Compare AI models
| Specifications | ||
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. | 400k | — |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.25 | $1.25 |
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. | $2.00 | $2.50 |
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. | $0.025 | $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. | $0.125OpenAI | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.00OpenAI | — |
| $150OpenAI list price | $375SpaceXSI list price | |
| Benchmarks | ||
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. | 71% | 76.7% |
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | — | 56% |
ProgramBenchProgram reconstruction — The AI receives a working program and its documentation, then builds a replacement from scratch without the original source code, internet access or decompilation. The score is the percentage of 200 programs that pass every behavioral test. We record each model's best published mini-SWE-agent result, including higher reasoning efforts where available. Partial test-pass rates and almost-solved programs do not count toward this score. Equal scores share a rank here; the official board also uses partial progress to break ties. Higher is better. | 0% | — |
Aider Polyglot (diff)Coding — editing code in six languages using the diff edit format | 71.6% | — |
τ²-bench · retailAgentic tasks — task success in the retail domain of the second-generation customer-service evaluation | 78.3% | — |
τ²-bench · airlineAgentic tasks — task success in the airline domain of the second-generation customer-service evaluation | 60% | — |
τ²-bench · telecomAgentic tasks — task success in the telecom domain of the second-generation customer-service evaluation | 74.1% | — |
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. | 16.7% | — |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | — | 53.3% |
AIME 2025Reasoning — single-answer accuracy on the 2025 invitational mathematics exam without tools | 91.1% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 82.3% | — |
HealthBench · hardHealthcare — physician rubric score on the hard subset of the original HealthBench evaluation | 40.3% | — |
CharXiv Reasoning · with toolsChart reasoning — The same chart-and-figure reasoning test, run with the AI allowed to use tools — writing code to inspect the image, for instance — rather than reading the chart unaided. Scores run higher than the unaided version, so read the two as separate tests. Higher is better. | 75.5% | — |
MMMU-Pro · standard and vision meanMultimodal reasoning — average across the standard and vision sets | 74.1% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 81.6% | — |
GraphWalks BFS (<128K)Long context — breadth-first traversal accuracy for inputs shorter than 128K tokens | 73.4% | — |
GraphWalks parents (<128K)Long context — parent-node retrieval accuracy for inputs shorter than 128K tokens | 64.3% | — |
OpenAI MRCR (2 needles) · 128KLong context — retrieval of two matching turns in the original MRCR evaluation | 84.3% | — |
OpenAI MRCR (2 needles) · 256KLong context — retrieval of two matching turns in the original MRCR evaluation | 58.8% | — |
| Overview | ||
| Company | OpenAI | SpaceXSI |
| Release date | Aug 7 2025 | Feb 17 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
GPT-5 minivsClaude Fable 5.2Grok 4.20 BetavsClaude Fable 5.2GPT-5 minivsGemini 4 CarbonGrok 4.20 BetavsGemini 4 CarbonGPT-5 minivsMuse EmberGrok 4.20 BetavsMuse EmberGPT-5 minivsDeepSeek-V4.1-Flash-1017Grok 4.20 BetavsDeepSeek-V4.1-Flash-1017GPT-5 minivsMistral Large 4Grok 4.20 BetavsMistral Large 4GPT-5 minivsKimi K3.1 ProGrok 4.20 BetavsKimi K3.1 ProFrequently asked questions
Grok 4.20 Beta leads GPT-5 mini on 1 of the 1 benchmark they both report (SWE-Bench Verified). GPT-5 mini is cheaper on both input and output: $0.25 vs $1.25 per million input tokens, and $2.00 vs $2.50 per million output tokens. Figures are base-tier rates. GPT-5 mini shipped 194 days before Grok 4.20 Beta, so benchmark comparisons should account for the intervening progress.
Published specifications for these two models are limited — see each model page for the latest details.
On SWE-Bench Verified, Grok 4.20 Beta leads at 76.7% vs GPT-5 mini at 71%.