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. | — | 119B |
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 | 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. | — | $0.15 |
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. | — | $0.60 |
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. | — | $0.15Mistral |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.60Mistral |
| — | $60.00Mistral 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. | — | 6% |
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. | 95.5% | — |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 88% | — |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | 88% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 88% | — |
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. | 64.5% | — |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 85% | — |
| Overview | ||
| Company | Anthropic | Mistral |
| Release date | Jun 9 2026 | Mar 16 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
Claude Mythos 5vsGPT-6.1 SolMistral Small 4vsGPT-6.1 SolClaude Mythos 5vsGemini 4 ArgonMistral Small 4vsGemini 4 ArgonClaude Mythos 5vsMuse Spark OpenMistral Small 4vsMuse Spark OpenClaude Mythos 5vsGrok 4.7Mistral Small 4vsGrok 4.7Claude Mythos 5vsDeepSeek-V4.1-Flash-1017Mistral Small 4vsDeepSeek-V4.1-Flash-1017Claude Mythos 5vsKimi K3Mistral Small 4vsKimi K3Frequently asked questions
Claude Mythos 5 and Mistral Small 4 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Mistral Small 4 has a verified first-party API price: $0.15 per million input tokens and $0.60 per million output tokens. No pay-as-you-go API rate is tracked for Claude Mythos 5. Mistral Small 4 shipped 85 days before Claude Mythos 5, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos 5) vs 256k (Mistral Small 4).
Direct benchmark comparisons are unavailable — Claude Mythos 5 and Mistral Small 4 don't publish scores on any of the same benchmarks.