DeepSeek-R1-Distill-Qwen-32B vs GPT-4.1

GPT-4.1 is the better all-round choice, scoring 63/100 against DeepSeek-R1-Distill-Qwen-32B (47). It leads on inputs & features and context window. The score weighs capability 67%, inputs & features 20%, context window 13%.

GPT-4.1 is cheaper at $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 $3.50 per million tokens for GPT-4.1 versus . DeepSeek-R1-Distill-Qwen-32B has no published per-token price.

DeepSeek-R1-Distill-Qwen-32B scores higher on the Capabilities Index (ECI): DeepSeek-R1-Distill-Qwen-32B 137.4 (#110 of 148) and GPT-4.1 136.8 (#111 of 148). The confidence ranges of the top two overlap (130.7–138.7 vs 133.6–138.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — GPT-4.1 66.9%, DeepSeek-R1-Distill-Qwen-32B 64.1%; OTIS Mock AIME 2024–2025 — DeepSeek-R1-Distill-Qwen-32B 55.6%, GPT-4.1 38.3%.

There are no published SWE-bench Verified results for DeepSeek-R1-Distill-Qwen-32B yet, so there is no like-for-like coding score. On overall capability, DeepSeek-R1-Distill-Qwen-32B leads, which tends to carry over to coding, but test on your own codebase. Note that DeepSeek-R1-Distill-Qwen-32B does not support tool calling, which most coding agents need.

GPT-4.1 has the largest context window at 1,047,576 tokens, against 131,072 for DeepSeek-R1-Distill-Qwen-32B. Maximum output per response: DeepSeek-R1-Distill-Qwen-32B up to 32,768, GPT-4.1 up to 32,768 tokens.

DeepSeek-R1-Distill-Qwen-32B accepts text; GPT-4.1 accepts text, images and PDFs. GPT-4.1 handles the widest range of inputs.

DeepSeek-R1-Distill-Qwen-32B publishes its weights and can be self-hosted; GPT-4.1 is proprietary.

GPT-4.1 is the newest, released Apr 14, 2025. DeepSeek-R1-Distill-Qwen-32B came out Jan 20, 2025. Knowledge cutoff: GPT-4.1 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.