GLM-5.3 vs Qwen3.8 Max

Qwen3.8 Max is the better all-round choice, scoring 69/100 against GLM-5.3 (64). It leads on inputs & features. GLM-5.3 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GLM-5.3 is cheaper at $1.40 input / $4.40 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 $2.15 per million tokens for GLM-5.3 versus $3.00 for Qwen3.8 Max (1.4× as much).

Qwen3.8 Max scores higher on the Capabilities Index (ECI): Qwen3.8 Max 156.6 (#20 of 148) and GLM-5.3 155.8 (#24 of 148). The confidence ranges of the top two overlap (154.5–158.8 vs 153.7–158.3), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3.8 Max 92.7%, GLM-5.3 90.9%; FrontierMath Tiers 1–3 — Qwen3.8 Max 74.7%, GLM-5.3 68.8%; OTIS Mock AIME 2024–2025 — Qwen3.8 Max 99.4%, GLM-5.3 91.1%; SimpleQA Verified — Qwen3.8 Max 45.8%, GLM-5.3 41.0%.

There are no published SWE-bench Verified results for GLM-5.3 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 and Qwen3.8 Max share the same 1,000,000-token context window. Maximum output per response: GLM-5.3 up to 131,072, Qwen3.8 Max up to 131,072 tokens.

GLM-5.3 accepts text; Qwen3.8 Max accepts text, images, PDFs and video. Qwen3.8 Max handles the widest range of inputs.

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

GLM-5.3 is the newest, released Aug 14, 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.