deepseek-v3.2 vs gpt-5.6-sol

Capability for the money

Not the same tier: gpt-5.6-sol leads deepseek-v3.2 by 23.7 points on following instructions (IFBench), for 36.3× the price. They are level on tool use (τ²-bench), so the premium only pays off on following instructions work.

1. Following instructions

   extraction, classification, sticking to a format

   IFBench
   deepseek-v3.249.0%

   gpt-5.6-sol72.7%

   gpt-5.6-sol +23.7

2. Tool use

   multi-step agent work against real tools

   τ²-bench
   deepseek-v3.278.9%

   gpt-5.6-sol85.1%

   gpt-5.6-sol +6.2

3. Hard reasoning

   graduate-level science and multi-step analysis

   GPQA Diamond
   deepseek-v3.275.1%

   gpt-5.6-sol94.1%

   gpt-5.6-sol +19.0

Averaged over the benchmarks both models sat, deepseek-v3.2 returns 218.3 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.