o3-pro vs Claude Opus 4 vs QwQ 32B

It is close. Our weighted score puts them within 2 points (QwQ 32B 53/100, o3-pro 51/100, Claude Opus 4 48/100), so choose by what matters most for your work: o3-pro for raw capability and QwQ 32B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

QwQ 32B is cheaper at $0.66 input / $1.00 output per million tokens (median across 1 API provider). Claude Opus 4 costs $15.00 input / $75.00 output per million tokens (median across 5 API providers); o3-pro costs $20.00 input / $80.00 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 $30.00 for Claude Opus 4 (40× as much) and $35.00 for o3-pro (47× as much).

o3-pro scores higher on the Capabilities Index (ECI): o3-pro 147.4 (#60 of 148), Claude Opus 4 142.7 (#87 of 148) and QwQ 32B 137.6 (#109 of 148). Their confidence ranges do not overlap (145.8–149.7 vs 140.2–144.2), so the gap is a real one.

There are no published SWE-bench Verified results for o3-pro and QwQ 32B yet, so there is no like-for-like coding score. On overall capability, o3-pro leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.

o3-pro and Claude Opus 4 have the largest context windows (200,000 and 200,000 tokens), against 131,072 for QwQ 32B. Maximum output per response: o3-pro up to 100,000, Claude Opus 4 up to 32,000, QwQ 32B up to 8,192 tokens.

o3-pro accepts text and images; Claude Opus 4 accepts text, images and PDFs; QwQ 32B accepts text. Claude Opus 4 handles the widest range of inputs.

QwQ 32B publishes its weights and can be self-hosted; o3-pro and Claude Opus 4 is proprietary.

o3-pro is the newest, released Jun 10, 2025. Claude Opus 4 came out May 22, 2025; QwQ 32B came out Mar 5, 2025. Knowledge cutoff: o3-pro May 2024, Claude Opus 4 Mar 31, 2025, 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.