GLM-5.3-Flash vs Qwen3.8 Max

GLM-5.3-Flash is the better all-round choice, scoring 80/100 against Qwen3.8 Max (69). It leads on price. Qwen3.8 Max wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GLM-5.3-Flash is cheaper at $0.15 input / $0.50 output per million tokens (official Z.AI API price). Qwen3.8 Max costs $2.00 input / $6.00 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.237 per million tokens for GLM-5.3-Flash versus $3.00 for Qwen3.8 Max (13× as much).

Qwen3.8 Max scores higher on the Capabilities Index (ECI): Qwen3.8 Max 156.6 (#20 of 148) and GLM-5.3-Flash 151.9 (#42 of 148). Their confidence ranges do not overlap (154.5–158.8 vs 149.4–154.3), so the gap is a real one. On individual benchmarks: GPQA Diamond — Qwen3.8 Max 92.7%, GLM-5.3-Flash 90.2%; FrontierMath Tiers 1–3 — Qwen3.8 Max 74.7%, GLM-5.3-Flash 55.8%; OTIS Mock AIME 2024–2025 — Qwen3.8 Max 99.4%, GLM-5.3-Flash 93.9%.

There are no published SWE-bench Verified results for GLM-5.3-Flash and Qwen3.8 Max yet, so there is no like-for-like coding score. On overall capability, Qwen3.8 Max leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GLM-5.3-Flash and Qwen3.8 Max share the same 1,000,000-token context window. Maximum output per response: GLM-5.3-Flash up to 131,072, Qwen3.8 Max up to 131,072 tokens.

GLM-5.3-Flash accepts text, images, PDFs and video; Qwen3.8 Max accepts text, images, PDFs and video. They handle the same number of input types.

GLM-5.3-Flash publishes its weights and can be self-hosted; Qwen3.8 Max is proprietary.

GLM-5.3-Flash is the newest, released Aug 26, 2026. Qwen3.8 Max came out Aug 3, 2026.

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.