DeepSeek V3.1 vs Mistral Medium 3.1

Overview: Across our 12-test suite, Mistral wins 7 categories, DeepSeek wins 3, and 2 tie. Below are each test, the scores, and the practical implication. 1) Faithfulness — DeepSeek 5 vs Mistral 4. DeepSeek is tied for 1st (tied with 32 others of 55) for sticking to source material, so prefer it when avoiding hallucinations matters. 2) Constrained_rewriting — DeepSeek 3 vs Mistral 5. Mistral is tied for 1st (tied with 4 of 53) — better for tight compression and strict character limits. 3) Safety_calibration — DeepSeek 1 vs Mistral 2. Mistral ranks higher (rank 12 of 55 vs DeepSeek rank 32 of 55), so it more reliably refuses harmful prompts while permitting legitimate ones. 4) Tool_calling — DeepSeek 3 vs Mistral 4. Mistral ranks 18 of 54 (vs DeepSeek 47 of 54), indicating stronger function selection and argument sequencing for agents and tool chains. 5) Structured_output — DeepSeek 5 vs Mistral 4. DeepSeek ties for 1st (with 24 others), so it better follows JSON schemas and strict formats. 6) Agentic_planning — DeepSeek 4 vs Mistral 5. Mistral ties for 1st (with 14 others), making it stronger at goal decomposition and recovery in multi-step agents. 7) Multilingual — DeepSeek 4 vs Mistral 5. Mistral ties for 1st (with 34 others), so it gives higher-quality non-English outputs in our tests. 8) Classification — DeepSeek 3 vs Mistral 4. Mistral ties for 1st (with 29 others), so it’s preferable for routing and labeling tasks. 9) Long_context — DeepSeek 5 vs Mistral 5. Both tie for 1st (tied with 36 others in 55) — both handle 30K+ token retrieval well. 10) Persona_consistency — DeepSeek 5 vs Mistral 5. Both tie for 1st (36 others), so dialog continuity is comparable. 11) Strategic_analysis — DeepSeek 4 vs Mistral 5. Mistral is tied for 1st (with 25 others), so it provides stronger nuanced tradeoff reasoning for decisions. 12) Creative_problem_solving — DeepSeek 5 vs Mistral 3. DeepSeek ties for 1st (with 7 others) and is better at non-obvious, feasible idea generation. Practical summary: choose Mistral for agentic, classification, multilingual, and constrained-rewrite tasks; choose DeepSeek for strict schema outputs, faithfulness, and creative ideation. Long-context and persona consistency are equivalent in our tests.

Token prices (from the payload): DeepSeek V3.1 input $0.15 / output $0.75 per M-token; Mistral Medium 3.1 input $0.40 / output $2.00 per M-token. A simple combined example (1M input + 1M output tokens): DeepSeek = $0.15 + $0.75 = $0.90; Mistral = $0.40 + $2.00 = $2.40. Scale that linearly: for 10M in+out DeepSeek = $9, Mistral = $24; for 100M in+out DeepSeek = $90, Mistral = $240. The gap widens when your workload is output-heavy because output rates are higher ($0.75 vs $2.00). High-volume apps (>=10M in+out M-tokens/month), or apps that generate long outputs (summaries, reports) should care about the cost gap; DeepSeek saves $1.50 per combined M-token in our example ($2.40 - $0.90 = $1.50), amounting to $150/month at 100M in+out tokens.

Choose DeepSeek V3.1 if you need schema-compliant JSON, high faithfulness, or creative brainstorming at scale and want much lower token costs (input $0.15 / output $0.75). Choose Mistral Medium 3.1 if you prioritize strategic analysis, tool calling/agent workflows, constrained rewriting, classification, or multilingual quality — it wins 7 of 12 benchmarks despite higher costs (input $0.40 / output $2.00).