Devstral Medium vs GPT-5.2

Our 12-test benchmark suite (scored 1–5) gives GPT-5.2 the clear edge across the board. Devstral Medium wins zero tests outright and ties two.

Where GPT-5.2 leads:

• Strategic analysis: GPT-5.2 scores 5 vs Devstral Medium's 2. GPT-5.2 ties for 1st among 54 models; Devstral Medium ranks 44th. This is a large gap for tasks requiring nuanced tradeoff reasoning — think financial analysis, product strategy, or research synthesis.
• Creative problem solving: GPT-5.2 scores 5 vs 2. GPT-5.2 ties for 1st among 54 models; Devstral Medium ranks 47th of 54. In our testing, Devstral Medium struggled to produce non-obvious, specific, feasible ideas.
• Safety calibration: GPT-5.2 scores 5 vs Devstral Medium's 1. GPT-5.2 ties for 1st among 55 models (5 models total); Devstral Medium ranks 32nd. A score of 1 here is below the 25th percentile (p25 = 1), meaning Devstral Medium is at the bottom of the field on refusing harmful requests while permitting legitimate ones. For production deployments handling untrusted input, this is a significant risk flag.
• Persona consistency: 5 vs 3. GPT-5.2 ties for 1st; Devstral Medium ranks 45th of 53. Relevant for chatbot and assistant deployments that require stable character across long conversations.
• Agentic planning: 5 vs 4. GPT-5.2 ties for 1st among 54 models; Devstral Medium ranks 16th (tied among 26 models). Both are above the 50th percentile (p50 = 4), but GPT-5.2's ceiling matters for multi-step autonomous workflows.
• Long context: 5 vs 4. GPT-5.2 ties for 1st; Devstral Medium ranks 38th of 55. GPT-5.2 also carries a 400K context window versus Devstral Medium's 131K — a structural advantage for very long documents.
• Faithfulness: 5 vs 4. GPT-5.2 ties for 1st; Devstral Medium ranks 34th of 55. Relevant for RAG pipelines where hallucination is a liability.
• Multilingual: 5 vs 4. GPT-5.2 ties for 1st; Devstral Medium ranks 36th of 55.
• Tool calling: 4 vs 3. GPT-5.2 ranks 18th of 54; Devstral Medium ranks 47th. In our tests, Devstral Medium scored below the field median (p50 = 4) on function selection and argument accuracy.
• Constrained rewriting: 4 vs 3. GPT-5.2 ranks 6th of 53; Devstral Medium ranks 31st.

Where models tie:

• Structured output: Both score 4. Both share rank 26 of 54 with 27 models at this score. Adequate for JSON schema compliance in most workflows.
• Classification: Both score 4. Both tie for 1st among 53 tested models (30 models share this score). For routing and categorization tasks, neither has a head-to-head advantage.

External benchmarks (Epoch AI data): GPT-5.2 scores 73.8% on SWE-bench Verified, ranking 5th of 12 models with this score — placing it above the field median of 70.8% for real GitHub issue resolution. GPT-5.2 also scores 96.1% on AIME 2025, ranking 1st of 23 models in our data — well above the median of 83.9%. Devstral Medium has no external benchmark scores in our data. These external scores reinforce GPT-5.2's strength on hard reasoning and coding tasks, though Devstral Medium's description positions it specifically as a code generation and agentic reasoning model from Mistral AI and All Hands AI.

Devstral Medium costs $0.40/MTok input and $2.00/MTok output. GPT-5.2 costs $1.75/MTok input and $14.00/MTok output. The output cost gap is where this matchup is decided economically.

At 1M output tokens/month: Devstral Medium costs $2.00; GPT-5.2 costs $14.00 — a $12 difference. At 10M output tokens/month: $20 vs $140 — you're paying $120 more for GPT-5.2. At 100M output tokens/month: $200 vs $1,400 — a $1,200/month premium for GPT-5.2.

For developers running high-volume pipelines — document processing, classification at scale, code generation loops — Devstral Medium's cost profile is a meaningful advantage, especially given that both models tie on classification and structured output. But for use cases where GPT-5.2's advantages in strategic analysis, agentic planning, or safety calibration are load-bearing, the premium may be justified. Consumer users on a per-query basis will feel this less acutely; it's enterprise and API-heavy workloads where the 7x output multiplier becomes a real budget line.

Choose Devstral Medium if: You're running a high-volume API pipeline — code generation, structured output, classification — where both models are competitive and the 7x output cost savings ($2 vs $14/MTok) compound quickly. At 100M output tokens/month you save $1,200. If your workload is classification or JSON-schema tasks where both models score 4/5 and tie, paying GPT-5.2's premium is hard to justify. Also relevant for teams building on Mistral's ecosystem.

Choose GPT-5.2 if: Quality floors matter more than cost. Its 5/1 advantage on safety calibration alone disqualifies Devstral Medium for any deployment handling untrusted user input. For strategic analysis, creative ideation, agentic planning, and long-context tasks (up to 400K tokens), GPT-5.2's benchmark advantages translate to meaningfully better outputs. It also supports image and file input (text+image+file->text modality), which Devstral Medium does not. If you're building a general-purpose assistant, a research tool, or an autonomous agent that needs to reason, plan, and stay on-task across complex workflows, GPT-5.2 justifies the premium.