deepseek-v3.2 vs jamba-mini-1.7

Capability for the money

Not the same tier: deepseek-v3.2 leads jamba-mini-1.7 by 53.2 points on coding (LiveCodeBench), for 1.2× the price.

1. Following instructions

   extraction, classification, sticking to a format

   IFBench
   deepseek-v3.249.0%

   jamba-mini-1.731.4%

   deepseek-v3.2 +17.6

2. Coding

   writing and fixing code

   LiveCodeBench
   deepseek-v3.259.3%

   jamba-mini-1.76.1%

   deepseek-v3.2 +53.2

3. Tool use

   multi-step agent work against real tools

   τ²-bench
   deepseek-v3.278.9%

   jamba-mini-1.712.6%

   deepseek-v3.2 +66.3

4. Hard reasoning

   graduate-level science and multi-step analysis

   GPQA Diamond
   deepseek-v3.275.1%

   jamba-mini-1.732.2%

   deepseek-v3.2 +42.9

Averaged over the benchmarks both models sat, deepseek-v3.2 returns 211.5 points of pass rate per dollar of blended price against 82.3 — 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.