DeepSeek V3.2 vs Mistral Small 3.1 24B

Summary of head-to-head results from our 12-test suite: DeepSeek V3.2 wins 10 tasks, Mistral wins 0, with 2 ties (classification, long_context). Detailed comparisons (score A vs B and ranking context):

• Structured output: DeepSeek 5 (tied for 1st of 54) vs Mistral 4 (rank 26 of 54). For JSON/schema tasks DeepSeek is top-tier in our tests.
• Strategic analysis: DeepSeek 5 (tied for 1st of 54) vs Mistral 3 (rank 36 of 54). DeepSeek yields more reliable numeric trade-off reasoning.
• Constrained rewriting: DeepSeek 4 (rank 6 of 53) vs Mistral 3 (rank 31). DeepSeek handles hard character limits better in our tests.
• Creative problem solving: DeepSeek 4 (rank 9 of 54) vs Mistral 2 (rank 47). DeepSeek produces more feasible, non-obvious ideas on our prompts.
• Tool calling: DeepSeek 3 (rank 47 of 54) vs Mistral 1 (rank 53 of 54) — DeepSeek wins but both are weak versus best-in-class; note the payload flags Mistral with no_tool_calling=true, so Mistral cannot perform tool calling.
• Faithfulness: DeepSeek 5 (tied for 1st of 55) vs Mistral 4 (rank 34 of 55). DeepSeek sticks to source material more reliably in our tests.
• Safety calibration: DeepSeek 2 (rank 12 of 55) vs Mistral 1 (rank 32 of 55). DeepSeek better balances refusal/allow decisions on harmful prompts.
• Persona consistency: DeepSeek 5 (tied for 1st of 53) vs Mistral 2 (rank 51). DeepSeek resists injection and keeps character more consistently.
• Agentic planning: DeepSeek 5 (tied for 1st of 54) vs Mistral 3 (rank 42). DeepSeek is substantially stronger at goal decomposition and recovery in our testing.
• Multilingual: DeepSeek 5 (tied for 1st of 55) vs Mistral 4 (rank 36). DeepSeek delivered higher non-English quality on our multilingual prompts.
• Ties: Classification both score 3 (rank 31 of 53) and long_context both score 5 (tied for 1st of 55), so for retrieval over 30K+ tokens both models perform equally well in our suite. Practical meaning: DeepSeek is a clearly stronger all-rounder in our benchmarks—especially for structured outputs, reasoning, agentic workflows, faithfulness, and multilingual outputs—while Mistral’s main payload distinction is its multimodal modality (text+image->text) but it loses across our core 12 tests.

Per the payload: DeepSeek V3.2 charges $0.26 input + $0.38 output per 1M tokens = $0.64 per 1M tokens. Mistral Small 3.1 24B charges $0.35 input + $0.56 output = $0.91 per 1M tokens. At 1M tokens/month: DeepSeek $0.64 vs Mistral $0.91 (saves $0.27). At 10M: DeepSeek $6.40 vs Mistral $9.10 (saves $2.70). At 100M: DeepSeek $64 vs Mistral $91 (saves $27). High-volume users (10M+ tokens/month) should care: the gap scales linearly and becomes a non-trivial operational cost at 100M+ tokens/month. These numbers assume simple aggregation of input+output unit prices as listed in the payload; adjust if your usage is heavily skewed toward inputs or outputs.

Choose DeepSeek V3.2 if you need: structured JSON/schema outputs, strong strategic reasoning, reliable faithfulness, agentic planning/tool-enabled workflows, persona consistency, and a lower cost ($0.64 per 1M tokens). Choose Mistral Small 3.1 24B if you specifically require built-in multimodal support (text+image->text) and accept weaker agentic and tool behaviors and higher cost ($0.91 per 1M tokens). If your app relies heavily on tool calling or complex goal decomposition, prefer DeepSeek; if your app must parse images into text and can tolerate lower scores on our benchmarks, consider Mistral.