DeepSeek V3.2 vs Mistral Medium 3.1

Across our 12-test suite DeepSeek V3.2 and Mistral Medium 3.1 each win three tests and tie on six (see win/tie list). Test-by-test:

• Structured output: DeepSeek 5 vs Mistral 4 — DeepSeek wins and ranks "tied for 1st with 24 other models out of 54 tested", so JSON/schema compliance is top-tier in our testing. Mistral ranks "rank 26 of 54 (27 models share this score)" here.
• Faithfulness: DeepSeek 5 vs Mistral 4 — DeepSeek is stronger at sticking to source material (DeepSeek tied for 1st with 32 others; Mistral ranks 34 of 55). That matters if you need low hallucination.
• Creative problem solving: DeepSeek 4 vs Mistral 3 — DeepSeek ranks 9 of 54 vs Mistral 30 of 54, so it gives more non-obvious, feasible ideas in our tests.
• Constrained rewriting: Mistral 5 vs DeepSeek 4 — Mistral is better compressing or fitting hard character limits (Mistral tied for 1st with 4 others; DeepSeek rank 6 of 53). Use Mistral when tight-length rewriting is critical.
• Tool calling: Mistral 4 vs DeepSeek 3 — Mistral wins and ranks "rank 18 of 54 (29 models share this score)" while DeepSeek ranks "rank 47 of 54 (6 models share this score)"; in our tests Mistral selects functions and arguments more accurately.
• Classification: Mistral 4 vs DeepSeek 3 — Mistral tied for 1st with 29 others; DeepSeek ranks 31 of 53. For routing or tagging pipelines, Mistral performed better in our suite.
• Ties (no winner): strategic_analysis (5/5), long_context (both 5, both tied for 1st), safety_calibration (2/2), persona_consistency (5/5), agentic_planning (5/5), multilingual (5/5). These ties indicate similar capability on long-context handling, multilingual output, goal decomposition and basic safety refusal behavior in our tests. In short: DeepSeek is stronger for structured outputs, faithfulness and creativity; Mistral is stronger for tool integrations, classification, and constrained rewriting. Use the ranking displays above to see how each win places the model among our 52–55 tested models.

Raw per-1k (mTok) rates: DeepSeek V3.2 charges $0.26 input / $0.38 output; Mistral Medium 3.1 charges $0.40 input / $2.00 output. Example monthly bills (assumes 50% input / 50% output tokens):

• 1M tokens (1,000 mTok): DeepSeek = 500mTok$0.26 + 500mTok$0.38 = $320; Mistral = 500*$0.40 + 500*$2.00 = $1,200.
• 10M tokens (10,000 mTok): DeepSeek = $3,200; Mistral = $12,000.
• 100M tokens (100,000 mTok): DeepSeek = $32,000; Mistral = $120,000. If your workload is output-heavy (e.g., 80% output), the gap widens — Mistral’s $2.00 output rate makes it ~5x more expensive on output than DeepSeek. Teams with large volumes (>=10M tokens/month), embedded assistants, or cost-sensitive consumer apps should care most; DeepSeek reduces operating cost substantially, while Mistral’s higher output price may only be justified when its specific wins (tool calling, classification, constrained rewriting) materially improve product outcomes.

Choose DeepSeek V3.2 if: you need reliable JSON/schema output, high faithfulness, strong long-context performance and much lower operating cost (input $0.26 / output $0.38 per 1k). Good for production chatbots, data pipelines that require structured outputs, and volume-sensitive deployments. Choose Mistral Medium 3.1 if: your product depends on accurate tool calling (function selection & arguments), high-throughput classification, or strict constrained rewriting and you can absorb higher output costs (input $0.40 / output $2.00 per 1k). Good for agentic workflows where tool integration accuracy outweighs cost.