontier models (Claude, GPT, Gemini, and others) selectable per request — not locked to a single model vendor.
Editor support: Standalone VS Code fork, not an editor plugin — adopting Cursor means switching editors, not installing an extension into one your team already uses.
SSO / admin: Business/enterprise tiers (separate from the individual Pro tier priced here) add SSO, centralized billing, and admin usage controls.
Source · checked 2026-07-18GitHub Copilot
Included usage: Pro tier includes a monthly allowance of "premium requests" for agent mode and premium models, plus effectively unlimited standard code completions outside that quota.
Overage: Additional premium requests beyond the included allowance are billed pay-as-you-go per request once the allowance is used.
Model choice: Multiple underlying models selectable (OpenAI, Anthropic, Google), each consuming the premium-request quota at a model-specific multiplier rather than a flat rate.
Editor support: Extension available across many editors (VS Code, JetBrains IDEs, Neovim, Visual Studio, and more) rather than tied to one editor — the opposite tradeoff from Cursor and Windsurf, which require switching to their own editor.
SSO / admin: Business/Enterprise tiers (separate from the individual Pro tier priced here) add SSO, policy management, and centralized admin controls.
Source · checked 2026-07-18Methodology
Cost is each provider's current published Pro-tier flat monthly seat price multiplied by 12 seats, read live from the AICostCompass catalogue. This does not include usage-based overage charges some enterprise tiers apply on top of the seat price.
Frequently asked questions
Is Cursor or Copilot cheaper for a team?
See the live comparison above for current per-seat pricing at 12 seats. The gap between the two Pro tiers is typically modest relative to either tool's total cost once you factor in a team's size.
Do I need to switch editors to use Cursor?
Yes — Cursor is a VS Code fork with its own agentic editing features, while Copilot is an extension that adds AI assistance to whatever editor your team already uses. That switching cost is separate from, and often larger than, the seat-price difference.
Does either tool charge extra for heavy usage?
Some enterprise tiers on both platforms meter usage beyond the included allowance in the seat price. Check each provider's current plan page for the specific usage-based terms on the tier you're evaluating.
Related guides
Head-to-head comparison
OpenAI vs Anthropic API pricing at scale
The GPT vs Claude comparison changes shape once volume and discount eligibility enter the picture.
This is a deliberately different comparison from the general GPT vs Claude pricing page: same two providers, but scaled up to a 190M-token-a-month agentic workload — the kind a background-processing pipeline or high-traffic agent fleet generates — with batch processing and committed-use discounts applied, since both are realistically available at this volume and neither shows up at small scale.
Discount eligibility is the part most volume comparisons skip. Batch discounts require your workload to tolerate asynchronous, delayed processing rather than real-time responses; committed-use discounts require locking in a spend commitment ahead of time. Both change the effective per-token rate substantially, and both change which provider comes out ahead compared to the undiscounted, small-workload comparison.
The candidates here span budget and frontier tiers on both sides, since a high-volume pipeline is more likely to route the bulk of its traffic through a budget or fast tier and reserve the frontier tier for a smaller share of harder tasks — a single-tier comparison would understate how volume workloads are actually built.
Live pricing snapshot
Workload: 150M input tokens, 40M output tokens per month · Last verified Jul 14, 2026| Quality signal | Unit price | Blended $/1M | vs cheapest | ||||
|---|---|---|---|---|---|---|---|
| Anthropic | Claude Haiku 4.5 Cheapestsource | fast | 73.3% SWE-bench Verified | In $1.00 / Out $5.00 per 1M tokens | $0.92/1M | $175 | — |
| OpenAI | GPT-5.6 Lunasource | budget | 51 Artificial Analysis Intelligence Index | In $1.00 / Out $6.00 per 1M tokens | $2.05/1M | $390 | 2.2× (+$215) |
| Anthropic | Claude Opus 4.8source | frontier | 88.6% SWE-bench Verified | In $5.00 / Out $25.00 per 1M tokens | $4.61/1M | $875 | 5.0× (+$700) |
| OpenAI | GPT-5.6 Solsource | frontier | 94.6% GPQA Diamond | In $5.00 / Out $30.00 per 1M tokens | $10.26/1M | $1.95K | 11.1× (+$1.78K) |
Prices are read live from the AICostCompass catalogue at page load, not hardcoded on this page, and reflect the workload above. Adjust the workload in the Compare tool to price your own usage instead.
Quality signal links to its source and is whatever third-party benchmark is actually published for that model (SWE-bench Verified, GPQA Diamond, an Elo rating, etc.) — different metrics aren't directly comparable across rows, and "Not verified" means we found no credible published score rather than that the model scores zero.
What changes at this volume
The budget-tier gap between providers tends to matter more at this scale than the frontier-tier gap, simply because volume pipelines route most tokens through the cheaper tier by design. Confirm your actual eligibility for batch and committed-use pricing with each provider directly — the discount rates and minimum commitments are set by the provider, not by AICostCompass, and change independently of list price.
Rate limits, SLA & data residency
Cheapest-per-token is moot if you're throttled. Cost table above, capacity/policy context below.
OpenAI
Rate limits: Five spend-based usage tiers (Tier 1 at $5 spent up to Tier 5 at $1,000+ cumulative spend and 30+ days), enforced on requests and tokens per minute and per day. A new account starts at Tier 1 with meaningfully lower limits than an established Tier 5 or enterprise account.
