grok-4.20-0309-reasoning vs llama-3.1-8b-instruct

Capability for the money

Not the same tier: grok-4.20-0309-reasoning leads llama-3.1-8b-instruct by 54.3 points on following instructions (IFBench), for 15.0× the price.

1. Following instructions

   extraction, classification, sticking to a format

   IFBench
   grok-4.20-0309-reasoning82.9%

   llama-3.1-8b-instruct28.6%

   grok-4.20-0309-reasoning +54.3

2. Tool use

   multi-step agent work against real tools

   τ²-bench
   grok-4.20-0309-reasoning96.5%

   llama-3.1-8b-instruct16.4%

   grok-4.20-0309-reasoning +80.1

3. Hard reasoning

   graduate-level science and multi-step analysis

   GPQA Diamond
   grok-4.20-0309-reasoning88.5%

   llama-3.1-8b-instruct25.9%

   grok-4.20-0309-reasoning +62.6

Averaged over the benchmarks both models sat, llama-3.1-8b-instruct returns 118.2 points of pass rate per dollar of blended price against 29.8 — 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.