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

o1 is the better all-round choice, scoring 65/100 against QwQ 32B (52) and 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%.

QwQ 32B is cheaper at $0.66 input / $1.00 output per million tokens (median across 1 API provider). o1 costs $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 $0.745 per million tokens for QwQ 32B versus $26.25 for o1 (35× as much). 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), QwQ 32B 137.6 (#109 of 148) and DeepSeek-R1-Distill-Qwen-32B 137.4 (#110 of 148). The confidence ranges of the top two overlap (139.7–143.1 vs 133.1–141.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — o1 76.8%, QwQ 32B 65.3%, DeepSeek-R1-Distill-Qwen-32B 64.1%; OTIS Mock AIME 2024–2025 — o1 73.3%, QwQ 32B 59.2%, DeepSeek-R1-Distill-Qwen-32B 55.6%.

There are no published SWE-bench Verified results for DeepSeek-R1-Distill-Qwen-32B, o1 and QwQ 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 and 131,072 for QwQ 32B. Maximum output per response: DeepSeek-R1-Distill-Qwen-32B up to 32,768, o1 up to 100,000, QwQ 32B up to 8,192 tokens.

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

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

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