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. | — | 675B |
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. | 100k | 256k |
| 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. | — | $0.50 |
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. | — | $1.50 |
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. | — | $0.50Mistral |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $1.50Mistral |
| — | $175Mistral list price | |
| These models have no shared benchmark scores. | ||
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. | — | 2% |
HumanEvalFunction synthesis — 164 small Python problems: the AI is given a function's description and has to write the working function. This was the coding benchmark of the GPT-3.5 and GPT-4 era, before the field moved to fixing real bugs in real repositories. Higher is better. | 58.7% | — |
GSM8KGrade-school math — Grade-school maths word problems that take a few steps of arithmetic to work through. It separated the models of 2022 and 2023 sharply, then saturated. One caveat on the historical numbers: OpenAI included part of the GSM8K training set in GPT-4's pre-training mix, so GPT-4's score is not a clean few-shot result. Higher is better. | 86.7% | — |
| Overview | ||
| Company | Anthropic | Mistral |
| Release date | Aug 9 2023 | Dec 2 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Instant 1.2vsGPT-6.1 SolMistral Large 3vsGPT-6.1 SolClaude Instant 1.2vsGemini 4 ArgonMistral Large 3vsGemini 4 ArgonClaude Instant 1.2vsMuse Spark OpenMistral Large 3vsMuse Spark OpenClaude Instant 1.2vsGrok 4.7Mistral Large 3vsGrok 4.7Claude Instant 1.2vsDeepSeek-V4.1-Flash-1017Mistral Large 3vsDeepSeek-V4.1-Flash-1017Claude Instant 1.2vsKimi K3Mistral Large 3vsKimi K3Frequently asked questions
Claude Instant 1.2 and Mistral Large 3 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Mistral Large 3 has a verified first-party API price: $0.50 per million input tokens and $1.50 per million output tokens. No pay-as-you-go API rate is tracked for Claude Instant 1.2. Claude Instant 1.2 shipped 846 days before Mistral Large 3, so benchmark comparisons should account for the intervening progress.
Context windows are 100k (Claude Instant 1.2) vs 256k (Mistral Large 3). Claude Instant 1.2 is closed, while Mistral Large 3 is open weight.
Direct benchmark comparisons are unavailable — Claude Instant 1.2 and Mistral Large 3 don't publish scores on any of the same benchmarks.