deepseek-v3.2 vs jamba-large-1.7

Capability for the money

deepseek-v3.2 is 11.3× 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
   deepseek-v3.249.0%

   jamba-large-1.735.2%

   deepseek-v3.2 +13.8

2. Coding

   writing and fixing code

   LiveCodeBench
   deepseek-v3.259.3%

   jamba-large-1.718.1%

   deepseek-v3.2 +41.2

3. Tool use

   multi-step agent work against real tools

   τ²-bench
   deepseek-v3.278.9%

   jamba-large-1.713.5%

   deepseek-v3.2 +65.4

4. Hard reasoning

   graduate-level science and multi-step analysis

   GPQA Diamond
   deepseek-v3.275.1%

   jamba-large-1.739.0%

   deepseek-v3.2 +36.1

Averaged over the benchmarks both models sat, deepseek-v3.2 returns 211.5 points of pass rate per dollar of blended price against 7.6 — 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.