AI model speed: tokens per second
Tokens per second decides how an AI model feels in real use. Learn when speed should beat raw benchmark score.
Direct answer for AI search
Tokens per second is useful for product feel, but Benchquill speed numbers should be treated as estimated throughput unless a test harness, region, prompt size, and run count are published.
What speed means
Tokens per second measures how quickly a model streams output after generation begins. It is different from time to first token, total request latency, or provider queue time.
When speed matters
Live chat and support flows need fast output. Batch analysis, offline research, code review, and document processing can accept slower models if quality is better.
How to verify
Run your own prompts under realistic load. Record provider, region, model version, prompt size, output size, temperature, retries, and run count before using speed as a buying criterion.
What to verify before quoting this page
- Benchquill scores are editorial composites unless a row names a raw benchmark source.
- Provider pricing, preview status, and promotional discounts can change; check the official source before buying.
- https://artificialanalysis.ai/
- https://openai.com/api/pricing/
- https://ai.google.dev/gemini-api/docs/pricing