SLA: No published uptime SLA on standard pay-as-you-go API access. Enterprise / high-commitment customers can negotiate priority processing and an SLA via sales.
Data residency: Official data residency program for at-rest processing/storage in specific regions (EU, UK, Japan, Canada, South Korea, Singapore, Australia, India, UAE), plus a Zero Data Retention option — both sales-gated for eligible enterprise customers.
Source · checked 2026-07-18Anthropic
Rate limits: Organization-level usage tiers (Start, Build, Scale, plus custom enterprise), tied to spend, with separate request- and token-per-minute ceilings. Self-service increase requests unlock once you hit roughly 50% of your current limit.
SLA: No uptime guarantee on the default Standard tier. A Priority Tier product targeted 99.5% uptime for committed-capacity contracts, though Anthropic's docs currently note it's no longer available for new purchases (existing contracts continue through term).
Data residency: Zero Data Retention available for eligible orgs. The direct API only supports "us" or "global" inference routing today — dedicated EU-only residency requires routing through AWS Bedrock or Google Vertex AI's EU endpoints instead.
Source · checked 2026-07-18Methodology
Costs apply each provider's current per-1M-token rates to a 150M input / 40M output token monthly workload, with batch and committed-use discount rates applied where the provider currently offers them, read live from the catalogue.
Frequently asked questions
Do batch and committed-use discounts stack?
On both OpenAI and Anthropic, yes — this page applies both where currently offered. Actual stacking rules and minimum commitment terms are set by each provider and can change; confirm current terms before budgeting against them.
Why compare budget and frontier tiers on the same page?
Because a real high-volume pipeline rarely sends every request through the most expensive model — it typically routes the bulk of traffic through a cheaper tier and reserves the frontier tier for harder cases, so a single-tier comparison would misrepresent the actual blended cost.
Is batch processing viable for my use case?
Batch discounts require tolerating asynchronous, non-real-time responses (often processed within a set window rather than instantly). It fits background jobs, bulk classification, and offline generation well; it does not fit live chat or interactive agent responses.
Related guides
Head-to-head comparison
Gemini vs Claude pricing
Google and Anthropic API pricing, tier for tier, at a moderate production workload.
Gemini gets compared to GPT far more often than to Claude, but for teams already committed to Google Cloud infrastructure, Gemini vs Claude is frequently the real decision — not Gemini vs GPT. This page runs that comparison directly at a moderate production API workload, matching Google's frontier and fast tiers against Anthropic's comparable tiers rather than mixing tiers of different capability levels.
Google's pricing model differs from Anthropic's in a detail that matters at scale: some Gemini tiers apply different rates above a per-request context-length threshold, so a workload with long prompts can cost more per token than the headline rate suggests. This page uses the workload's stated token volume for the calculation; if your real traffic runs long individual prompts, verify the threshold pricing on Google's current pricing page before relying on the headline rate.
Context window size is also part of this decision even though it does not appear in a monthly-cost number: Gemini's largest tiers ship a substantially larger context window than Claude's, which matters for workloads built around large document or codebase ingestion regardless of which side wins on price.
No shared quality tier between sides at this workload — each side shows its own cheapest option instead of a tier-matched pair.
Live pricing snapshot
Workload: 20M input tokens, 4M output tokens per month · Last verified Jul 14, 2026| Quality signal | Unit price | Blended $/1M | vs cheapest | ||||
|---|---|---|---|---|---|---|---|
| Gemini 3 Flash Cheapestsource | budget | 90.4% GPQA Diamond | In $0.50 / Out $3.00 per 1M tokens | $0.92/1M | $22 | — | |
| Anthropic | Claude Haiku 4.5source | fast | 73.3% SWE-bench Verified | In $1.00 / Out $5.00 per 1M tokens | $1.67/1M | $40 | 1.8× (+$18) |
| Anthropic | Claude Sonnet 5source | balanced | Not verified | In $2.00 / Out $10.00 per 1M tokens | $3.33/1M | $80 | 3.6× (+$58) |
| Gemini 3 Prosource | frontier | Not verified | In $2.00 / Out $12.00 per 1M tokens | $3.67/1M | $88 | 4.0× (+$66) |
Prices are read live from the AICostCompass catalogue at page load, not hardcoded on this page, and reflect the workload above. Adjust the workload in the Compare tool to price your own usage instead.
Quality signal links to its source and is whatever third-party benchmark is actually published for that model (SWE-bench Verified, GPQA Diamond, an Elo rating, etc.) — different metrics aren't directly comparable across rows, and "Not verified" means we found no credible published score rather than that the model scores zero.
Beyond the monthly number
If your workload depends on ingesting very large single documents or codebases in one call, context window ceiling can outweigh a modest per-token price difference — check both providers' current context limits, not just the cost shown here, before deciding.
Rate limits, SLA & data residency
Cheapest-per-token is moot if you're throttled. Cost table above, capacity/policy context below.
Google (Gemini)
Rate limits: Project-based usage tiers (Free, Tier 1 once billing is linked, Tier 2 above $250 spend, Tier 3 above $1,000 spend, each after a 30-day wait), enforced simultaneously on requests/tokens per minute and requests per day.
