Devstral 2 2512 vs GPT-4o-mini

All claims below are from our 12-test suite. Wins/ties: Devstral (A) wins 8 tests, GPT‑4o‑mini (B) wins 2, ties on 2. Detailed walk-through: • Structured output — A: 5 vs B: 4. Devstral ties for 1st ("tied for 1st with 24 other models out of 54 tested"); this matters when you need strict JSON/schema compliance. • Long context — A: 5 vs B: 4. Devstral is tied for 1st ("tied for 1st with 36 other models out of 55 tested"); expect better retrieval and reference accuracy at 30k+ tokens. • Constrained rewriting — A: 5 vs B: 3. Devstral is tied for top ("tied for 1st with 4 other models out of 53 tested"); better for tight character/size limits. • Creative problem solving — A: 4 vs B: 2. Devstral ranks substantially higher (rank 9 of 54) so it generates more feasible, non‑obvious ideas. • Strategic analysis — A: 4 vs B: 2. Devstral's score and rank (rank 27 of 54) indicate stronger nuanced tradeoff reasoning. • Agentic planning — A: 4 vs B: 3. Devstral ranks 16 of 54 vs GPT‑4o‑mini 42 of 54 — better at goal decomposition and failure recovery. • Faithfulness — A: 4 vs B: 3. Devstral is more likely in our tests to stick to sources (A rank 34 of 55 vs B rank 52 of 55). • Multilingual — A: 5 vs B: 4; Devstral tied for 1st ("tied for 1st with 34 other models out of 55 tested"). • Tool calling — A: 4 vs B: 4 — tie (both rank display: A rank 18 of 54; B rank 18 of 54); both are comparable at function selection and argument accuracy. • Persona consistency — tie 4 vs 4. • Classification — A: 3 vs B: 4 — GPT‑4o‑mini wins and is tied for 1st for classification ("tied for 1st with 29 other models out of 53 tested"); choose it for routing/categorization tasks. • Safety calibration — A: 1 vs B: 4 — GPT‑4o‑mini clearly wins (B rank 6 of 55), meaning it better refuses harmful requests while permitting legitimate ones in our tests. External math benchmarks (Epoch AI): GPT‑4o‑mini scores 52.6% on MATH Level 5 and 6.9% on AIME 2025 (Epoch AI); Devstral has no external math results in the payload. Overall interpretation: Devstral trades higher cost for better long‑context handling, structured outputs, constrained rewriting, creative problem solving, and multilingual performance. GPT‑4o‑mini is the safer, cheaper choice and is stronger at classification.

Prices from the payload: Devstral 2 2512 input $0.40 / output $2.00 per 1k tokens; GPT‑4o‑mini input $0.15 / output $0.60 per 1k tokens. Using a conservative assumption that total monthly tokens are split 50/50 between input and output, costs are: • 1M tokens/month — Devstral: $1,200 vs GPT‑4o‑mini: $375. • 10M tokens/month — Devstral: $12,000 vs GPT‑4o‑mini: $3,750. • 100M tokens/month — Devstral: $120,000 vs GPT‑4o‑mini: $37,500. The payload also lists a priceRatio of 3.333. In short, Devstral is roughly 3–3.3x more expensive; this matters for high‑volume consumer apps, chatbots with many concurrent users, or startups with tight budgets. Teams paying for enterprise-grade coding/agent tooling who need long context or strict structured outputs may justify the higher cost; cost‑sensitive classification or safety‑first services should prefer GPT‑4o‑mini.

Choose Devstral 2 2512 if you need: large long‑context windows (256K), top‑tier structured output and constrained rewriting, stronger agentic planning and creative problem solving, or top multilingual fidelity — and you can absorb ~3x higher token costs. Choose GPT‑4o‑mini if you need: lower operating cost (input $0.15 / output $0.60 per 1k), better safety calibration (score 4 vs 1), best‑in‑class classification, or are building cost‑sensitive chat/classification services. If you need both safety and low cost with acceptable structured output, GPT‑4o‑mini is the pragmatic pick; if your product depends on reliably formatted long‑context outputs or advanced coding/agent workflows, choose Devstral.