QwQ 32B vs GPT-5 Nano

GPT-5 Nano is the better all-round choice, scoring 70/100 against QwQ 32B (53). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GPT-5 Nano is cheaper at $0.05 input / $0.40 output per million tokens (official OpenAI API price). QwQ 32B costs $0.66 input / $1.00 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.138 per million tokens for GPT-5 Nano versus $0.745 for QwQ 32B (5.4× as much).

GPT-5 Nano scores higher on the Capabilities Index (ECI): GPT-5 Nano 139.4 (#102 of 148) and QwQ 32B 137.6 (#109 of 148). The confidence ranges of the top two overlap (134.9–141.7 vs 133.1–141.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — GPT-5 Nano 69.4%, QwQ 32B 65.3%; OTIS Mock AIME 2024–2025 — GPT-5 Nano 81.1%, QwQ 32B 59.2%.

There are no published SWE-bench Verified results for QwQ 32B and GPT-5 Nano yet, so there is no like-for-like coding score. On overall capability, GPT-5 Nano leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GPT-5 Nano has the largest context window at 400,000 tokens, against 131,072 for QwQ 32B. Maximum output per response: QwQ 32B up to 8,192, GPT-5 Nano up to 128,000 tokens.

QwQ 32B accepts text; GPT-5 Nano accepts text and images. GPT-5 Nano handles the widest range of inputs.

QwQ 32B publishes its weights and can be self-hosted; GPT-5 Nano is proprietary.

GPT-5 Nano is the newest, released Aug 7, 2025. QwQ 32B came out Mar 5, 2025. Knowledge cutoff: QwQ 32B Apr 2024, GPT-5 Nano May 30, 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.