SLA: A formal SLA exists for the Gemini API on Vertex AI, but a guarantee on processed-request availability requires Provisioned Throughput (a paid reserved-capacity product) — standard pay-as-you-go has a narrower model-availability SLA.
Data residency: Jurisdictional/regional endpoints keep processing within a chosen region (e.g. US or EU); eligible enterprise customers can get zero-data-retention-equivalent contract terms via a DPA amendment that disables caching and abuse-monitoring logging.
Source · checked 2026-07-18Anthropic
Rate limits: Organization-level usage tiers (Start, Build, Scale, plus custom enterprise), tied to spend, with separate request- and token-per-minute ceilings. Self-service increase requests unlock once you hit roughly 50% of your current limit.
SLA: No uptime guarantee on the default Standard tier. A Priority Tier product targeted 99.5% uptime for committed-capacity contracts, though Anthropic's docs currently note it's no longer available for new purchases (existing contracts continue through term).
Data residency: Zero Data Retention available for eligible orgs. The direct API only supports "us" or "global" inference routing today — dedicated EU-only residency requires routing through AWS Bedrock or Google Vertex AI's EU endpoints instead.
Source · checked 2026-07-18Methodology
Costs use each provider's current per-1M-token input/output rates against the stated workload, read live from the catalogue. Long-context threshold pricing that some Gemini tiers apply above a certain prompt length is not modeled here — verify directly with Google if your prompts run long.
Frequently asked questions
Does Gemini pricing change for long prompts?
Some Gemini tiers apply a different (typically higher) per-token rate once a single request exceeds a length threshold set by Google. This page prices the stated monthly workload at the standard rate; check Google's current pricing page if your individual prompts commonly run long.
Why compare Gemini to Claude instead of GPT?
Teams already running on Google Cloud infrastructure often evaluate Gemini against Claude directly, since Gemini vs GPT is a different decision (platform-agnostic, both third-party APIs) than Gemini vs Claude, which for a GCP-committed team can hinge on infrastructure integration as much as price.
Which has the larger context window?
Check each model's context window field in the live pricing table — Google's frontier Gemini tiers have historically shipped larger context windows than Claude's, but both providers update this independently of price, so verify current figures rather than relying on past reputation.
Related comparisons
Related guides
Head-to-head comparison
Mistral vs OpenAI pricing
For teams shopping primarily on price, here is what the gap actually looks like at balanced and budget tiers.
Mistral is positioned, and usually chosen, on price and on being independently hostable — it's a common pick for teams that want lower per-token rates than the largest US labs or the option to self-host via open-weight releases. This page isolates that comparison: Mistral's balanced and budget tiers against OpenAI's comparable tiers, at the same moderate workload used across the rest of these comparison pages so the numbers are consistent with the rest of the site.
Price is genuinely the main reason to run this comparison, but it is not the only variable worth checking before switching a production workload to a cheaper model: tool-use and function-calling support, structured output reliability, and multilingual quality vary tier to tier and are not captured by a monthly-cost table. If your workload leans on any of those features heavily, validate them against the specific tier you're pricing here before treating the cost gap as the full picture.
For teams evaluating self-hosting Mistral's open-weight models instead of using the hosted API, this page prices the hosted API only — self-hosted inference cost depends on your own infrastructure and is a materially different calculation than either side shown here.
Live pricing snapshot
Workload: 20M input tokens, 4M output tokens per month · Last verified Jul 14, 2026| Quality signal | Unit price | Blended $/1M | vs cheapest | ||||
|---|---|---|---|---|---|---|---|
| Mistral | Mistral Small Cheapestsource | budget | Not verified | In $0.20 / Out $0.60 per 1M tokens | $0.27/1M | $6.4 | — |
| OpenAI | GPT-5.6 Lunasource | budget | 51 Artificial Analysis Intelligence Index | In $1.00 / Out $6.00 per 1M tokens | $1.83/1M | $44 | 6.9× (+$37.6) |
| Mistral | Mistral Large 2source | balanced | Not verified | In $2.00 / Out $6.00 per 1M tokens | $2.67/1M | $64 | 10.0× (+$57.6) |
| OpenAI | GPT-5.6 Terrasource | balanced | 55 Artificial Analysis Intelligence Index | In $2.50 / Out $15.00 per 1M tokens | $4.58/1M | $110 | 17.2× (+$103.6) |
Prices are read live from the AICostCompass catalogue at page load, not hardcoded on this page, and reflect the workload above. Adjust the workload in the Compare tool to price your own usage instead.
Quality signal links to its source and is whatever third-party benchmark is actually published for that model (SWE-bench Verified, GPQA Diamond, an Elo rating, etc.) — different metrics aren't directly comparable across rows, and "Not verified" means we found no credible published score rather than that the model scores zero.
What the price gap buys you
Mistral's per-token rates typically run below OpenAI's at comparable tiers, which is the whole reason teams shortlist it — but confirm tool-use, structured-output, and language-coverage parity for your specific use case before switching a production workload purely on the cost delta shown here.
Rate limits, SLA & data residency
Cheapest-per-token is moot if you're throttled. Cost table above, capacity/policy context below.
