jamba-mini-1.7 vs zai-glm-4.6

Capability for the money

Not the same tier: zai-glm-4.6 leads jamba-mini-1.7 by 64.3 points on tool use (τ²-bench), for 9.5× the price.

1. Following instructions

   extraction, classification, sticking to a format

   IFBench
   jamba-mini-1.731.4%

   zai-glm-4.636.7%

   zai-glm-4.6 +5.3

2. Coding

   writing and fixing code

   LiveCodeBench
   jamba-mini-1.76.1%

   zai-glm-4.656.1%

   zai-glm-4.6 +50.0

3. Tool use

   multi-step agent work against real tools

   τ²-bench
   jamba-mini-1.712.6%

   zai-glm-4.676.9%

   zai-glm-4.6 +64.3

4. Hard reasoning

   graduate-level science and multi-step analysis

   GPQA Diamond
   jamba-mini-1.732.2%

   zai-glm-4.663.2%

   zai-glm-4.6 +31.0

Averaged over the benchmarks both models sat, jamba-mini-1.7 returns 82.3 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.