Qwen3.5 397B-A17B vs GPT-5.4 nano

It is close. Our weighted score puts them within 3 points (GPT-5.4 nano 68/100, Qwen3.5 397B-A17B 65/100), so choose by what matters most for your work: Qwen3.5 397B-A17B 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). Qwen3.5 397B-A17B 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 Qwen3.5 397B-A17B (2.9× as much).

Qwen3.5 397B-A17B scores higher on the Capabilities Index (ECI): Qwen3.5 397B-A17B 146.7 (#67 of 148) and GPT-5.4 nano 145.8 (#75 of 148). The confidence ranges of the top two overlap (144.8–148.2 vs 143.2–147.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3.5 397B-A17B 86.4%, GPT-5.4 nano 78.5%; FrontierMath Tiers 1–3 — GPT-5.4 nano 44.9%, Qwen3.5 397B-A17B 31.2%; OTIS Mock AIME 2024–2025 — Qwen3.5 397B-A17B 88.9%, GPT-5.4 nano 87.8%.

There are no published SWE-bench Verified results for Qwen3.5 397B-A17B and GPT-5.4 nano yet, so there is no like-for-like coding score. On overall capability, Qwen3.5 397B-A17B 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 Qwen3.5 397B-A17B. Maximum output per response: Qwen3.5 397B-A17B up to 65,536, GPT-5.4 nano up to 128,000 tokens.

Qwen3.5 397B-A17B accepts text, images, audio and video; GPT-5.4 nano accepts text and images. Qwen3.5 397B-A17B handles the widest range of inputs.

Qwen3.5 397B-A17B publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.

GPT-5.4 nano is the newest, released Mar 17, 2026. Qwen3.5 397B-A17B came out Feb 15, 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.