Devstral Small 1.1 vs GPT-4.1 Nano

Summary of head-to-head results in our testing: Devstral Small 1.1 wins classification (score 4 vs GPT-4.1 Nano's 3) and in our rankings is tied for 1st in classification (tied with 29 others). GPT-4.1 Nano wins five benchmarks: structured output (5 vs 4), constrained rewriting (4 vs 3), faithfulness (5 vs 4), persona consistency (4 vs 2), and agentic planning (4 vs 2). GPT-4.1 Nano ranks particularly well on structured output (tied for 1st with 24 others) and faithfulness (tied for 1st with 32 others), which means it reliably follows JSON/schema constraints and sticks closer to source material — important for APIs that require exact schema outputs and low hallucination. Ties across both models include tool calling (4/4), long context (4/4), safety calibration (2/2), multilingual (4/4), strategic analysis (2/2), and creative problem solving (2/2) — in practice these ties mean similar behavior on function-selection, retrieval at 30k+ tokens, basic refusal calibration, and non-obvious idea generation. Rankings context: Devstral's persona consistency score (2) places it low (rank 51 of 53), while GPT-4.1 Nano's persona consistency (4) sits mid-pack (rank 38 of 53). For math/external benchmarks, GPT-4.1 Nano reports MATH Level 5 = 70% and AIME 2025 = 28.9% (Epoch AI); Devstral Small 1.1 has no external math scores in the payload. Overall, GPT-4.1 Nano's wins are concentrated on format fidelity, faithfulness, persona, and multi-step planning — attributes that matter for production pipelines that enforce strict output formats and minimize hallucinations. Devstral's standout is classification accuracy and lower cost.

Per the payload, Devstral Small 1.1 charges $0.10 per 1k input and $0.30 per 1k output; GPT-4.1 Nano charges $0.10 per 1k input and $0.40 per 1k output. For a concrete usage scenario of 1M input tokens + 1M output tokens/month (1M = 1,000 × 1k): Devstral = (1,000×$0.10)+(1,000×$0.30) = $100 + $300 = $400/month. GPT-4.1 Nano = $100 + $400 = $500/month. At 10M in+10M out: Devstral ≈ $4,000 vs GPT-4.1 Nano ≈ $5,000. At 100M in+100M out: Devstral ≈ $40,000 vs GPT-4.1 Nano ≈ $50,000. The output-cost gap ($0.10/1k tokens) scales linearly and matters most for high-throughput apps (chat logs, long generation pipelines, or large batch inference). For low-volume or feature-driven use (needing structured outputs, stronger faithfulness or multimodal inputs), the higher GPT-4.1 Nano cost can be justified; for cost-sensitive routing or classification tasks, Devstral's savings add up quickly.

Choose Devstral Small 1.1 if: you need the lowest per-token cost and strong classification/routing (Devstral scores 4 vs GPT-4.1 Nano's 3 in classification and is tied for 1st in our ranking), or you run very high-volume workloads where the $0.10/1k output savings materially reduce monthly bills. Choose GPT-4.1 Nano if: you require strict schema/JSON compliance, higher faithfulness (5 vs 4), better persona consistency, constrained-rewriting, or agentic planning (GPT-4.1 Nano wins these tests in our suite), or you need multimodal inputs (GPT-4.1 Nano supports text+image+file->text per the payload).