kimi-k2.5 vs llama-3.1-70b-instruct

Capability for the money

Not the same tier: kimi-k2.5 leads llama-3.1-70b-instruct by 80.7 points on tool use (τ²-bench), for 1.2× the price.

1. Following instructions

   extraction, classification, sticking to a format

   IFBench
   kimi-k2.570.2%

   llama-3.1-70b-instruct34.4%

   kimi-k2.5 +35.8

2. Tool use

   multi-step agent work against real tools

   τ²-bench
   kimi-k2.595.9%

   llama-3.1-70b-instruct15.2%

   kimi-k2.5 +80.7

3. Hard reasoning

   graduate-level science and multi-step analysis

   GPQA Diamond
   kimi-k2.587.9%

   llama-3.1-70b-instruct40.9%

   kimi-k2.5 +47.0

Averaged over the benchmarks both models sat, kimi-k2.5 returns 70.6 points of pass rate per dollar of blended price against 30.2 — 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.