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. | 200k | 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 | |
| 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. | 33.4% | 72.2% |
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. | 46% | — |
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 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% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 59.4% | — |
| Overview | ||
| Company | Anthropic | Mistral |
| Release date | Jun 20 2024 | Dec 9 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude 3.5 SonnetvsGPT-6.1 SolDevstral 2 (123B)vsGPT-6.1 SolClaude 3.5 SonnetvsGemini 4 ArgonDevstral 2 (123B)vsGemini 4 ArgonClaude 3.5 SonnetvsMuse Spark OpenDevstral 2 (123B)vsMuse Spark OpenClaude 3.5 SonnetvsGrok 4.7Devstral 2 (123B)vsGrok 4.7Claude 3.5 SonnetvsDeepSeek-V4.1-Flash-1017Devstral 2 (123B)vsDeepSeek-V4.1-Flash-1017Claude 3.5 SonnetvsKimi K3Devstral 2 (123B)vsKimi K3Frequently asked questions
Devstral 2 (123B) leads Claude 3.5 Sonnet on 1 of the 1 benchmark they both report (SWE-Bench Verified). Claude 3.5 Sonnet shipped 537 days before Devstral 2 (123B), so benchmark comparisons should account for the intervening progress.
Context windows are 200k (Claude 3.5 Sonnet) vs 256k (Devstral 2 (123B)). Claude 3.5 Sonnet is closed, while Devstral 2 (123B) is open weight.
On SWE-Bench Verified, Devstral 2 (123B) leads at 72.2% vs Claude 3.5 Sonnet at 33.4%.