USE-CASE GUIDES

Best cheap LLM API for high-volume tasks

When you're calling the API millions of times a month, the per-token rate is the budget. These are our tracked budget-tier models, cheapest input rate first.

High-volume workloads — classification, tagging, moderation, bulk extraction — are usually bottlenecked by unit economics, not raw capability. At millions of calls a month, a fraction of a cent per call compounds fast, and since these tasks typically return a short answer (a label, a category, a boolean) for a much longer prompt, the input rate tends to matter more than the output rate. The models below are our budget-tier picks, sorted by the cheapest input price first.

ℹ️How this list is built: Filtered to the budget tier, sorted by input price ascending.
11 models
Model Provider Input /1M Output /1M Context
Mistral $0.15 $0.60 Not published
Meta (via Together AI) $0.18 $0.59 1M tokens
OpenAI $0.20 $1.20 ~1.05M tokens
Google $0.25 $1.50 Not published
Meta (via Together AI) $0.27 $0.85 ~1.05M tokens
DeepSeek $0.30 $1.20 1M tokens
Amazon $0.30 $2.50 1M tokens
Alibaba $0.40 $1.60 1M tokens
Mistral $0.50 $1.50 Not published
Anthropic $1.00 $5.00 200K tokens
xAI $1.00 $2.00 256K tokens

We don't benchmark task-specific quality — this shortlist is built from verified price and published context window only. Use it to narrow candidates by cost, then evaluate output quality yourself.

See all use cases →

Frequently asked questions

Most high-volume tasks send a long prompt but return a short answer — a label or a few fields. Since input tokens dominate the bill in that pattern, the input rate is usually what determines your actual cost per call.

Not necessarily. We track verified price and context window only, not task accuracy. If a cheaper model produces enough errors to need manual review, that hidden cost can outweigh the savings — test on a sample of your real data first.

Not always — budget-tier models sometimes ship with smaller context windows than a provider's flagship. Check the context column above if your task involves long input documents, not just short strings.