gpt-5.6-sol vs jamba-large-1.7

Capability for the money

Not the same tier: gpt-5.6-sol leads jamba-large-1.7 by 55.1 points on hard reasoning (GPQA Diamond), for 3.2× the price.

1. Following instructions

   extraction, classification, sticking to a format

   IFBench
   gpt-5.6-sol72.7%

   jamba-large-1.735.2%

   gpt-5.6-sol +37.5

2. Tool use

   multi-step agent work against real tools

   τ²-bench
   gpt-5.6-sol85.1%

   jamba-large-1.713.5%

   gpt-5.6-sol +71.6

3. Hard reasoning

   graduate-level science and multi-step analysis

   GPQA Diamond
   gpt-5.6-sol94.1%

   jamba-large-1.739.0%

   gpt-5.6-sol +55.1

Averaged over the benchmarks both models sat, jamba-large-1.7 returns 8.4 points of pass rate per dollar of blended price against 7.5 — 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. 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.