DeepSeek V3.1 Terminus vs Mistral Medium 3.1

Summary (our 12-test suite): Mistral Medium 3.1 wins 7 tests, DeepSeek V3.1 Terminus wins 2, and 3 tests tie. Details (scores shown are from our testing):

• structured_output: DeepSeek 5 vs Mistral 4 — DeepSeek wins and is tied for 1st on this test (tied with 24 others out of 54), meaning better JSON/schema compliance in programmatic integrations.
• creative_problem_solving: DeepSeek 4 vs Mistral 3 — DeepSeek ranks 9 of 54 (tied), so expect more non-obvious, feasible ideas from DeepSeek in brainstorming and product-design tasks.
• constrained_rewriting: DeepSeek 3 vs Mistral 5 — Mistral tied for 1st on compression/character-limit rewriting, so it’s better for aggressive summarization and strict byte-limited outputs.
• tool_calling: DeepSeek 3 vs Mistral 4 — Mistral ranks 18 of 54, so in our testing it selects functions and arguments more accurately and sequences multi-step calls more reliably.
• faithfulness: DeepSeek 3 vs Mistral 4 — Mistral’s stronger faithfulness (rank 34 of 55 vs DeepSeek rank 52) reduces hallucination risk in source-bound tasks like citing documents or data transformation.
• classification: DeepSeek 3 vs Mistral 4 — Mistral tied for 1st (with 29 others), indicating better routing, intent classification, and label accuracy in our tests.
• safety_calibration: DeepSeek 1 vs Mistral 2 — both are low, but Mistral better resists harmful requests while permitting legitimate ones (Mistral rank 12 of 55 vs DeepSeek rank 32).
• persona_consistency: DeepSeek 4 vs Mistral 5 — Mistral tied for 1st here, so it maintains character and resists prompt injection more reliably in chat scenarios.
• agentic_planning: DeepSeek 4 vs Mistral 5 — Mistral tied for 1st, showing superior goal decomposition and failure recovery in our planning tests.
• strategic_analysis: 5 vs 5 (tie) — both tied for 1st with 25 others, so nuanced tradeoff reasoning is comparable.
• long_context: 5 vs 5 (tie) — both tied for 1st with 36 others; DeepSeek has a larger context window (163,840 vs 131,072 tokens) but both scored top on retrieval at 30K+ tokens in our testing.
• multilingual: 5 vs 5 (tie) — both tied for 1st with 34 others; expect equivalent non-English quality in our tests. Interpretation for real tasks: choose Mistral for agentic pipelines, tool calling, classification, and lower-hallucination data tasks; choose DeepSeek when you need exact schema output, creative idea generation, very large contexts, or a lower operational bill.

Costs are materially different. Output pricing per 1,000 tokens: DeepSeek V3.1 Terminus $0.79, Mistral Medium 3.1 $2.00; input pricing: $0.21 vs $0.40. For output-only volume: 1M tokens = DeepSeek $790 vs Mistral $2,000; 10M = $7,900 vs $20,000; 100M = $79,000 vs $200,000. If you include inputs (assume equal input/output volume), total monthly costs become: 1M tokens = DeepSeek $1,000 vs Mistral $2,400; 10M = $10,000 vs $24,000; 100M = $100,000 vs $240,000. Teams with high throughput (chat fleets, vector DB refreshes, heavy API usage) should care: DeepSeek cuts recurring costs by ~60% at scale; projects where tool reliability, classification accuracy, or strict faithfulness matter may justify Mistral’s higher spend.

Choose DeepSeek V3.1 Terminus if you need lower-cost inference and best-in-class structured output: it costs $0.79 per MTok output (vs $2.00) and scores 5/5 on structured_output and long_context in our testing — ideal for JSON APIs, large-context retrieval, and creative problem prompts where budget matters. Choose Mistral Medium 3.1 if your priority is reliable tool-calling, classification, faithfulness, agentic planning, and persona consistency: it wins those tests in our suite and is safer on safety_calibration (2 vs 1), making it the better pick for agentic workflows, function-driven backends, and data-to-decision pipelines even at higher cost.