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. | — | 24B |
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 |
| 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. | 76.2% | 68% |
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. | 48% | — |
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. | — | 55.7% |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 52% | — |
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. | — | 22.5% |
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. | 31.1% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 91.9% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 81% | — |
| Overview | ||
| Company | Mistral | |
| Release date | Nov 18 2025 | Dec 9 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 3.0 ProvsClaude Haiku 5.5Devstral Small 2 (24B)vsClaude Haiku 5.5Gemini 3.0 ProvsGPT-6.1 SolDevstral Small 2 (24B)vsGPT-6.1 SolGemini 3.0 ProvsMuse Spark OpenDevstral Small 2 (24B)vsMuse Spark OpenGemini 3.0 ProvsGrok 4.7Devstral Small 2 (24B)vsGrok 4.7Gemini 3.0 ProvsDeepSeek-V4.1-Flash-1017Devstral Small 2 (24B)vsDeepSeek-V4.1-Flash-1017Gemini 3.0 ProvsKimi K3Devstral Small 2 (24B)vsKimi K3Frequently asked questions
Gemini 3.0 Pro leads Devstral Small 2 (24B) on 1 of the 1 benchmark they both report (SWE-Bench Verified). Gemini 3.0 Pro shipped 21 days before Devstral Small 2 (24B), so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 3.0 Pro) vs 256k (Devstral Small 2 (24B)). Gemini 3.0 Pro is closed, while Devstral Small 2 (24B) is open weight.
On SWE-Bench Verified, Gemini 3.0 Pro leads at 76.2% vs Devstral Small 2 (24B) at 68%.