DeepSeek V3.1 vs Mistral Small 3.2 24B
DeepSeek V3.1 is the pick if you need high-fidelity, schema-compliant outputs and robust long-context reasoning — it wins 6 of 12 benchmarks in our tests. Mistral Small 3.2 24B is substantially cheaper and outperforms DeepSeek on constrained rewriting and tool calling, making it the better cost-effective choice for function-calling and tight-rewrite tasks.
deepseek
DeepSeek V3.1
Benchmark Scores
External Benchmarks
Pricing
Input
$0.150/MTok
Output
$0.750/MTok
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mistral
Mistral Small 3.2 24B
Benchmark Scores
External Benchmarks
Pricing
Input
$0.075/MTok
Output
$0.200/MTok
modelpicker.net
Benchmark Analysis
Across our 12-test suite DeepSeek V3.1 wins 6 tests, Mistral Small 3.2 24B wins 2, and 4 tests tie. DeepSeek wins: structured_output 5 vs 4 (DeepSeek tied for 1st of 54 with a score of 5; Mistral rank 26/54) — meaning DeepSeek is more reliable at JSON/schema compliance for API responses; faithfulness 5 vs 4 (DeepSeek tied for 1st of 55) — it sticks to source material more reliably; long_context 5 vs 4 (DeepSeek tied for 1st of 55) — better retrieval accuracy in our 30K+ token tests despite a smaller raw window (DeepSeek context_window=32768 vs Mistral 128000); persona_consistency 5 vs 3 (DeepSeek tied for 1st of 53) — stronger role/identity maintenance; creative_problem_solving 5 vs 2 (DeepSeek tied for 1st of 54) — better at non-obvious feasible ideas; strategic_analysis 4 vs 2 (DeepSeek rank 27/54 vs Mistral 44/54) — superior nuanced tradeoff reasoning. Mistral wins: constrained_rewriting 4 vs 3 (Mistral rank 6/53) — better at hitting hard character limits and compression; tool_calling 4 vs 3 (Mistral rank 18/54; DeepSeek rank 47/54) — better function selection and argument accuracy in our tests. Ties (3/3 or 1/1): classification (3/3), safety_calibration (1/1), agentic_planning (4/4), multilingual (4/4) — both models perform equivalently on these tasks in our benchmarks. Implication: choose DeepSeek when fidelity, strict format, and long-document correctness matter; choose Mistral when function-calling reliability and cost-per-token are the priority.
Pricing Analysis
Per the payload, DeepSeek V3.1 charges $0.15/mTok input + $0.75/mTok output = $0.90 per 1,000 tokens. Mistral Small 3.2 24B charges $0.075/mTok input + $0.20/mTok output = $0.275 per 1,000 tokens. Assuming 1 mTok = 1,000 tokens, monthly costs are: 1M tokens => DeepSeek $900 vs Mistral $275; 10M => DeepSeek $9,000 vs Mistral $2,750; 100M => DeepSeek $90,000 vs Mistral $27,500. At these volumes the delta becomes material: organizations with heavy traffic or low-margin products should prefer Mistral for cost control; teams that generate high-value, fidelity-critical outputs (APIs returning strict JSON, long-document analysis) may justify DeepSeek’s higher price.
Real-World Cost Comparison
Bottom Line
Choose DeepSeek V3.1 if you need: strict schema/JSON outputs (structured_output 5/5, tied for 1st), faithful answers (faithfulness 5/5), long-document retrieval and persona consistency — and you can absorb higher per-token costs. Choose Mistral Small 3.2 24B if you need: lower per-token cost ($0.275/mTok), stronger tool calling (4/5, rank 18/54), or better constrained rewriting (4/5, rank 6/53) for function-heavy or space-constrained workflows.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.