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 | 35B |
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. | 256k | 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. | — | $0.375 |
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. | — | $2.25 |
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. | — | $0.05Darkbloom |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.70Darkbloom |
| — | $45.00Darkbloom · fp4 | |
| 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. | 68% | 73.4% |
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% | 67.2% |
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% | 51.5% |
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | — | 49.5% |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | — | 21.4% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 86% |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | — | 78% |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | — | 75.3% |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | — | 81.7% |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | Dec 9 2025 | Apr 16 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Devstral Small 2 (24B)vsClaude Haiku 5.5Qwen3.6vsClaude Haiku 5.5Devstral Small 2 (24B)vsGPT-6.1 SolQwen3.6vsGPT-6.1 SolDevstral Small 2 (24B)vsGemini 4 ArgonQwen3.6vsGemini 4 ArgonDevstral Small 2 (24B)vsMuse Spark OpenQwen3.6vsMuse Spark OpenDevstral Small 2 (24B)vsGrok 4.7Qwen3.6vsGrok 4.7Devstral Small 2 (24B)vsDeepSeek-V4.1-Flash-1017Qwen3.6vsDeepSeek-V4.1-Flash-1017Frequently asked questions
Qwen3.6 leads Devstral Small 2 (24B) on 3 of the 3 benchmarks they both report (SWE-Bench Verified, SWE-Bench Multilingual, Terminal-Bench 2.0). Only Qwen3.6 has a verified first-party API price: $0.375 per million input tokens and $2.25 per million output tokens. No pay-as-you-go API rate is tracked for Devstral Small 2 (24B). Devstral Small 2 (24B) shipped 128 days before Qwen3.6, so benchmark comparisons should account for the intervening progress.
Devstral Small 2 (24B) has 24B parameters, while Qwen3.6 has 35B. Context windows are 256k (Devstral Small 2 (24B)) vs 256k (Qwen3.6).
On SWE-Bench Verified, Qwen3.6 leads at 73.4% vs Devstral Small 2 (24B) at 68%. On SWE-Bench Multilingual, Qwen3.6 leads at 67.2% vs Devstral Small 2 (24B) at 55.7%. On Terminal-Bench 2.0, Qwen3.6 leads at 51.5% vs Devstral Small 2 (24B) at 22.5%.