GPT-4.1 mini vs Magistral Small 1.2

GPT-4.1 mini is the better all-round choice, scoring 60/100 against Magistral Small 1.2 (53). It leads on capability, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GPT-4.1 mini is cheaper at $0.40 input / $1.60 output per million tokens (official OpenAI API price). Magistral Small 1.2 costs $0.50 input / $1.50 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.70 per million tokens for GPT-4.1 mini versus $0.75 for Magistral Small 1.2 (1.1× as much).

GPT-4.1 mini scores higher on the Capabilities Index (ECI): GPT-4.1 mini 135.0 (#115 of 148) and Magistral Small 1.2 131.4 (#124 of 148). The confidence ranges of the top two overlap (131.2–136.6 vs 126.5–133.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — GPT-4.1 mini 65.9%, Magistral Small 1.2 47.6%; OTIS Mock AIME 2024–2025 — GPT-4.1 mini 44.7%, Magistral Small 1.2 28.1%.

There are no published SWE-bench Verified results for GPT-4.1 mini and Magistral Small 1.2 yet, so there is no like-for-like coding score. On overall capability, GPT-4.1 mini leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GPT-4.1 mini has the largest context window at 1,047,576 tokens, against 131,072 for Magistral Small 1.2. Maximum output per response: GPT-4.1 mini up to 32,768, Magistral Small 1.2 up to 131,072 tokens.

GPT-4.1 mini accepts text, images and PDFs; Magistral Small 1.2 accepts text and images. GPT-4.1 mini handles the widest range of inputs.

Magistral Small 1.2 publishes its weights (Apache 2.0) and can be self-hosted; GPT-4.1 mini is proprietary.

Magistral Small 1.2 is the newest, released Sep 18, 2025. GPT-4.1 mini came out Apr 14, 2025. Knowledge cutoff: GPT-4.1 mini Apr 2024.

Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.