GPT-4.1 nano vs Qwen3 30B A3B

GPT-4.1 nano is the better all-round choice, scoring 63/100 against Qwen3 30B A3B (58). It leads on price, inputs & features and context window. Qwen3 30B A3B wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GPT-4.1 nano is cheaper at $0.10 input / $0.40 output per million tokens (official OpenAI API price). Qwen3 30B A3B costs $0.114 input / $0.50 output per million tokens (median across 8 API providers). At a typical mix of three input tokens to one output token, that is $0.175 per million tokens for GPT-4.1 nano versus $0.21 for Qwen3 30B A3B (1.2× as much).

Qwen3 30B A3B scores higher on the Capabilities Index (ECI): Qwen3 30B A3B 136.2 (#112 of 148) and GPT-4.1 nano 129.6 (#127 of 148). The confidence ranges of the top two overlap (130.3–138.7 vs 123.2–132.0), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3 30B A3B 61.7%, GPT-4.1 nano 48.9%; OTIS Mock AIME 2024–2025 — Qwen3 30B A3B 62.8%, GPT-4.1 nano 28.9%.

There are no published SWE-bench Verified results for GPT-4.1 nano and Qwen3 30B A3B yet, so there is no like-for-like coding score. On overall capability, Qwen3 30B A3B leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GPT-4.1 nano has the largest context window at 1,047,576 tokens, against 131,072 for Qwen3 30B A3B. Maximum output per response: GPT-4.1 nano up to 32,768, Qwen3 30B A3B up to 16,384 tokens.

GPT-4.1 nano accepts text and images; Qwen3 30B A3B accepts text. GPT-4.1 nano handles the widest range of inputs.

Qwen3 30B A3B publishes its weights and can be self-hosted; GPT-4.1 nano is proprietary.

Qwen3 30B A3B is the newest, released Apr 28, 2025. GPT-4.1 nano came out Apr 14, 2025. Knowledge cutoff: GPT-4.1 nano 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.