mistral-large-3 vs zai-glm-4.6

Capability for the money

Not the same tier: zai-glm-4.6 leads mistral-large-3 by 52.3 points on tool use (τ²-bench), for 3.2× the price. They are level on following instructions (IFBench) and hard reasoning (GPQA Diamond), so the premium only pays off on tool use work.

1. Following instructions

   extraction, classification, sticking to a format

   IFBench
   mistral-large-336.2%

   zai-glm-4.636.7%

   zai-glm-4.6 +0.5

2. Coding

   writing and fixing code

   LiveCodeBench
   mistral-large-346.5%

   zai-glm-4.656.1%

   zai-glm-4.6 +9.6

3. Tool use

   multi-step agent work against real tools

   τ²-bench
   mistral-large-324.6%

   zai-glm-4.676.9%

   zai-glm-4.6 +52.3

4. Hard reasoning

   graduate-level science and multi-step analysis

   GPQA Diamond
   mistral-large-368.0%

   zai-glm-4.663.2%

   mistral-large-3 +4.8

Averaged over the benchmarks both models sat, mistral-large-3 returns 58.4 points of pass rate per dollar of blended price against 24.5 — 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.