GLM-5.1 vs Qwen3 Max

GLM-5.1 is the better all-round choice, scoring 57/100 against Qwen3 Max (50). It leads on capability, price and inputs & features. Qwen3 Max wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GLM-5.1 is cheaper at $1.40 input / $4.40 output per million tokens (official Z.AI API price). Qwen3 Max costs $1.20 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-5.1 versus $2.40 for Qwen3 Max (1.1× as much).

GLM-5.1 scores higher on the Capabilities Index (ECI): GLM-5.1 149.9 (#51 of 148) and Qwen3 Max 142.4 (#91 of 148). Their confidence ranges do not overlap (148.0–151.6 vs 140.0–144.6), so the gap is a real one. On individual benchmarks: GPQA Diamond — GLM-5.1 89.9%, Qwen3 Max 72.6%; FrontierMath Tiers 1–3 — GLM-5.1 36.8%, Qwen3 Max 19.0%; OTIS Mock AIME 2024–2025 — GLM-5.1 93.3%, Qwen3 Max 73.3%; SimpleQA Verified — Qwen3 Max 48.8%, GLM-5.1 34.0%.

There are no published SWE-bench Verified results for Qwen3 Max yet, so there is no like-for-like coding score. On overall capability, GLM-5.1 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Qwen3 Max has the largest context window at 262,144 tokens, against 200,000 for GLM-5.1. Maximum output per response: GLM-5.1 up to 131,072, Qwen3 Max up to 65,536 tokens.

GLM-5.1 accepts text; Qwen3 Max accepts text. They handle the same number of input types.

GLM-5.1 publishes its weights and can be self-hosted; Qwen3 Max is proprietary.

GLM-5.1 is the newest, released Apr 7, 2026. Qwen3 Max came out Sep 23, 2025. Knowledge cutoff: Qwen3 Max Apr 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.