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 | 80B |
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 · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.15 | $0.30 |
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 | $1.50 |
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 | $0.12Parasail |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.60Mistral | $0.80Parasail |
| $60.00Mistral list price | $64.00Parasail · bf16 | |
| 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 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. | — | 44.3% |
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. | — | 70.6% |
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. | — | 62.8% |
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. | — | 36.2% |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | Mar 16 2026 | Feb 3 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Mistral Small 4vsClaude Haiku 5.5Qwen3-Coder-NextvsClaude Haiku 5.5Mistral Small 4vsGPT-6.1 SolQwen3-Coder-NextvsGPT-6.1 SolMistral Small 4vsGemini 4 ArgonQwen3-Coder-NextvsGemini 4 ArgonMistral Small 4vsMuse Spark OpenQwen3-Coder-NextvsMuse Spark OpenMistral Small 4vsGrok 4.7Qwen3-Coder-NextvsGrok 4.7Mistral Small 4vsDeepSeek-V4.1-Flash-1017Qwen3-Coder-NextvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Mistral Small 4 and Qwen3-Coder-Next don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mistral Small 4 is cheaper on both input and output: $0.15 vs $0.30 per million input tokens, and $0.60 vs $1.50 per million output tokens. Figures are base-tier rates. Qwen3-Coder-Next shipped 41 days before Mistral Small 4, so benchmark comparisons should account for the intervening progress.
Mistral Small 4 has 119B parameters, while Qwen3-Coder-Next has 80B. Context windows are 256k (Mistral Small 4) vs 256k (Qwen3-Coder-Next). Mistral Small 4 is closed, while Qwen3-Coder-Next is open weight.
Direct benchmark comparisons are unavailable — Mistral Small 4 and Qwen3-Coder-Next don't publish scores on any of the same benchmarks.