gpt-5.6-terra vs jamba-mini-1.7

Capability for the money

Not the same tier: gpt-5.6-terra leads jamba-mini-1.7 by 60.3 points on hard reasoning (GPQA Diamond), for 18.0× the price.

1. Following instructions

   extraction, classification, sticking to a format

   IFBench
   gpt-5.6-terra71.2%

   jamba-mini-1.731.4%

   gpt-5.6-terra +39.8

2. Tool use

   multi-step agent work against real tools

   τ²-bench
   gpt-5.6-terra86.3%

   jamba-mini-1.712.6%

   gpt-5.6-terra +73.7

3. Hard reasoning

   graduate-level science and multi-step analysis

   GPQA Diamond
   gpt-5.6-terra92.5%

   jamba-mini-1.732.2%

   gpt-5.6-terra +60.3

Averaged over the benchmarks both models sat, jamba-mini-1.7 returns 101.6 points of pass rate per dollar of blended price against 18.5 — 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.