GLM-5 vs Mistral Medium 3.5 vs Qwen3 Max

It is close. Our weighted score puts them within a point (GLM-5 55/100, Mistral Medium 3.5 55/100, Qwen3 Max 50/100), so choose by what matters most for your work: GLM-5 for raw capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GLM-5 is cheaper at $1.00 input / $3.20 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); Mistral Medium 3.5 costs $1.50 input / $7.50 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $1.55 per million tokens for GLM-5 versus $2.40 for Qwen3 Max (1.5× as much) and $3.00 for Mistral Medium 3.5 (1.9× as much).

GLM-5 scores higher on the Capabilities Index (ECI): GLM-5 145.8 (#74 of 148), Qwen3 Max 142.4 (#91 of 148) and Mistral Medium 3.5 141.4 (#95 of 148). The confidence ranges of the top two overlap (143.9–147.7 vs 140.0–144.6), so treat the gap as small.

There are no published SWE-bench Verified results for Mistral Medium 3.5 and Qwen3 Max yet, so there is no like-for-like coding score. On overall capability, GLM-5 leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.

Mistral Medium 3.5 and Qwen3 Max have the largest context windows (262,144 and 262,144 tokens), against 204,800 for GLM-5. Maximum output per response: GLM-5 up to 131,072, Mistral Medium 3.5 up to 262,144, Qwen3 Max up to 65,536 tokens.

GLM-5 accepts text; Mistral Medium 3.5 accepts text and images; Qwen3 Max accepts text. Mistral Medium 3.5 handles the widest range of inputs.

GLM-5 and Mistral Medium 3.5 publishes its weights and can be self-hosted; Qwen3 Max is proprietary.

Mistral Medium 3.5 is the newest, released Apr 29, 2026. GLM-5 came out Feb 12, 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.