Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | — | 21B |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 1M | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $8.00 | — |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $40.00 | — |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.20 | — |
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $0.018Darkbloom |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.068PrimeIntellect |
| $3,600Anthropic list price | $8.10Darkbloom · fp8 | |
| Benchmarks | ||
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 74.2% | 17.3% |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | — | 60.7% |
FrontierSWE V2Ultra-long-horizon coding — Engineering problems that would occupy a person for days: systems implementation, performance work, scientific computing and AI research, with up to twenty hours per task. Partial credit is awarded, because finishing one outright is still rare. Higher is better. | 69.9% | — |
FrontierCode 2.0Agentic coding — whether a model’s code changes to open-source repositories would be merged by their maintainers (version 2.0); separate from FrontierCode v1.1 scores | 49.1% | — |
Terminal-Bench-Science 0.1Agentic scientific computing — The same command-line setup as Terminal-Bench, pointed at scientific work: the AI has to drive research tooling and computational workflows through to a result, rather than administer a machine. Version 0.1 is the first release of the task set, and scores run lower than on the general board. Higher is better. | 74.6% | — |
RoboDojo SimRobotics — control of a simulated two-arm robot from camera images, average success rate | 64.9% | — |
WANDR (relaxed score)Agentic search — agentic web search, relaxed scoring | 82.1% | — |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | — | 10.9% |
AIME 2024Reasoning — single-answer accuracy on the 2024 invitational mathematics exam without tools | — | 92.1% |
AIME 2025Reasoning — single-answer accuracy on the 2025 invitational mathematics exam without tools | — | 91.7% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 71.5% |
MMMLUKnowledge — average accuracy on multilingual translations of MMLU | — | 75.7% |
MMLUGeneral knowledge — A 57-subject multiple-choice exam — history, law, medicine, maths — that was the standard measure of how much a model knows from 2020 until roughly 2024, when frontier scores crowded into the high 80s and labs moved on to harder tests. The scores here were published years apart under different testing setups, so read them as a historical record rather than a like-for-like ranking. Higher is better. | — | 85.3% |
OSWorld 2.1 · offlineAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Version 2.1, in its offline setting. Scores are not comparable with version 2.0. Higher is better. | 89.3% | — |
AutomationBenchBusiness workflows — Tests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | 51.4% | — |
HealthBench · full setHealthcare — physician rubric score on the full set of the original HealthBench evaluation | — | 42.5% |
HealthBench · hardHealthcare — physician rubric score on the hard subset of the original HealthBench evaluation | — | 10.8% |
HealthBench · consensusHealthcare — physician rubric score on the consensus subset of the original HealthBench evaluation | — | 82.6% |
AA-Briefcase v1.1Knowledge work — Artificial Analysis agentic office-work eval (Elo, v1.1 rating fit) | 1785 | — |
AA-Briefcase v1.1 (rubric)Knowledge work — Artificial Analysis agentic office-work eval, v1.1 rubric score; separate from the Elo rating | 66.1% | — |
Remote Labor Index (RLI)Knowledge work — real paid remote-work projects, scored on the share completed to client standard | 41.3% | — |
Chartography · no toolsChart tasks — The same chart-centred test with no tools: the AI has to read each chart unaided. Scores run far lower than the with-tools version, so read the two as separate tests. Higher is better. | 73.4% | — |
| Overview | ||
| Company | Anthropic | OpenAI |
| Release date | Nov 9 2026 | Aug 5 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Fable 5.2vsGemini 4 Carbongpt-oss-20bvsGemini 4 CarbonClaude Fable 5.2vsMuse Embergpt-oss-20bvsMuse EmberClaude Fable 5.2vsGrok 4.7gpt-oss-20bvsGrok 4.7Claude Fable 5.2vsDeepSeek-V4.1-Flash-1017gpt-oss-20bvsDeepSeek-V4.1-Flash-1017Claude Fable 5.2vsMistral Large 4gpt-oss-20bvsMistral Large 4Claude Fable 5.2vsKimi K3.1 Progpt-oss-20bvsKimi K3.1 ProFrequently asked questions
Claude Fable 5.2 leads gpt-oss-20b on 1 of the 1 benchmark they both report (Humanity's Last Exam). Only Claude Fable 5.2 has a verified first-party price for an available API: $8.00 per million input tokens and $40.00 per million output tokens. No currently available pay-as-you-go API rate is tracked for gpt-oss-20b. gpt-oss-20b shipped 461 days before Claude Fable 5.2, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Fable 5.2) vs 128k (gpt-oss-20b). Claude Fable 5.2 is closed, while gpt-oss-20b is open weight.
On Humanity's Last Exam · with tools, Claude Fable 5.2 leads at 74.2% vs gpt-oss-20b at 17.3%.