Devstral 2 2512 vs GPT-5

Summary from our 12-test suite: GPT-5 wins 7 benchmarks, Devstral 2 2512 wins 1, and 4 are ties. Detailed walk-through: - Strategic analysis: Devstral 2 = 4 vs GPT-5 = 5. GPT-5 wins; it ranks tied for 1st in strategic_analysis ("tied for 1st with 25 other models"), meaning better nuanced tradeoff reasoning for tasks that need real-number decisions. - Tool calling: Devstral 2 = 4 vs GPT-5 = 5. GPT-5 wins and is tied for 1st on tool_calling, so it is stronger at function selection, argument accuracy, and sequencing in our tests. - Faithfulness: Devstral 2 = 4 vs GPT-5 = 5. GPT-5 wins and is tied for 1st in faithfulness, indicating fewer deviations from source material in our runs. - Classification: Devstral 2 = 3 vs GPT-5 = 4. GPT-5 wins and is tied for 1st in classification among tested models, so routing and categorization tasks were more accurate with GPT-5. - Agentic planning: Devstral 2 = 4 vs GPT-5 = 5. GPT-5 wins and is tied for 1st on agentic_planning, suggesting stronger goal decomposition and failure recovery in our benchmarks. - Persona consistency: Devstral 2 = 4 vs GPT-5 = 5. GPT-5 wins and is tied for 1st on persona_consistency, so it better maintains character and resists injection in our tests. - Safety calibration: Devstral 2 = 1 vs GPT-5 = 2. GPT-5 wins (Devstral ranks 32 of 55; GPT-5 ranks 12 of 55), meaning GPT-5 more reliably refuses harmful prompts while permitting legitimate ones in our evaluation. - Constrained rewriting: Devstral 2 = 5 vs GPT-5 = 4. Devstral 2 wins and is tied for 1st in constrained_rewriting, so it's the better choice when strict compression or hard character limits are mandatory. - Structured output: both score 5 — tie. Both models tie for top ranks on structured_output, so JSON/schema adherence is equally strong in our tests. - Creative problem solving, long_context, multilingual: all ties (both score 4/5 or 5/5 depending on task); both models are capable for idea generation, very long contexts (tied for 1st in long_context), and non-English output. External benchmarks: GPT-5 scores 73.6% on SWE-bench Verified (Epoch AI), 98.1% on MATH Level 5 (Epoch AI), and 91.4% on AIME 2025 (Epoch AI). Devstral 2 2512 has no external SWE/MATH/AIME scores in the payload. These external results (Epoch AI) reinforce GPT-5’s strength on coding and high-level math in our comparison. Practical meaning: pick GPT-5 when tool-calling, faithfulness, classification, agentic planning, safety, or math-level accuracy matter; pick Devstral 2 2512 where constrained rewriting and lower inference cost dominate.

Pricing (per mTok): Devstral 2 2512 = $0.40 input, $2.00 output. GPT-5 = $1.25 input, $10.00 output. At realistic monthly volumes we assume a 50/50 split of input vs output tokens: - 1M total tokens (500k input + 500k output): Devstral ≈ $1,200; GPT-5 ≈ $5,625. - 10M total tokens: Devstral ≈ $12,000; GPT-5 ≈ $56,250. - 100M total tokens: Devstral ≈ $120,000; GPT-5 ≈ $562,500. The per-month gap scales linearly — GPT-5 costs about 4.69× more under the 50/50 assumption in these examples. High-volume products (APIs, customer-facing chatbots, bulk inference) will feel this difference immediately; teams prioritizing top-tier tool-calling, faithfulness, or advanced reasoning may accept GPT-5’s higher cost, while cost-sensitive use cases should evaluate Devstral 2 2512 first.

Choose Devstral 2 2512 if: - You need the lowest inference spend at scale (input $0.40 / mTok, output $2.00 / mTok) and constrained rewriting (score 5/5 in our tests) is important. - Your app runs very high token volumes and cost per token is the primary constraint. Choose GPT-5 if: - You prioritize tool calling, faithfulness, classification, strategic analysis, agentic planning, or better safety calibration (GPT-5 wins 7 of 12 benchmarks in our tests). - You need the best external coding/math signals: GPT-5 scores 73.6% on SWE-bench Verified, 98.1% on MATH Level 5, and 91.4% on AIME 2025 (Epoch AI).