Mistral
Rate limits: A Free/Experiment tier (no billing card, capped monthly tokens) and a Scale (pay-as-you-go) tier whose limits increase automatically with cumulative billed spend, plus a negotiated Enterprise tier. Exact RPM/TPM figures are account-specific and shown only in Mistral's Admin Console.
SLA: Enterprise plans are described as including custom SLAs as part of a negotiated agreement; no specific public uptime percentage was found for the standard pay-as-you-go tier.
Data residency: EU-hosted by default (Mistral is an EU company), with an optional US API endpoint that hosts data in the US instead. Enterprise customers can restrict data transfers outside the EU, and self-hosted/dedicated-VPC deployment is available for full control.
Source · checked 2026-07-18OpenAI
Rate limits: Five spend-based usage tiers (Tier 1 at $5 spent up to Tier 5 at $1,000+ cumulative spend and 30+ days), enforced on requests and tokens per minute and per day. A new account starts at Tier 1 with meaningfully lower limits than an established Tier 5 or enterprise account.
SLA: No published uptime SLA on standard pay-as-you-go API access. Enterprise / high-commitment customers can negotiate priority processing and an SLA via sales.
Data residency: Official data residency program for at-rest processing/storage in specific regions (EU, UK, Japan, Canada, South Korea, Singapore, Australia, India, UAE), plus a Zero Data Retention option — both sales-gated for eligible enterprise customers.
Source · checked 2026-07-18Methodology
Costs apply each provider's current per-1M-token input/output rates to a 20M input / 4M output token monthly workload, read live from the catalogue. This page prices Mistral's hosted API only, not self-hosted open-weight inference.
Frequently asked questions
Is Mistral always cheaper than OpenAI?
At comparable tiers, Mistral's hosted API typically prices below OpenAI's — see the live table above for the current gap. Compare tier-matched models, since both providers ship a wide range across their own tiers.
Can I self-host Mistral to cut costs further?
Mistral releases open-weight models that can be self-hosted, which this page does not price — self-hosted cost depends on your own compute and is a separate calculation from the hosted-API prices shown here.
Does Mistral support the same features as OpenAI models?
Tool-use, function calling, and structured output support vary by specific model and tier on both sides. Verify feature parity for your use case against the exact tier you're pricing here, not against the provider generally.
Related guides
Head-to-head comparison
DeepSeek vs OpenAI pricing
DeepSeek built its reputation on being dramatically cheaper than the US labs — here is the actual multiple, tier for tier.
DeepSeek's entire market position is built on undercutting the major US labs on per-token price, and the gap against OpenAI is usually large enough to be the headline rather than a footnote — this page pins down exactly how large at a moderate production workload, rather than repeating the "DeepSeek is cheap" claim without a number attached.
The comparison uses DeepSeek's balanced and budget tiers against OpenAI's comparable tiers at 20M input / 4M output tokens a month. Both companies ship a range of models at different capability levels, so — as with every pairing on this site — tier-matching matters more than picking whichever headline price sounds most dramatic.
Price is the reason to look at DeepSeek, but it is not the only variable: DeepSeek is a China-based company, and enterprise buyers with data-residency or compliance requirements should check DeepSeek's current data-handling terms directly before routing production traffic there, independent of the cost comparison below.
Live pricing snapshot
Workload: 20M input tokens, 4M output tokens per month · Last verified Jul 14, 2026| Quality signal | Unit price | Blended $/1M | vs cheapest | ||||
|---|---|---|---|---|---|---|---|
| DeepSeek | DeepSeek Chat V3.2 Cheapestsource | budget | 1421 LMArena Elo (approx.) | In $0.28 / Out $0.42 per 1M tokens | $0.30/1M | $7.28 | — |
| DeepSeek | DeepSeek V4 Prosource | balanced | 80.6% SWE-bench Verified | In $0.435 / Out $0.87 per 1M tokens | $0.51/1M | $12.18 | 1.7× (+$4.9) |
| OpenAI | GPT-5.6 Terrasource | balanced | 55 Artificial Analysis Intelligence Index | In $2.50 / Out $15.00 per 1M tokens | $4.58/1M | $110 | 15.1× (+$102.72) |
1 candidate model in this comparison is not currently in the live catalogue and was excluded from the table above.
Prices are read live from the AICostCompass catalogue at page load, not hardcoded on this page, and reflect the workload above. Adjust the workload in the Compare tool to price your own usage instead.
Quality signal links to its source and is whatever third-party benchmark is actually published for that model (SWE-bench Verified, GPQA Diamond, an Elo rating, etc.) — different metrics aren't directly comparable across rows, and "Not verified" means we found no credible published score rather than that the model scores zero.
What the gap actually buys you
DeepSeek's per-token rates typically run several multiples below OpenAI's at comparable tiers — the live table below shows the exact multiple today. That gap is real and is the entire reason to shortlist DeepSeek, but independently verify tool-use reliability, output consistency, and data-handling terms for your use case before moving production traffic on cost alone.
Rate limits, SLA & data residency
Cheapest-per-token is moot if you're throttled. Cost table above, capacity/policy context below.
DeepSeek
Rate limits: No public requests-per-minute tier table — DeepSeek instead publishes a fixed concurrent-request cap per model per account, with capacity expansion available on request at no extra cost.
SLA: Not verified: no published uptime SLA was found in official DeepSeek documentation or policy pages as of the last check.
