DeepSeek V3.1 vs Mistral Small 4

Across our 12-test suite DeepSeek V3.1 wins 4 tests, Mistral Small 4 wins 3, and 5 tests tie. Detailed comparisons (score shown as DeepSeek / Mistral):

• Faithfulness: 5 / 4 — DeepSeek scores 5/5 and is tied for 1st with 32 other models out of 55 in our testing; Mistral ranks 34 of 55. This means DeepSeek is less likely to stray from source material in tasks that demand strict fidelity.
• Long context: 5 / 4 — DeepSeek scored 5/5 (tied for 1st with 36 others) while Mistral scored 4/5 (rank 38 of 55). Despite Mistral's larger context_window (262,144 vs DeepSeek's 32,768), DeepSeek performs better in our long-context retrieval accuracy test.
• Creative problem solving: 5 / 4 — DeepSeek 5/5 (tied for 1st with 7 others); Mistral 4/5 (rank 9). DeepSeek is stronger on non-obvious, feasible idea generation in our tasks.
• Classification: 3 / 2 — DeepSeek 3/5 (rank 31 of 53) vs Mistral 2/5 (rank 51). For routing and tagging, DeepSeek is measurably better in our tests.
• Tool calling: 3 / 4 — Mistral wins here (4/5, rank 18 of 54) vs DeepSeek (3/5, rank 47). Mistral selects functions and arguments more accurately in our function-selection benchmarks.
• Safety calibration: 1 / 2 — Mistral (2/5, rank 12 of 55) refuses harmful prompts slightly more appropriately in our safety tests; DeepSeek scored 1/5 (rank 32).
• Multilingual: 4 / 5 — Mistral ties for 1st (5/5 with 34 models); DeepSeek scored 4/5 (rank 36). For non-English parity, Mistral performs better in our multilingual evaluations.
• Structured output: 5 / 5 — both 5/5 and tied for 1st (structured JSON/schema tasks), so either model adheres well to format constraints in our tests.
• Agentic planning, persona consistency, constrained rewriting, strategic analysis: ties (both scored equally). Those domains are comparable between the two in our suite. Practical meaning: pick DeepSeek when you need faithful answers, reliable long-context retrieval, high creativity, or better classification. Pick Mistral when you need stronger tool-calling, top-tier multilingual output, slightly better safety calibration, and a lower output cost.

Costs per 1,000 tokens are explicit: DeepSeek input $0.15/1k, output $0.75/1k; Mistral input $0.15/1k, output $0.60/1k. Per 1 million tokens that is: input $150 and output $750 for DeepSeek; input $150 and output $600 for Mistral. If you send equal-volume input+output of 1M tokens each, monthly spend = DeepSeek $900 vs Mistral $750. At 10M input+10M output: DeepSeek $9,000 vs Mistral $7,500. At 100M+100M: DeepSeek $90,000 vs Mistral $75,000. The output-price gap drives the 25% premium (priceRatio 1.25 in the payload). Teams with heavy output-generation (summaries, transcripts, long responses) should care most about the extra $150 per 1M output tokens; low-output or inference-heavy workloads will see proportionally smaller differences.

Choose DeepSeek V3.1 if you need: long-context retrieval at 30K+ tokens, strict faithfulness to sources, high creative problem solving, or better classification in our tests (DeepSeek scores 5/5 on faithfulness and long_context, 5/5 creative_problem_solving). Choose Mistral Small 4 if you need: more accurate tool calling (4/5 vs DeepSeek 3/5), best-in-class multilingual outputs (5/5), modestly better safety calibration (2/5 vs 1/5), and lower output costs ($0.60 vs $0.75 per 1k tokens). If output token volume is a major cost driver, Mistral is the practical choice; if answer fidelity and long-context performance are mission-critical, DeepSeek justifies the 25% premium on output tokens.