DeepSeek V3.1 Terminus vs Devstral 2 2512

Score-by-score (our 12-test suite): • Structured output: tie, both 5 — both models reliably follow JSON/schema constraints (each tied for 1st). • Creative problem solving: tie, both 4 — similar ability to produce feasible, non-obvious ideas (rank 9 of 54). • Classification: tie, both 3 — comparable routing/categorization performance (rank 31 of 53). • Long context: tie, both 5 — both excel at retrieval over 30K+ tokens (tied for 1st). • Safety calibration: tie, both 1 — both models scored poorly on calibration in our tests (rank 32 of 55); expect conservative safety behavior gaps. • Persona consistency: tie, both 4 — similar character maintenance (rank 38 of 53). • Agentic planning: tie, both 4 — comparable goal decomposition/failure recovery (rank 16 of 54). • Multilingual: tie, both 5 — both are top-tier for non-English parity (tied for 1st). Where they diverge: • Strategic analysis: DeepSeek wins (5 vs 4). DeepSeek ranks tied for 1st on nuanced tradeoff reasoning — better for numeric tradeoffs and multi-criteria decisions. • Tool calling: Devstral wins (4 vs 3). Devstral ranks 18 of 54 vs DeepSeek 47 of 54 — Devstral is materially better at selecting functions, populating arguments, and sequencing calls (important for agent pipelines and code-execution flows). • Faithfulness: Devstral wins (4 vs 3). Devstral's faithfulness rank (34 of 55) vs DeepSeek (52 of 55) shows fewer source-hallucinations in our tests. • Constrained rewriting: Devstral wins (5 vs 3). Devstral is tied for 1st here — superior at hard-length compression and strict character budgets. Practical meaning: pick Devstral for agentic coding, tool-integrated flows, and strict-rewrite tasks; pick DeepSeek for complex strategic analysis plus significantly lower runtime cost. Both models share top ranks on long-context, structured output, and multilingual tasks, so basic JSON schema work, large-context retrieval, and non-English workloads perform well on either model.

Pricing per 1k tokens: DeepSeek V3.1 Terminus charges $0.21 input / $0.79 output; Devstral 2 2512 charges $0.40 input / $2.00 output. Assuming a 50/50 split of input/output tokens, total monthly costs are: • 1M tokens (1,000 mtok): DeepSeek ≈ $500 vs Devstral ≈ $1,200 (difference $700). • 10M tokens (10,000 mtok): DeepSeek ≈ $5,000 vs Devstral ≈ $12,000 (difference $7,000). • 100M tokens (100,000 mtok): DeepSeek ≈ $50,000 vs Devstral ≈ $120,000 (difference $70,000). The cost gap scales linearly and matters most for high-volume production usage (10M+ tokens/month) or businesses with tight unit-economics; teams prioritizing tooling and faithfulness should budget the higher Devstral bill, while cost-sensitive deployments should prefer DeepSeek.

Choose DeepSeek V3.1 Terminus if: • You need the best strategic analysis in our suite (score 5 vs 4). • You're running high-volume or cost-sensitive deployments — output at $0.79/1k vs $2.00/1k saves materially at scale. • You need top long-context, structured output, or multilingual performance at lower cost. Choose Devstral 2 2512 if: • Your primary workflows rely on tool calling, function orchestration, or agentic coding (tool calling 4 vs 3, constrained rewriting 5 vs 3). • Faithful adherence to source material and strict character-limited rewrites matter. • You accept higher runtime costs for better tooling/faithfulness in integrated agent pipelines.