GPT-5.4 nano vs Qwen3.6 27B

It is close. Our weighted score puts them within 3 points (GPT-5.4 nano 68/100, Qwen3.6 27B 65/100), so choose by what matters most for your work: Qwen3.6 27B for raw capability, GPT-5.4 nano on price and GPT-5.4 nano for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GPT-5.4 nano is cheaper at $0.20 input / $1.25 output per million tokens (official OpenAI API price). Qwen3.6 27B costs $0.60 input / $3.60 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.463 per million tokens for GPT-5.4 nano versus $1.35 for Qwen3.6 27B (2.9× as much).

Qwen3.6 27B scores higher on the Capabilities Index (ECI): Qwen3.6 27B 146.5 (#68 of 148) and GPT-5.4 nano 145.8 (#75 of 148). The confidence ranges of the top two overlap (144.2–147.9 vs 143.2–147.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3.6 27B 85.9%, GPT-5.4 nano 78.5%; FrontierMath Tiers 1–3 — GPT-5.4 nano 44.9%, Qwen3.6 27B 35.1%; OTIS Mock AIME 2024–2025 — Qwen3.6 27B 91.1%, GPT-5.4 nano 87.8%.

There are no published SWE-bench Verified results for GPT-5.4 nano and Qwen3.6 27B yet, so there is no like-for-like coding score. On overall capability, Qwen3.6 27B leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GPT-5.4 nano has the largest context window at 400,000 tokens, against 262,144 for Qwen3.6 27B. Maximum output per response: GPT-5.4 nano up to 128,000, Qwen3.6 27B up to 65,536 tokens.

GPT-5.4 nano accepts text and images; Qwen3.6 27B accepts text, images, audio and video. Qwen3.6 27B handles the widest range of inputs.

Qwen3.6 27B publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.

Qwen3.6 27B is the newest, released Apr 22, 2026. GPT-5.4 nano came out Mar 17, 2026. Knowledge cutoff: GPT-5.4 nano Aug 31, 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.