LLM Compare
Head to head

Grok Voice 3.0 vs Fish Audio Drama 3

How Grok Voice 3.0 and Fish Audio Drama 3 stack up across benchmarks, pricing, speed, and the workloads that matter.

Grok Voice 3.0

xAI · Proprietary

Blended pricen/aper 1M tokens
Contextn/atokens
Speedn/atok/s
Benchmarks1results
VS

Fish Audio Drama 3

Fish Audio · Proprietary

Blended pricen/aper 1M tokens
Contextn/atokens
Speedn/atok/s
Benchmarks5results

On reasoning, Grok Voice 3.0 leads by about 6 points in our normalized benchmark average.

The table below breaks down every workload axis where both models have published results. Each score is a normalized 0 to 1 average across the benchmarks tagged with that category. A gap of a few points is noise; a gap of ten or more is a real difference in capability. Where one model has not reported a result, the cell shows n/a and the missing benchmark does not drag its average down.

Benchmark scores by workload

WorkloadGrok Voice 3.0Fish Audio Drama 3Edge
Reasoning 99% 93% +5.6 pts
Knowledge & factuality n/a 93% n/a

The headline benchmarks below are the most widely cited individual tests. GPQA Diamond measures graduate-level reasoning in physics, chemistry, and biology. SWE-Bench Verified tests whether a model can fix real GitHub issues. MMMU-Pro covers college-level multimodal understanding across six disciplines. AIME and FrontierMath push competitive and research math. BrowseComp measures web research ability.

Headline benchmarks

BenchmarkGrok Voice 3.0Fish Audio Drama 3

Benchmarks tell you what a model can do in a controlled setting. They do not tell you whether it will work on your specific task, with your specific data, at your specific scale. The recommendations below map each common workload to whichever of these two models scores higher on the relevant axis. Use them as a starting point, not a final answer.

Which should you choose?

Hard reasoning

Multi-step analysis, research, and problems that need sustained thought.

Grok Voice 3.0

Scores are normalized from public benchmarks published by llm-stats.com and averaged per workload. Pricing is the blended input/output cost per million tokens at an 8:1 mix. Refreshes daily. How this works.