jamba-large-1.7 vs kimi-k2.6

Capability for the money

kimi-k2.6 is 2.0× cheaper than jamba-large-1.7 and scores higher on every benchmark they share — there is no case for paying more here.

1. Following instructions

   extraction, classification, sticking to a format

   IFBench
   jamba-large-1.735.2%

   kimi-k2.676.0%

   kimi-k2.6 +40.8

2. Tool use

   multi-step agent work against real tools

   τ²-bench
   jamba-large-1.713.5%

   kimi-k2.695.9%

   kimi-k2.6 +82.4

3. Hard reasoning

   graduate-level science and multi-step analysis

   GPQA Diamond
   jamba-large-1.739.0%

   kimi-k2.691.1%

   kimi-k2.6 +52.1

Averaged over the benchmarks both models sat, kimi-k2.6 returns 51.2 points of pass rate per dollar of blended price against 8.4 — 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. No published coding score for one of these models, so that lens is omitted. And a benchmark is not your workload — it narrows the shortlist, it does not pick for you.