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. | 671B | 119B |
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. | 128k | 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. | — | $0.15 |
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 |
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. | $0.27SiliconFlow | $0.15Mistral |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.00SiliconFlow | $0.60Mistral |
| $104SiliconFlow · fp8 | $60.00Mistral list price | |
| 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 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. | 68.4% | — |
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. | 57.8% | — |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | 74.9% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 80.7% | — |
| Overview | ||
| Company | DeepSeek | Mistral |
| Release date | Sep 22 2025 | Mar 16 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.1 TerminusvsClaude Haiku 5.5Mistral Small 4vsClaude Haiku 5.5DeepSeek V3.1 TerminusvsGPT-6.1 SolMistral Small 4vsGPT-6.1 SolDeepSeek V3.1 TerminusvsGemini 4 ArgonMistral Small 4vsGemini 4 ArgonDeepSeek V3.1 TerminusvsMuse Spark OpenMistral Small 4vsMuse Spark OpenDeepSeek V3.1 TerminusvsGrok 4.7Mistral Small 4vsGrok 4.7DeepSeek V3.1 TerminusvsKimi K3Mistral Small 4vsKimi K3Frequently asked questions
DeepSeek V3.1 Terminus and Mistral Small 4 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Mistral Small 4 has a verified first-party API price: $0.15 per million input tokens and $0.60 per million output tokens. No pay-as-you-go API rate is tracked for DeepSeek V3.1 Terminus. DeepSeek V3.1 Terminus shipped 175 days before Mistral Small 4, so benchmark comparisons should account for the intervening progress.
DeepSeek V3.1 Terminus has 671B parameters, while Mistral Small 4 has 119B. Context windows are 128k (DeepSeek V3.1 Terminus) vs 256k (Mistral Small 4). DeepSeek V3.1 Terminus is open weight, while Mistral Small 4 is closed.
Direct benchmark comparisons are unavailable — DeepSeek V3.1 Terminus and Mistral Small 4 don't publish scores on any of the same benchmarks.