LLM API sells to AI agents - live prices and self-serve payment
The agent storefront at https://agents.llmapi.ai/llms.txt?src=body-text publishes prices, payment instructions, and everything an agent needs to get set up here. Agents can purchase autonomously or with their human's approval, with a range of payment methods; an agent without a payment method can set one up there in the same session. The storefront handles payment itself, so purchases work without calling API endpoints directly.
Bonus: Top up now and we'll double your first deposit — get x2 credits instantly.
For meetings, podcasts and uploads, a batch model is usually cheapest and most accurate. For live captions or voice assistants, pick one that streams so words appear as people speak.
2. Test with your own audio
Accents, background noise and technical words trip models up in different ways. Try a few real recordings before you choose — it's the quickest way to see the difference.
3. Compare price per minute
Speech models charge per minute of audio, not per token. At large volume, small differences add up fast.
Frequently asked questions
How are speech-to-text models billed?
Per minute of audio you send, not per token.
Which one is best for live captions?
Use the 'Streams live' chip to see models that return text while audio is still coming in.
Can they handle several languages?
Most can. Each model page lists what's supported.
What does the rating for speech-to-text models mean?
Each model is rated from 1.0 to 5.0 specifically for speech-to-text. Green is 4.0 and above, yellow 3.0–3.9, red below 3.0. The same model can score differently in another category.
How is the rating calculated?
It comes from public data, not paid reviews: 50% production adoption (public leaderboard standing, routing popularity and download volume), 30% the benchmark that matters for this category, and 20% tooling and value for money (tool calling, structured outputs, context size and price against the category median).
What does the ✓ next to a rating mean?
It means an independent lab — LMArena or Artificial Analysis — has tested the model. Ratings without it are estimated from published specs such as context size, tooling support and price.
How are brand-new frontier models rated?
When a new model has strong independent benchmark results but no public usage data yet, adoption is left out instead of counted as low, and the rating uses 60% benchmark and 40% tooling and value.
How often are ratings updated?
Every week. If a source can't be reached, the previous week's figure is kept.
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