mistral-large-3 vs zai-glm-4.7

Capability for the money

Not the same tier: zai-glm-4.7 leads mistral-large-3 by 71.3 points on tool use (τ²-bench), for 3.2× the price.

1. Following instructions

   extraction, classification, sticking to a format

   IFBench
   mistral-large-336.2%

   zai-glm-4.767.9%

   zai-glm-4.7 +31.7

2. Coding

   writing and fixing code

   LiveCodeBench
   mistral-large-346.5%

   zai-glm-4.789.4%

   zai-glm-4.7 +42.9

3. Tool use

   multi-step agent work against real tools

   τ²-bench
   mistral-large-324.6%

   zai-glm-4.795.9%

   zai-glm-4.7 +71.3

4. Hard reasoning

   graduate-level science and multi-step analysis

   GPQA Diamond
   mistral-large-368.0%

   zai-glm-4.785.9%

   zai-glm-4.7 +17.9

Averaged over the benchmarks both models sat, mistral-large-3 returns 58.4 points of pass rate per dollar of blended price against 35.7 — the ratio that decides whether the dearer model earns its rate.

Each figure is a pass rate: the share of that test set the model answered correctly, measured by a third party rather than self-reported by the lab. Rows are different tests of different difficulty, so compare a model against the other model, not one row against the next. And a benchmark is not your workload — it narrows the shortlist, it does not pick for you.