DeepSeek V3.1 Terminus vs Mistral Small 3.1 24B

All benchmark claims below are from our 12-test suite. Summary: DeepSeek wins 7 tests, Mistral wins 1, and 4 tests tie. Detailed walk-through: 1) structured_output — DeepSeek 5 vs Mistral 4. In our testing DeepSeek is tied for 1st (tied with 24 others) on JSON/schema compliance, meaning it’s far more reliable when you need strict format adherence. 2) strategic_analysis — DeepSeek 5 vs Mistral 3; DeepSeek is tied for 1st (with 25 others) for nuanced tradeoff reasoning, so use it for number-forward decision reasoning. 3) creative_problem_solving — DeepSeek 4 vs Mistral 2; DeepSeek ranks 9 of 54 (many tied) and produces more feasible, non-obvious ideas in our tests. 4) tool_calling — DeepSeek 3 vs Mistral 1; both perform poorly, but DeepSeek is better (rank 47/54 vs Mistral 53/54). Note: Mistral has a documented quirk — no_tool_calling — so it cannot be used for function-selection workflows in our data. 5) persona_consistency — DeepSeek 4 vs Mistral 2; DeepSeek better resists injection and keeps character. 6) agentic_planning — DeepSeek 4 vs Mistral 3; DeepSeek ranks higher for decomposition and recovery. 7) multilingual — DeepSeek 5 vs Mistral 4; DeepSeek tied for 1st across 55 models, indicating stronger non-English parity. 8) long_context — tie: both 5 and tied for 1st with many others, so both handle 30k+ token retrieval equally well in our tests. 9) constrained_rewriting — tie (both 3): equal for tight compression tasks. 10) classification — tie (both 3): neither is a standout classifier in our suite. 11) safety_calibration — tie (both 1): both score low on refusing harmful prompts vs permitting legitimate ones. 12) faithfulness — Mistral 4 vs DeepSeek 3; Mistral wins here (rank 34/55 vs DeepSeek rank 52/55), meaning Mistral sticks closer to source material and hallucinates less in our tests. Practical meaning: pick DeepSeek when you need strict formats, long-context strategic reasoning, and creative outputs; pick Mistral when faithfulness to source text and lower per-token cost or multimodal inputs matter.

Prices in the payload are per mTok (1k tokens). DeepSeek V3.1 Terminus: input $0.21 + output $0.79 = $1.00 per 1k tokens. Mistral Small 3.1 24B: input $0.35 + output $0.56 = $0.91 per 1k tokens. At typical volumes that maps to: 1M tokens/month (1,000 mTok) = $1,000 (DeepSeek) vs $910 (Mistral). 10M = $10,000 vs $9,100. 100M = $100,000 vs $91,000 — a $9,000 monthly gap at 100M tokens. The payload also shows an output-cost ratio of 1.4107 (DeepSeek output $0.79 vs Mistral output $0.56). Teams doing hundreds of millions of tokens or running large fleets of API-backed agents should weigh that $9k/month gap; smaller projects will usually prefer the model whose capabilities match the task rather than chasing the modest cost difference.

Choose DeepSeek V3.1 Terminus if you need: - Reliable structured outputs (JSON/schema) for production pipelines; - Long-context retrieval (30k+ tokens) combined with strategic analysis and creative problem solving; - Better agentic planning and tool-oriented parameter support (DeepSeek lists tool_choice/tools in supported parameters). Choose Mistral Small 3.1 24B if you need: - Higher faithfulness to source material (Mistral scores 4 vs DeepSeek 3 on faithfulness in our tests); - Lower per-1k-token spend ($0.91 vs $1.00 per 1k tokens) at scale; - Multimodal input (payload modality is text+image->text) and you do not require tool calling (payload flags no_tool_calling).