Devstral 2 2512 vs Gemini 2.5 Pro

Overview: across our 12-test suite Gemini 2.5 Pro wins 5 tests, Devstral 2 2512 wins 1, and 6 tests tie. Below we walk each test and what the scores mean in practice (all scores from our testing).

• Constrained rewriting: Devstral 2 2512 wins (5 vs 3). Devstral ties for 1st of 53 models in this test, showing it handles tight-character compression and strict-length rewrites best in our evaluation—useful for tweet-length summaries, release-note compression, or fixed-field outputs.

• Creative problem solving: Gemini wins (5 vs 4). In our tests Gemini ranks tied for 1st on creative_problem_solving, meaning it produced more non-obvious, feasible ideas under our prompts—valuable when you need novel approaches or brainstorming.

• Tool calling: Gemini wins (5 vs 4). Gemini’s tool_calling score is 5 and ranks tied for 1st of 54 (sole top tier), while Devstral ranks lower (18 of 54). In practical terms, Gemini is more reliable at selecting functions, producing accurate arguments, and sequencing multi-step calls in our tool-chaining scenarios.

• Faithfulness: Gemini wins (5 vs 4). Gemini scores 5 and is tied for 1st of 55 on faithfulness in our tests; Devstral scored 4 and ranks 34 of 55. This indicates Gemini better sticks to source material and avoids hallucination in our prompts—critical for factual assistants and document-grounded responses.

• Classification: Gemini wins (4 vs 3). Gemini ties for 1st among 53 models on classification, while Devstral ranks 31 of 53. Expect Gemini to route or categorize inputs more accurately in our evaluation.

• Persona consistency: Gemini wins (5 vs 4). Gemini is tied for 1st on persona_consistency; Devstral ranks lower. In chat or character-driven interfaces our tests show Gemini better maintains persona constraints and resists injection.

• Ties (no clear winner in our tests): structured_output (both 5; tied for 1st), strategic_analysis (both 4), long_context (both 5; tied for 1st), safety_calibration (both 1), agentic_planning (both 4), multilingual (both 5; tied for 1st). For these tasks the models performed equivalently in our suite—structured JSON output, long-context retrieval up to tens of thousands of tokens, and multilingual outputs are comparable in our tests.

• External benchmarks: beyond our internal tests, Gemini 2.5 Pro scores 57.6% on SWE-bench Verified and 84.2% on AIME 2025, according to Epoch AI. Devstral 2 2512 has no external scores in the payload. These external measures support Gemini’s strength on coding/problem-solving and math in third-party evaluations.

Takeaway: in our testing Gemini 2.5 Pro is the stronger, more dependable model for faithfulness, tool calling, classification, persona consistency, and creative problem-solving; Devstral’s standout win is constrained_rewriting and it delivers those results at a much lower cost.

Devstral 2 2512: input $0.40 / mTok, output $2.00 / mTok. Gemini 2.5 Pro: input $1.25 / mTok, output $10.00 / mTok. Assuming a 50/50 split of input/output tokens (per mTok averages of $1.20 for Devstral vs $5.625 for Gemini), monthly costs are: 1M tokens = Devstral $1,200 vs Gemini $5,625; 10M = $12,000 vs $56,250; 100M = $120,000 vs $562,500. If your workload is output-heavy (80% output), the gap widens: 1M tokens costs ~$1,680 (Devstral) vs ~$8,250 (Gemini). High-volume services, consumer-facing chatbots, or anything generating many output tokens should care deeply about this gap; prototyping, low-volume apps, or applications that require Gemini’s higher faithfulness/tool-calling may find the extra cost justified.

Choose Devstral 2 2512 if: you need a cost-effective model for heavy-volume deployments, tight constrained rewriting (5/5 in our tests; tied for 1st), long-context handling, and good general agentic coding support at a fraction of the price (input $0.40 / mTok, output $2.00 / mTok). Choose Gemini 2.5 Pro if: accuracy, faithful grounding, reliable tool calling, classification, and persona consistency matter most (Gemini wins those tests in our suite and ranks top in faithfulness and tool calling), and you can absorb a substantially higher runtime cost (input $1.25 / mTok, output $10.00 / mTok) for better end-to-end reliability and multimodal inputs.