Devstral 2 2512 vs GPT-5.4

Summary of head-to-heads in our 12-test suite (scores on a 1–5 scale). GPT-5.4 wins five benchmarks: agentic planning (5 vs 4), faithfulness (5 vs 4), strategic analysis (5 vs 4), safety calibration (5 vs 1), and persona consistency (5 vs 4). Devstral 2 2512 wins one: constrained rewriting (5 vs 4). Six benchmarks tie: structured output (5/5), creative problem solving (4/4), tool calling (4/4), classification (3/3), long context (5/5), and multilingual (5/5). Context and task implications: - Safety_calibration: GPT-5.4 scored 5/5 and ranks tied for 1st of 55 (tied with 4 others); Devstral scored 1/5 and ranks 32 of 55. For public-facing chat or regulated domains, GPT-5.4’s safety calibration is materially better in our testing. - Faithfulness and strategic analysis: GPT-5.4’s 5/5 (tied for 1st on faithfulness and strategic analysis) means fewer source hallucinations and stronger nuanced tradeoff reasoning—important for summarization, research assistants, and financial analysis. - Agentic_planning: GPT-5.4 is 5/5 and tied for 1st of 54; Devstral is 4/5 and ranks 16 of 54. If you need goal decomposition and failure recovery (agent workflows), GPT-5.4 performed better. - Constrained_rewriting: Devstral’s 5/5 and tied for 1st of 53 indicates it excels at hard character-limit compression and microcopy tasks. - Structured_output and long context: both models score 5/5 and tie for 1st on structured output and long context; in practice both are reliable for JSON/schema compliance and retrieval at 30K+ tokens. Note external benchmarks: GPT-5.4 scores 76.9% on SWE-bench Verified (Epoch AI) and 95.3% on AIME 2025 (Epoch AI), ranking 2nd of 12 and 3rd of 23 respectively on those third-party math/coding tests. Devstral has no external benchmark entries in the payload. Also consider context window: Devstral supports a 262,144-token window; GPT-5.4 exposes a >1,000,000-token window—this can affect very large-document workflows.

Costs shown are per 1,000 tokens (mTok). Devstral 2 2512: input $0.40/mTok, output $2.00/mTok. GPT-5.4: input $2.50/mTok, output $15.00/mTok. If you only pay for output tokens, 1M output tokens (1,000 mTok) costs $2,000 on Devstral vs $15,000 on GPT-5.4. At 10M output tokens: $20,000 vs $150,000. At 100M output tokens: $200,000 vs $1,500,000. Assuming a 1:1 input:output token split, total per-mTok is $2.40 (Devstral) vs $17.50 (GPT); that yields $2,400 vs $17,500 for 1M tokens, $24,000 vs $175,000 for 10M, and $240,000 vs $1,750,000 for 100M. The cost gap matters most for startups, content-generation pipelines, and high-throughput developer tooling; teams needing top-tier safety and faithfulness should budget for GPT-5.4, while high-volume applications with tight budgets should consider Devstral 2 2512.

Choose Devstral 2 2512 if: you need a much lower-cost model ($0.40 input / $2.00 output per mTok), require top-tier constrained rewriting or high-volume code-generation/cost-sensitive tasks, or want a 256K context window at a fraction of the price. Choose GPT-5.4 if: safety, faithfulness, agentic planning and high-stakes decisioning matter (GPT-5.4 scored 5/5 on safety calibration, faithfulness and agentic planning in our testing and ranks tied for 1st in those areas), or you need the largest context window and third-party math/coding performance (76.9% SWE-bench Verified; 95.3% AIME 2025 per Epoch AI).