DeepSeek V3.1 vs Mistral Small 3.1 24B

Across our 12-test suite DeepSeek V3.1 wins 7 categories, ties 5, and Mistral wins none. Detailed walk-through (score, what it means, and rank context):

• Structured output: DeepSeek 5 vs Mistral 4. DeepSeek is tied for 1st (tied with 24 others out of 54) — better at JSON/schema compliance and strict format adherence for production APIs.
• Faithfulness: DeepSeek 5 vs Mistral 4. DeepSeek is tied for 1st of 55 (tied with 32) — less prone to inventing facts in our tests.
• Creative problem solving: DeepSeek 5 vs Mistral 2. DeepSeek is tied for 1st of 54 (tied with 7) — stronger at non-obvious, feasible idea generation.
• Tool calling: DeepSeek 3 vs Mistral 1. DeepSeek ranks 47 of 54; Mistral ranks 53 of 54 and has a quirk (no_tool_calling=true). For workflows that require function selection/argument accuracy, DeepSeek is usable; Mistral cannot reliably call tools.
• Agentic planning: DeepSeek 4 vs Mistral 3. DeepSeek ranks 16 of 54 (many models share the spot) — better at goal decomposition and recovery in multi-step tasks.
• Persona consistency: DeepSeek 5 vs Mistral 2. DeepSeek tied for 1st of 53 — better at staying in-character and resisting injections in chat interfaces.
• Strategic analysis: DeepSeek 4 vs Mistral 3. DeepSeek ranks 27 of 54 — stronger at nuanced tradeoff reasoning. Ties (no clear winner): constrained rewriting (3/3, rank 31 of 53 for both), classification (3/3, rank 31 of 53), long_context (5/5, tied for 1st of 55 for both), safety_calibration (1/1, both rank 32 of 55), multilingual (4/4, both rank 36 of 55). Practical meaning: pick DeepSeek when you need reliable structured outputs, factual fidelity, creative ideas, persona/chat stability, or tool-enabled agent workflows. Pick Mistral when you need very large context (128k) or multimodal inputs (text+image), but plan for the lack of tool calling and its lower scores on persona and creative benchmarks.

The payload reports costs per mTok (interpreted here as per 1,000 tokens). DeepSeek V3.1: input $0.15 /1k, output $0.75 /1k. Mistral Small 3.1 24B: input $0.35 /1k, output $0.56 /1k. Per 1,000,000 tokens (1M): DeepSeek input = $150, output = $750; Mistral input = $350, output = $560. If you treat 1M tokens as 50% input + 50% output (500k each), combined monthly cost ≈ DeepSeek $900 per 1M total tokens vs Mistral $910 per 1M total tokens. At scale: for 10M total tokens (50/50 split) DeepSeek ≈ $9,000 vs Mistral ≈ $9,100; for 100M total tokens DeepSeek ≈ $90,000 vs Mistral ≈ $91,000. Who should care: high-volume consumers (10M+ tokens/mo) will see a small absolute saving with DeepSeek (~$100 per 10M tokens in this equal-split example). If your workload skews heavily to input tokens (short prompts, large inputs), Mistral’s higher input price ($350 per 1M vs $150) becomes materially more expensive. If you produce a lot of output tokens (long generations), DeepSeek’s higher output price ($750 per 1M vs $560) increases costs faster.

Choose DeepSeek V3.1 if you need: faithful answers, strict JSON/schema outputs, stronger creative problem solving, agentic planning or tool-calling support for production automations — it wins 7 of 12 benchmarks in our tests and ranks tied for 1st in faithfulness and structured output. Choose Mistral Small 3.1 24B if you need: multimodal inputs (text+image) and an extended 128k context window for single-document retrieval or long multimodal threads — accept weaker tool calling, lower persona consistency, and slightly different input/output pricing.