Data residency: DeepSeek's privacy policy states personal data is collected, processed, and stored in the People's Republic of China. No published enterprise zero-data-retention program or region-choice option was found.
Source · checked 2026-07-18OpenAI
Rate limits: Five spend-based usage tiers (Tier 1 at $5 spent up to Tier 5 at $1,000+ cumulative spend and 30+ days), enforced on requests and tokens per minute and per day. A new account starts at Tier 1 with meaningfully lower limits than an established Tier 5 or enterprise account.
SLA: No published uptime SLA on standard pay-as-you-go API access. Enterprise / high-commitment customers can negotiate priority processing and an SLA via sales.
Data residency: Official data residency program for at-rest processing/storage in specific regions (EU, UK, Japan, Canada, South Korea, Singapore, Australia, India, UAE), plus a Zero Data Retention option — both sales-gated for eligible enterprise customers.
Source · checked 2026-07-18Methodology
Costs apply each model's current per-1M-token input/output rate to a 20M input / 4M output token monthly workload, read live from the AICostCompass catalogue rather than hardcoded on this page.
Frequently asked questions
Is DeepSeek really that much cheaper than OpenAI?
At comparable tiers, yes, typically by a wide multiple — see the live table above for today's exact gap. Compare tier-matched models rather than DeepSeek's cheapest tier against OpenAI's most expensive one.
Is DeepSeek data hosted outside the US?
DeepSeek is a China-based company; if your organization has data-residency or compliance requirements, check DeepSeek's current data-handling and hosting terms directly — this page compares price only, not compliance posture.
How does DeepSeek compare to Claude instead of OpenAI?
See the DeepSeek vs Claude pricing comparison for that pairing — the gap and the tier-matching logic are similar, but the specific multiple differs.
Related comparisons
Related guides
Head-to-head comparison
DeepSeek vs Claude pricing
DeepSeek's reasoning tier against Claude, plus the balanced-tier gap most buyers actually ask about.
DeepSeek R1 was the release that put DeepSeek on the map outside China — a reasoning-tier model priced far below what reasoning-focused models from the major US labs typically cost. This page compares it, plus DeepSeek's balanced tier, against Claude directly, since Claude is frequently the incumbent a team is trying to displace or justify keeping.
The workload here is 20M input / 4M output tokens a month, the same moderate-production baseline used across this site's other pricing pages, so this comparison is directly stackable against the DeepSeek vs OpenAI and GPT vs Claude pages if you want to see all three providers on one consistent basis.
Reasoning-tier and balanced-tier models solve different problems — a reasoning model spends more computation per response and is priced and used differently than a general-purpose balanced model — so this page keeps DeepSeek's R1 (reasoning) and V4 Pro (balanced) as separate rows rather than collapsing them into a single "DeepSeek price."
Live pricing snapshot
Workload: 20M input tokens, 4M output tokens per month · Last verified Jul 14, 2026| Quality signal | Unit price | Blended $/1M | vs cheapest | ||||
|---|---|---|---|---|---|---|---|
| DeepSeek | DeepSeek V4 Pro Cheapestsource | balanced | 80.6% SWE-bench Verified | In $0.435 / Out $0.87 per 1M tokens | $0.51/1M | $12.18 | — |
| DeepSeek | DeepSeek R1source | reasoning | Not verified | In $0.55 / Out $2.19 per 1M tokens | $0.82/1M | $19.76 | 1.6× (+$7.58) |
| Anthropic | Claude Sonnet 5source | balanced | Not verified | In $2.00 / Out $10.00 per 1M tokens | $3.33/1M | $80 | 6.6× (+$67.82) |
1 candidate model in this comparison is not currently in the live catalogue and was excluded from the table above.
Prices are read live from the AICostCompass catalogue at page load, not hardcoded on this page, and reflect the workload above. Adjust the workload in the Compare tool to price your own usage instead.
Quality signal links to its source and is whatever third-party benchmark is actually published for that model (SWE-bench Verified, GPQA Diamond, an Elo rating, etc.) — different metrics aren't directly comparable across rows, and "Not verified" means we found no credible published score rather than that the model scores zero.
Reasoning tier vs balanced tier
DeepSeek R1 undercuts Claude by a wide margin on a per-token basis for reasoning-style workloads, but reasoning models are typically used for a smaller share of total traffic than general-purpose balanced models — check the live table for the balanced-tier gap too, since that is where most of a real bill actually lives.
Rate limits, SLA & data residency
Cheapest-per-token is moot if you're throttled. Cost table above, capacity/policy context below.
DeepSeek
Rate limits: No public requests-per-minute tier table — DeepSeek instead publishes a fixed concurrent-request cap per model per account, with capacity expansion available on request at no extra cost.
SLA: Not verified: no published uptime SLA was found in official DeepSeek documentation or policy pages as of the last check.
Data residency: DeepSeek's privacy policy states personal data is collected, processed, and stored in the People's Republic of China. No published enterprise zero-data-retention program or region-choice option was found.
Source · checked 2026-07-18Anthropic
Rate limits: Organization-level usage tiers (Start, Build, Scale, plus custom enterprise), tied to spend, with separate request- and token-per-minute ceilings. Self-service increase requests unlock once you hit roughly 50% of your current limit.
