Devstral 2 2512 vs Gemma 4 31B

In our testing across the 12-test suite, Gemma 4 31B wins the majority (7 tests), Devstral 2 2512 wins 2, and 3 are ties. Detailed walk-through (score: Devstral / Gemma; ranking context):

• Classification: 3 vs 4 — Gemma wins. Gemma is tied for 1st on classification ("tied for 1st with 29 other models out of 53 tested"), so expect more reliable routing and label accuracy in our benchmarks. Devstral ranks lower (rank 31 of 53).

• Agentic planning: 4 vs 5 — Gemma wins. Gemma is tied for 1st ("tied for 1st with 14 other models out of 54 tested"), meaning better goal decomposition and recovery in our agentic planning tests; Devstral is mid-tier (rank 16 of 54).

• Constrained rewriting: 5 vs 4 — Devstral wins. Devstral is tied for 1st ("tied for 1st with 4 other models out of 53 tested"), so it is stronger for strict compression and hard character-limit rewriting tasks.

• Tool calling: 4 vs 5 — Gemma wins. Gemma ranks tied for 1st on tool calling ("tied for 1st with 16 other models out of 54 tested"), so function selection, argument accuracy and sequencing were noticeably better in our tests.

• Faithfulness: 4 vs 5 — Gemma wins. Gemma is tied for 1st on faithfulness ("tied for 1st with 32 other models out of 55 tested"), indicating fewer hallucinations on source-dependent tasks compared to Devstral (rank 34 of 55).

• Structured output: 5 vs 5 — Tie. Both models are tied for 1st ("tied for 1st with 24 other models out of 54 tested"), so expect similar JSON/schema compliance in our tests.

• Safety calibration: 1 vs 2 — Gemma wins. Gemma ranks 12 of 55 on safety calibration (better refusal/permissiveness tradeoffs in our tests) while Devstral ranks 32 of 55.

• Long context: 5 vs 4 — Devstral wins. Devstral is tied for 1st on long_context ("tied for 1st with 36 other models out of 55 tested"), so it performs better on retrieval and accuracy at 30K+ token ranges in our benchmarks; Gemma ranks 38 of 55 here.

• Creative problem solving: 4 vs 4 — Tie. Both scored 4 and rank similarly (both at rank 9 of 54 tied with others), so idea generation quality was comparable in our tests.

• Strategic analysis: 4 vs 5 — Gemma wins. Gemma is tied for 1st ("tied for 1st with 25 other models out of 54 tested"), meaning clearer tradeoff reasoning and numeric nuance in our strategic tasks.

• Persona consistency: 4 vs 5 — Gemma wins. Gemma is tied for 1st ("tied for 1st with 36 other models out of 53 tested"), so it retained character and resisted injection better in our runs.

• Multilingual: 5 vs 5 — Tie. Both tied for 1st ("tied for 1st with 34 other models out of 55 tested"), so non-English parity was equal in our tests.

What this means for real tasks: Gemma is the stronger generalist for agentic flows, tool integration, classification, faithfulness and safety-sensitive apps. Devstral’s standout wins are for constrained rewriting (best-in-class in our suite) and long-context retrieval/accuracy, valuable for hard-limit compression and extremely long-document workflows.

Output cost per mTok: Devstral 2 2512 = $2.00, Gemma 4 31B = $0.38 (priceRatio 5.263). Output-only monthly cost examples: for 1M tokens — Gemma $380 vs Devstral $2,000; 10M tokens — Gemma $3,800 vs Devstral $20,000; 100M tokens — Gemma $38,000 vs Devstral $200,000. Input costs scale the same: Gemma input $0.13/mTok (1M → $130), Devstral input $0.40/mTok (1M → $400). High-volume SaaS, chat, and consumer-facing apps should favor Gemma to reduce operating expense; teams whose product requires the absolute best constrained-rewriting or 30K+ retrieval fidelity may justify Devstral’s higher bill.

Choose Devstral 2 2512 if you need best-in-class constrained rewriting or top long-context retrieval accuracy (scores: constrained_rewriting 5, long_context 5) and you can absorb ~5.26× higher per-token cost. Choose Gemma 4 31B if you want the best cost-to-performance balance for general API use: it wins 7 of 12 benchmarks in our testing (tool_calling 5, faithfulness 5, strategic_analysis 5, agentic_planning 5) and costs $0.38/output vs Devstral’s $2.00/output.