GPT-4.1 vs Qwen3 32B

It is close. Our weighted score puts them within 2 points (GPT-4.1 53/100, Qwen3 32B 51/100), so choose by what matters most for your work: Qwen3 32B for raw capability and GPT-4.1 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Qwen3 32B is cheaper at $0.70 input / $2.80 output per million tokens (official Alibaba API price). GPT-4.1 costs $2.00 input / $8.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $1.23 per million tokens for Qwen3 32B versus $3.50 for GPT-4.1 (2.9× as much).

Qwen3 32B scores higher on the Capabilities Index (ECI): Qwen3 32B 138.5 (#106 of 148) and GPT-4.1 136.8 (#111 of 148). The confidence ranges of the top two overlap (135.1–140.4 vs 133.6–138.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — GPT-4.1 66.9%, Qwen3 32B 65.7%; OTIS Mock AIME 2024–2025 — Qwen3 32B 66.9%, GPT-4.1 38.3%.

There are no published SWE-bench Verified results for Qwen3 32B yet, so there is no like-for-like coding score. On overall capability, Qwen3 32B leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GPT-4.1 has the largest context window at 1,047,576 tokens, against 131,072 for Qwen3 32B. Maximum output per response: GPT-4.1 up to 32,768, Qwen3 32B up to 16,384 tokens.

GPT-4.1 accepts text, images and PDFs; Qwen3 32B accepts text. GPT-4.1 handles the widest range of inputs.

Qwen3 32B publishes its weights and can be self-hosted; GPT-4.1 is proprietary.

Qwen3 32B is the newest, released Apr 29, 2025. GPT-4.1 came out Apr 14, 2025. Knowledge cutoff: GPT-4.1 Apr 2024, Qwen3 32B Apr 2025.

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.