Best LLM for text classification
Label short texts into a fixed taxonomy — sentiment, intent, topic, spam.
Why this ranking is opinionated
Classification is short-in, short-out — bulk throughput and price-per-call dominate. JSON mode keeps the label space clean. A small fast model usually beats a frontier model here.
Top 5 recommendations
ranked by monthly cost at this workload- · Cheapest qualifying option at this workload (~$0.00/mo).
- · 262,144 tokens of context — far above this use case's 4,000-token minimum.
- · Missing preferred: json_mode — may need a workaround.
- · ~$0.00/mo (+0% over the cheapest option).
- · 131,072 tokens of context — far above this use case's 4,000-token minimum.
- · Missing preferred: json_mode — may need a workaround.
- · ~$0.00/mo (+0% over the cheapest option).
- · 65,536 tokens of context — far above this use case's 4,000-token minimum.
- · Missing preferred: json_mode — may need a workaround.
- · ~$0.00/mo (+0% over the cheapest option).
- · 32,768 tokens of context — far above this use case's 4,000-token minimum.
- · Supports preferred capabilities: json_mode.
- · ~$0.00/mo (+0% over the cheapest option).
- · 1,048,576 tokens of context — far above this use case's 4,000-token minimum.
- · Missing preferred: json_mode — may need a workaround.
Frequently asked questions
What makes a good LLM for text classification?
Classification is short-in, short-out — bulk throughput and price-per-call dominate. JSON mode keeps the label space clean. A small fast model usually beats a frontier model here.
What capabilities matter most for text classification?
For text classification the typical filters are: no specific capability requirement, and a context window of at least 4k tokens. The ranking on this page weights monthly cost (at the workload defaults shown above) most heavily, then capability fit.
What is currently the cheapest LLM for text classification?
At the typical workload defaults, Trinity Large Thinking (free) from Arcee AI ranks cheapest right now (~$0 / month). Plug your own monthly token volumes into the calculator on this page for a workload-specific number.
Is the cheapest LLM always the right choice for text classification?
Not always. Cheap models often trade off reasoning quality, tool reliability, or context size. Use the cheapest as a baseline and benchmark against a tier-up model on your own evaluation set before committing to a contract — quality differences compound over millions of tokens.