Devstral 2 2512 vs Gemini 3.1 Pro Preview

Overview: our 12-test suite (scores 1–5) shows Gemini 3.1 Pro Preview winning six tests, Devstral 2 2512 winning two, and four ties. Detailed walk-through (scores listed as Devstral vs Gemini, with ranking context):

• strategic_analysis: 4 vs 5 — Gemini wins. In our testing Gemini ranks "tied for 1st" for strategic_analysis (rank 1 of 54), meaning it better handles nuanced tradeoff reasoning with real numbers for planning and cost/benefit choices.
• agentic_planning: 4 vs 5 — Gemini wins. Gemini is "tied for 1st" (rank 1 of 54) on agentic_planning, so it decomposes goals and recovers from failures more reliably in agentic flows.
• constrained_rewriting: 5 vs 4 — Devstral wins. Devstral is tied for 1st on constrained_rewriting ("tied for 1st with 4 other models"), which predicts better performance when you must compress or fit text into hard limits (e.g., SMS, UI snippets).
• creative_problem_solving: 4 vs 5 — Gemini wins. Gemini is "tied for 1st" in creative_problem_solving (top-tier), so it produces more non-obvious, feasible ideas in brainstorming and design tasks.
• tool_calling: 4 vs 4 — Tie. Both rank similarly (each displays "rank 18 of 54"), so function selection and argument sequencing are comparable in our tests.
• faithfulness: 4 vs 5 — Gemini wins. Gemini is "tied for 1st" for faithfulness (rank 1 of 55), indicating fewer hallucinations and tighter adherence to source material in our testing.
• classification: 3 vs 2 — Devstral wins. Devstral ranks "rank 31 of 53 (20 models share this score)" vs Gemini at "rank 51 of 53", so Devstral is better at straightforward tagging/routing tasks in our tests.
• structured_output: 5 vs 5 — Tie. Both tied for 1st ("tied for 1st with 24 other models") — both reliably produce JSON/schema-compliant outputs in our testing.
• safety_calibration: 1 vs 2 — Gemini wins. Gemini ranks "rank 12 of 55" for safety_calibration vs Devstral at "rank 32 of 55", meaning Gemini more reliably refuses harmful requests while permitting legitimate ones in our tests.
• long_context: 5 vs 5 — Tie. Both tied for 1st (large context support) — Devstral has a 262,144-token window; Gemini offers 1,048,576 tokens. In practice both handled retrieval accuracy at 30K+ tokens in our suite.
• persona_consistency: 4 vs 5 — Gemini wins. Gemini is "tied for 1st" for persona_consistency (maintaining character), which matters for multi-turn assistants and agent personas.
• multilingual: 5 vs 5 — Tie. Both tied for 1st; both performed equivalently across non-English outputs in our tests.

External benchmark note: Gemini scores 95.6 on AIME 2025 (Epoch AI), ranked 2 of 23 on that external math test; we include this as a supplementary signal of Gemini’s strong math/reasoning capability. Overall interpretation: Gemini leads on higher-level reasoning, agentic planning, faithfulness and safety; Devstral excels at constrained rewriting and classification and is competitive on structured outputs and long-context retrieval.

Pricing (per 1,000 tokens): Devstral 2 2512 — input $0.40, output $2.00. Gemini 3.1 Pro Preview — input $2.00, output $12.00. Assuming a 50/50 split of input/output tokens, 1M tokens/month costs: Devstral ≈ $1,200; Gemini ≈ $7,000. At 10M tokens/month: Devstral ≈ $12,000; Gemini ≈ $70,000. At 100M tokens/month: Devstral ≈ $120,000; Gemini ≈ $700,000. Who should care: startups, high-throughput APIs, and cost-conscious teams will see materially different budgets — Gemini's accuracy and multimodal capabilities may justify the +$5,800/month premium at 1M tokens for teams that need top-tier reasoning, but anyone operating at tens of millions of tokens should model the 6x+ cost gap carefully before selecting Gemini.

Choose Devstral 2 2512 if: you need a much cheaper text->text model (input $0.40/mk, output $2.00/mk), you operate at high token volumes, you prioritize constrained_rewriting (5/5) or classification, and you require a 256K context window for long-context retrieval while keeping costs low. Choose Gemini 3.1 Pro Preview if: you need top-tier strategic_analysis, agentic_planning, faithfulness, creative_problem_solving and safety_calibration (Gemini wins 6 of 12 tests in our suite), require multimodal inputs (text+image+file+audio+video), or need the larger 1,048,576-token window and best-in-class reasoning (also evidenced by 95.6 on AIME 2025, Epoch AI). If budget is tight, Devstral delivers most structured-output and long-context capabilities at roughly one-sixth the per-token expense.