SLA: No uptime guarantee on the default Standard tier. A Priority Tier product targeted 99.5% uptime for committed-capacity contracts, though Anthropic's docs currently note it's no longer available for new purchases (existing contracts continue through term).
Data residency: Zero Data Retention available for eligible orgs. The direct API only supports "us" or "global" inference routing today — dedicated EU-only residency requires routing through AWS Bedrock or Google Vertex AI's EU endpoints instead.
Source · checked 2026-07-18Methodology
Costs apply each model's current per-1M-token input/output rate to a 20M input / 4M output token monthly workload, read live from the AICostCompass catalogue. DeepSeek R1 and DeepSeek V4 Pro are shown as separate rows since they represent different model classes (reasoning vs. balanced), not interchangeable tiers.
Frequently asked questions
Is DeepSeek R1 a direct substitute for Claude?
Not automatically — R1 is a reasoning-focused model, comparable in category to Claude's reasoning-oriented usage rather than its general-purpose balanced tier. Match the model class to your actual task before comparing price alone.
Why show two DeepSeek models on this page?
DeepSeek R1 (reasoning) and DeepSeek V4 Pro (balanced) serve different workload types and are priced differently — collapsing them into one number would misrepresent which DeepSeek model you'd actually use for a given task.
How does this compare to DeepSeek vs OpenAI?
See the DeepSeek vs OpenAI pricing comparison for that pairing at the same workload — useful if you're weighing DeepSeek against multiple incumbents rather than Claude specifically.
Related comparisons
Related guides
Head-to-head comparison
Grok vs GPT pricing
xAI's frontier and balanced tiers against OpenAI's, at a moderate production workload.
Grok gets less pricing scrutiny than GPT or Claude despite xAI shipping a genuine frontier tier and real-time X/Twitter data access most competitors don't have — this page runs the same tier-matched, disclosed-workload comparison against GPT that the rest of this site applies to every other provider pairing.
As with the other API pricing pages here, the comparison is pinned to a single workload — 20M input and 4M output tokens a month — and matches frontier-to-frontier and balanced-to-balanced rather than comparing whichever tier happens to have the lowest headline rate.
Grok's differentiator is less about being the cheapest option and more about real-time data access and integration with the X platform — worth weighing alongside the price gap shown below rather than treating this purely as a cost decision.
Live pricing snapshot
Workload: 20M input tokens, 4M output tokens per month · Last verified Jul 14, 2026| Quality signal | Unit price | Blended $/1M | vs cheapest | ||||
|---|---|---|---|---|---|---|---|
| xAI | Grok 4.3 Cheapestsource | balanced | 96% Artificial Analysis coding sub-eval | In $1.25 / Out $2.50 per 1M tokens | $1.46/1M | $35 | — |
| xAI | Grok 4.5source | frontier | Not verified | In $2.00 / Out $6.00 per 1M tokens | $2.67/1M | $64 | 1.8× (+$29) |
| OpenAI | GPT-5.6 Terrasource | balanced | 55 Artificial Analysis Intelligence Index | In $2.50 / Out $15.00 per 1M tokens | $4.58/1M | $110 | 3.1× (+$75) |
| OpenAI | GPT-5.6 Solsource | frontier | 94.6% GPQA Diamond | In $5.00 / Out $30.00 per 1M tokens | $9.17/1M | $220 | 6.3× (+$185) |
Prices are read live from the AICostCompass catalogue at page load, not hardcoded on this page, and reflect the workload above. Adjust the workload in the Compare tool to price your own usage instead.
Quality signal links to its source and is whatever third-party benchmark is actually published for that model (SWE-bench Verified, GPQA Diamond, an Elo rating, etc.) — different metrics aren't directly comparable across rows, and "Not verified" means we found no credible published score rather than that the model scores zero.
Price is not the only reason to pick either side
Check the live table for the current tier-matched gap. If real-time data access or X-platform integration is the reason Grok is on your shortlist, that capability doesn't show up in a monthly-cost number — weigh it explicitly rather than letting the cheaper option win by default.
Rate limits, SLA & data residency
Cheapest-per-token is moot if you're throttled. Cost table above, capacity/policy context below.
xAI
Rate limits: Team-level tiers set by cumulative API spend (tracked since Jan 1, 2026), with per-model request- and token-per-minute limits that unlock automatically as spend grows and don't downgrade.
SLA: Not verified: xAI references an enterprise uptime commitment, but a specific percentage could not be confirmed on an xAI-owned page as of the last check — confirm directly with xAI before relying on a number.
Data residency: Standard API data is retained roughly 30 days for abuse auditing then deleted, with no training on API inputs/outputs by default. An Enterprise Vault option adds dedicated infrastructure, customer-managed keys, and tenant isolation.
Source · checked 2026-07-18OpenAI
Rate limits: Five spend-based usage tiers (Tier 1 at $5 spent up to Tier 5 at $1,000+ cumulative spend and 30+ days), enforced on requests and tokens per minute and per day. A new account starts at Tier 1 with meaningfully lower limits than an established Tier 5 or enterprise account.
SLA: No published uptime SLA on standard pay-as-you-go API access. Enterprise / high-commitment customers can negotiate priority processing and an SLA via sales.
