DeepSeek V3.1 vs Devstral 2 2512
For most teams building a general-purpose assistant or high-volume API product, DeepSeek V3.1 is the pragmatic pick: it wins the faithfulness, creative problem-solving, and persona consistency tests in our benchmarks while costing much less. Devstral 2 2512 wins constrained rewriting, tool calling, and multilingual tests and is the better choice when agentic coding, function selection, or extreme multilingual parity matter despite its higher cost.
deepseek
DeepSeek V3.1
Benchmark Scores
External Benchmarks
Pricing
Input
$0.150/MTok
Output
$0.750/MTok
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mistral
Devstral 2 2512
Benchmark Scores
External Benchmarks
Pricing
Input
$0.400/MTok
Output
$2.00/MTok
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Benchmark Analysis
Across our 12-test suite, the models split decisions 3-3 with 6 ties. In our testing: DeepSeek V3.1 wins creative_problem_solving (score 5 vs 4), faithfulness (5 vs 4), and persona_consistency (5 vs 4). DeepSeek's faithfulness ranks tied for 1st with 32 others out of 55 (rankingsA.faithfulness), and its creative_problem_solving and persona_consistency also sit at top ranks (both tied for 1st). Devstral 2 2512 wins constrained_rewriting (5 vs 3), tool_calling (4 vs 3), and multilingual (5 vs 4). Constrained_rewriting for Devstral is tied for 1st (with 4 others) and tool_calling places Devstral much higher (rank 18 of 54) than DeepSeek (rank 47 of 54), which matters for function-selection and argument accuracy in coding agents. Six tests are ties in our tests: structured_output (5/5), strategic_analysis (4/4), classification (3/3), long_context (5/5), safety_calibration (1/1), and agentic_planning (4/4) — meaning both models perform equivalently on schema compliance, nuanced tradeoff reasoning, routing, refusal behavior (both low on safety calibration), long-context retrieval at 30K+ tokens, and decomposition. Note the raw context windows in the payload: DeepSeek supports 32,768 tokens while Devstral supports 262,144 tokens; despite both scoring 5 on long_context in our tests, Devstral's 256K window enables workflows that need multi-hundred-thousand-token contexts. Practically: choose Devstral when tool calling, constrained-rewrite size limits, or non-English parity are critical; choose DeepSeek when faithfulness, creative idea generation, persona stability, and cost-efficiency matter.
Pricing Analysis
DeepSeek V3.1 charges $0.15 input + $0.75 output per mTok (total $0.90/mTok). Devstral 2 2512 charges $0.40 input + $2.00 output per mTok (total $2.40/mTok). Assuming the payload 'per mTok' is per 1,000 tokens, monthly costs are: 1M tokens — DeepSeek $900 vs Devstral $2,400; 10M tokens — DeepSeek $9,000 vs Devstral $24,000; 100M tokens — DeepSeek $90,000 vs Devstral $240,000. DeepSeek is 37.5% of Devstral's per-mTok cost (priceRatio 0.375), so teams with heavy throughput or tight margins should prefer DeepSeek; teams that need Devstral's coding/tooling and multilingual edge should budget for roughly 2.67x higher per-token spend.
Real-World Cost Comparison
Bottom Line
Choose DeepSeek V3.1 if you need a cost‑efficient, faithful assistant that excels at creative problem solving and maintaining consistent personas (scores: faithfulness 5, creative_problem_solving 5, persona_consistency 5) and you expect high token volumes. Choose Devstral 2 2512 if your priority is agentic coding, accurate tool calling, constrained-rewriting/compression, or full parity in non-English output (Devstral scores: constrained_rewriting 5, tool_calling 4, multilingual 5) and you can absorb ~2.7x the per-mTok cost.
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.