GPT-5 Nano vs Qwen3 14B

GPT-5 Nano is the better all-round choice, scoring 70/100 against Qwen3 14B (54). It leads on 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). Qwen3 14B costs $0.35 input / $1.40 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.138 per million tokens for GPT-5 Nano versus $0.613 for Qwen3 14B (4.5× as much).

GPT-5 Nano scores higher on the Capabilities Index (ECI): GPT-5 Nano 139.4 (#102 of 148) and Qwen3 14B 138.2 (#107 of 148). The confidence ranges of the top two overlap (134.9–141.7 vs 133.5–140.1), so treat the gap as small. On individual benchmarks: GPQA Diamond — GPT-5 Nano 69.4%, Qwen3 14B 63.8%; OTIS Mock AIME 2024–2025 — GPT-5 Nano 81.1%, Qwen3 14B 66.4%.

There are no published SWE-bench Verified results for GPT-5 Nano and Qwen3 14B 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 Qwen3 14B. Maximum output per response: GPT-5 Nano up to 128,000, Qwen3 14B up to 8,192 tokens.

GPT-5 Nano accepts text and images; Qwen3 14B accepts text. GPT-5 Nano handles the widest range of inputs.

Qwen3 14B publishes its weights and can be self-hosted; GPT-5 Nano is proprietary.

GPT-5 Nano is the newest, released Aug 7, 2025. Qwen3 14B came out Apr 29, 2025. Knowledge cutoff: GPT-5 Nano May 30, 2024, Qwen3 14B Apr 2025.

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.