Cheapest LLM APIs for high-volume tasks

Classification, tagging, extraction, moderation, translation of short strings, routing: jobs like these run millions of times, and the price per token decides whether they pay off. Below are the lowest-priced text models on GetVibe API. The prices are pulled live from the catalog every time this page is rendered, so the table never goes stale.

The shortlist

These were among the lowest-priced text models in our catalog on September 25, 2026. Prices move, so check the current order on the Models page.

How to choose between them

• You need strict JSON back: use a model with JSON mode, such as GPT-6 Luna, Qwen 3.8 Flash, DeepSeek V4.1 Flash or Hy4 Preview. The Gemini Flash models and GLM-5.3 Flash do not offer JSON mode in our catalog. Our JSON mode matrix has the details.
• You process long documents: Gemini 3.8 Flash, Gemini 3.7 Flash, DeepSeek V4.1 Flash and GLM-5.3 Flash have a window of about one million tokens.
• You want reasoning you can dial down: GPT-6 Luna accepts all six reasoning_effort levels. Set minimal for bulk work. Hy4 Preview accepts low and high. See reasoning_effort explained.
• Your input includes images: every model in the table above accepts image input.

Five ways to keep a bulk job cheap

1. Test quality on a sample first. Run a few hundred real items through two or three candidates and compare against a hand-checked answer key. The cheapest model that passes wins.
2. Keep the instruction as a fixed prefix. Put the long instruction and the examples first and the item last, so repeated requests hit the prompt cache. Cached input is billed at a fraction of the input rate (how caching works).
3. Cap the output. Set max_tokens to what a label or a short JSON object needs. Output is priced higher than input on every model above.
4. Keep reasoning low. Reasoning tokens are billed as output. For simple labels, use minimal or low where the model supports it.
5. Escalate only failures. Send everything to a cheap model, validate the output, and retry only the failures on a stronger model.

from openai import OpenAI

client = OpenAI(base_url="https://getvibeapi.com/v1", api_key="sk-pv-...")
INSTRUCTION = "Classify the ticket as billing, bug, feature or other. Reply with one word."

def classify(ticket: str) -> str:
    resp = client.chat.completions.create(
        model="gpt-6-luna",
        reasoning_effort="minimal",
        max_tokens=5,
        messages=[
            {"role": "system", "content": INSTRUCTION},
            {"role": "user", "content": ticket},
        ],
    )
    return resp.choices[0].message.content.strip()

Next steps

Sort the full catalog by price on the Models page, read about Hy4 Preview and Qwen 3.8 Flash, or start from the documentation. Billing is per token from a prepaid balance, with no subscription.