Data residency: Official data residency program for at-rest processing/storage in specific regions (EU, UK, Japan, Canada, South Korea, Singapore, Australia, India, UAE), plus a Zero Data Retention option — both sales-gated for eligible enterprise customers.
Source · checked 2026-07-18Methodology
Costs apply each model's current per-1M-token input/output rate to a 20M input / 4M output token monthly workload, read live from the AICostCompass catalogue.
Frequently asked questions
Is Grok cheaper than GPT?
Check the live tier-matched comparison above — the gap varies by tier and changes as either provider reprices, so this page always reads current catalogue values rather than a fixed number.
What does Grok offer that GPT does not?
Real-time access to X (Twitter) data and integration with the X platform are Grok's main differentiators beyond price — relevant if your use case needs current social/news context, less relevant for general-purpose API workloads.
Does this include ChatGPT or X Premium subscription pricing?
No — this page compares metered API pricing only, not consumer chat subscriptions.
Related comparisons
Related guides
Head-to-head comparison
Windsurf vs Cursor
Two agentic-editor rivals, priced at a 12-developer team size — plus Windsurf's premium tier most comparisons skip.
Windsurf and Cursor are the two standalone agentic code editors most teams shortlist against each other — both are VS Code forks built around multi-file, agent-driven edits rather than single-line autocomplete, which makes this a closer workflow comparison than Cursor vs. an editor-plugin tool like Copilot.
This page prices Windsurf's Pro tier against Cursor Pro at 12 seats, the team-size baseline used elsewhere on this site, and also includes Windsurf Max — a materially pricier top tier most quick comparisons leave out entirely. If your team is evaluating Windsurf specifically for its top-tier capability, the Pro-tier price alone understates what you'd actually pay.
Because both tools solve the same problem in a similar way (agentic multi-file editing in a dedicated editor), the switching-cost calculus is more about team preference and specific agent behavior than about learning a fundamentally different workflow — unlike moving from an editor-plugin tool to either of these.
Tied at this workload
Live pricing snapshot
Workload: 12 seats per month · Last verified Jul 16, 2026| Unit price | vs cheapest | ||||
|---|---|---|---|---|---|
| Windsurf (Cognition) | Windsurf Pro Cheapestsource | balanced | $20.00 / seat month | $240 | — |
| Cursor (Anysphere) | Cursor Prosource | balanced | $20.00 / seat month | $240 | tie (1.0×) |
| Windsurf (Cognition) | Windsurf Maxsource | frontier | $200.00 / seat month | $2.4K | 10.0× (+$2.16K) |
Prices are read live from the AICostCompass catalogue at page load, not hardcoded on this page, and reflect the workload above. Adjust the workload in the Compare tool to price your own usage instead.
Quality signal links to its source and is whatever third-party benchmark is actually published for that model (SWE-bench Verified, GPQA Diamond, an Elo rating, etc.) — different metrics aren't directly comparable across rows, and "Not verified" means we found no credible published score rather than that the model scores zero.
Reading the Pro-tier gap — and the Max tier
At the Pro tier, Windsurf and Cursor typically price close to each other, so the seat-cost gap alone rarely decides this one — agent behavior and team preference usually matter more. Windsurf Max is priced well above either Pro tier and is a separate decision: check the live table before assuming "Windsurf" means the Pro-tier price.
Included usage, model choice & admin controls
A near-tied seat price rarely decides between these tools — included usage caps, model choice, and editor lock-in usually do. Cost table above, the rest of the picture below.
Windsurf
Included usage: Pro tier includes a monthly allowance of agentic-action credits — usage is credit-metered rather than a flat unlimited allowance, so heavy agentic use can draw down the included quota faster than expected.
Overage: Additional credits beyond the included allowance are purchasable as top-ups, or covered by upgrading to the higher-capacity Max tier priced separately on this page.
Model choice: Multiple frontier models selectable per request, similar in breadth to Cursor.
Editor support: Standalone VS Code fork, like Cursor — the same editor-switching cost applies, not an in-place plugin for an existing editor.
SSO / admin: A separate enterprise tier (above Pro/Max) adds SSO and centralized admin controls.
Source · checked 2026-07-18Cursor
Included usage: Pro tier includes a monthly allowance of premium-model requests, plus effectively unlimited use of a faster "auto" model-selection mode for routine completions.
Overage: Additional premium-model requests beyond the included allowance are billed pay-as-you-go once the allowance is used, rather than hard-capping access.
Model choice: Multiple frontier models (Claude, GPT, Gemini, and others) selectable per request — not locked to a single model vendor.
Editor support: Standalone VS Code fork, not an editor plugin — adopting Cursor means switching editors, not installing an extension into one your team already uses.
SSO / admin: Business/enterprise tiers (separate from the individual Pro tier priced here) add SSO, centralized billing, and admin usage controls.
Source · checked 2026-07-18Methodology
Cost is each tool's current published seat price multiplied by 12 seats, read live from the AICostCompass catalogue. Enterprise-negotiated pricing and usage-based overage are not modeled here.
Frequently asked questions
Is Windsurf or Cursor cheaper for a team?
At the Pro tier, check the live comparison above — the two typically sit close together. Windsurf also offers a Max tier priced well above either Pro option, shown separately in the table.
What is Windsurf Max for?
