o1 vs DeepSeek-R1-Distill-Qwen-32B

o1 is the better all-round choice, scoring 65/100 against DeepSeek-R1-Distill-Qwen-32B (47). It leads on capability, inputs & features and context window. The score weighs capability 67%, inputs & features 20%, context window 13%.

o1 is cheaper at $15.00 input / $60.00 output per million tokens (official OpenAI API price). . At a typical mix of three input tokens to one output token, that is $26.25 per million tokens for o1 versus . DeepSeek-R1-Distill-Qwen-32B has no published per-token price.

o1 scores higher on the Capabilities Index (ECI): o1 141.9 (#92 of 148) and DeepSeek-R1-Distill-Qwen-32B 137.4 (#110 of 148). Their confidence ranges do not overlap (139.7–143.1 vs 130.7–138.7), so the gap is a real one. On individual benchmarks: GPQA Diamond — o1 76.8%, DeepSeek-R1-Distill-Qwen-32B 64.1%; OTIS Mock AIME 2024–2025 — o1 73.3%, DeepSeek-R1-Distill-Qwen-32B 55.6%.

There are no published SWE-bench Verified results for o1 and DeepSeek-R1-Distill-Qwen-32B yet, so there is no like-for-like coding score. On overall capability, o1 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.

o1 has the largest context window at 200,000 tokens, against 131,072 for DeepSeek-R1-Distill-Qwen-32B. Maximum output per response: o1 up to 100,000, DeepSeek-R1-Distill-Qwen-32B up to 32,768 tokens.

o1 accepts text, images and PDFs; DeepSeek-R1-Distill-Qwen-32B accepts text. o1 handles the widest range of inputs.

DeepSeek-R1-Distill-Qwen-32B publishes its weights and can be self-hosted; o1 is proprietary.

DeepSeek-R1-Distill-Qwen-32B is the newest, released Jan 20, 2025. o1 came out Dec 5, 2024. Knowledge cutoff: o1 Sep 2023.

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.