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. | — | 123B |
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 | ||
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. | — | $0.40Mistral |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $2.00Mistral |
| — | $180Mistral | |
| These models have no shared benchmark scores. | ||
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. | — | 72.2% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | — | 61.3% |
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. | 60.9% | — |
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. | — | 32.6% |
| Overview | ||
| Company | Anthropic | Mistral |
| Release date | Sep 1 2026 | Dec 9 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Mythos 5.1vsGPT-6.1 SolDevstral 2 (123B)vsGPT-6.1 SolClaude Mythos 5.1vsGemini 4 ArgonDevstral 2 (123B)vsGemini 4 ArgonClaude Mythos 5.1vsMuse Spark OpenDevstral 2 (123B)vsMuse Spark OpenClaude Mythos 5.1vsGrok 4.7Devstral 2 (123B)vsGrok 4.7Claude Mythos 5.1vsDeepSeek-V4.1-Flash-1017Devstral 2 (123B)vsDeepSeek-V4.1-Flash-1017Claude Mythos 5.1vsKimi K3Devstral 2 (123B)vsKimi K3Frequently asked questions
Claude Mythos 5.1 and Devstral 2 (123B) don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Devstral 2 (123B) shipped 266 days before Claude Mythos 5.1, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos 5.1) vs 256k (Devstral 2 (123B)). Claude Mythos 5.1 is closed, while Devstral 2 (123B) is open weight.
Direct benchmark comparisons are unavailable — Claude Mythos 5.1 and Devstral 2 (123B) don't publish scores on any of the same benchmarks.