A premium tier above Windsurf Pro, priced accordingly — relevant if your team needs its top-tier capability specifically, not a like-for-like alternative to Cursor Pro at the same price point.
How is this different from the Cursor vs Copilot comparison?
Cursor vs Copilot compares a standalone agentic editor against an in-editor plugin — a bigger workflow difference than Windsurf vs Cursor, which compares two tools built the same way.
Related comparisons
Related guides
Head-to-head comparison
JetBrains AI vs Copilot
JetBrains' entry tier undercuts Copilot by half — here is what that gap and the top tier actually look like at team size.
For teams already standardized on JetBrains IDEs (IntelliJ, PyCharm, WebStorm, and the rest), JetBrains AI Assistant is a natural alternative to bolting Copilot onto the same editors — and its entry tier is priced noticeably below Copilot Pro, which is easy to miss if you only compare the top-tier prices.
This page prices JetBrains AI's Pro and Ultimate tiers against Copilot Pro at 12 seats. The two JetBrains tiers are shown separately since the price gap between them is real — Ultimate is priced for teams that need its expanded capability, not a drop-in upgrade at a marginal cost.
Editor lock-in is the practical factor this price comparison doesn't capture: Copilot works across many editors, while JetBrains AI Assistant is built for the JetBrains ecosystem specifically. For a team already committed to JetBrains IDEs, that's not a cost, but for a team split across editors, it is a real constraint.
Live pricing snapshot
Workload: 12 seats per month · Last verified Jul 16, 2026| Unit price | vs cheapest | ||||
|---|---|---|---|---|---|
| JetBrains AI Assistant | AI Pro Cheapestsource | balanced | $10.00 / seat month | $120 | — |
| GitHub Copilot | Copilot Prosource | balanced | $20.00 / seat month | $240 | 2.0× (+$120) |
| JetBrains AI Assistant | AI Ultimatesource | frontier | $30.00 / seat month | $360 | 3.0× (+$240) |
Prices are read live from the AICostCompass catalogue at page load, not hardcoded on this page, and reflect the workload above. Adjust the workload in the Compare tool to price your own usage instead.
Quality signal links to its source and is whatever third-party benchmark is actually published for that model (SWE-bench Verified, GPQA Diamond, an Elo rating, etc.) — different metrics aren't directly comparable across rows, and "Not verified" means we found no credible published score rather than that the model scores zero.
Entry tier vs top tier
JetBrains AI's entry (Pro) tier typically prices below Copilot Pro — a real gap worth checking in the live table, not a rounding difference. If your team specifically needs JetBrains AI Ultimate's expanded capability, price that tier separately rather than assuming the entry-tier gap carries through.
Included usage, model choice & admin controls
A near-tied seat price rarely decides between these tools — included usage caps, model choice, and editor lock-in usually do. Cost table above, the rest of the picture below.
JetBrains AI
Included usage: Pro tier includes a monthly quota of AI credits covering chat, code completion, and agentic actions across supported JetBrains IDEs; Ultimate raises the included quota substantially above Pro.
Overage: Additional usage beyond the included quota can be purchased as extra credits rather than being hard-blocked.
Model choice: Access to multiple third-party models (Anthropic, OpenAI, Google) alongside JetBrains' own, selectable in-IDE.
Editor support: Built into JetBrains IDEs only (IntelliJ, PyCharm, WebStorm, Rider, and the rest of the JetBrains family) — not available in VS Code or other editors, unlike Copilot.
SSO / admin: Organization-level plans add centralized license management and admin controls above the per-seat Pro/Ultimate tiers priced here.
Source · checked 2026-07-18GitHub Copilot
Included usage: Pro tier includes a monthly allowance of "premium requests" for agent mode and premium models, plus effectively unlimited standard code completions outside that quota.
Overage: Additional premium requests beyond the included allowance are billed pay-as-you-go per request once the allowance is used.
Model choice: Multiple underlying models selectable (OpenAI, Anthropic, Google), each consuming the premium-request quota at a model-specific multiplier rather than a flat rate.
Editor support: Extension available across many editors (VS Code, JetBrains IDEs, Neovim, Visual Studio, and more) rather than tied to one editor — the opposite tradeoff from Cursor and Windsurf, which require switching to their own editor.
SSO / admin: Business/Enterprise tiers (separate from the individual Pro tier priced here) add SSO, policy management, and centralized admin controls.
Source · checked 2026-07-18Methodology
Cost is each tool's current published seat price multiplied by 12 seats, read live from the AICostCompass catalogue. Enterprise-negotiated pricing is not modeled here.
Frequently asked questions
Is JetBrains AI cheaper than Copilot?
At the entry tier, typically yes — check the live table above for the current gap. JetBrains AI Ultimate, the expanded-capability tier, is priced separately and may not undercut Copilot the same way.
Does JetBrains AI Assistant work outside JetBrains IDEs?
No — it is built specifically for the JetBrains IDE ecosystem (IntelliJ, PyCharm, WebStorm, etc.), unlike Copilot, which supports a broader range of editors. Factor that into the decision alongside price.
Why are two JetBrains AI tiers shown here?
Pro and Ultimate are priced differently for meaningfully different capability levels — showing only one would misrepresent the actual price a team might pay depending on which tier it needs.