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Use the Models API to list the models that are currently available through LLM7.io. 
curl https://api.llm7.io/v1/models
The catalog is live. Model IDs, pricing, tiers, context windows, and capability flags can change as upstream availability changes. Check this endpoint at startup, on a schedule, or before showing model choices in your own UI.

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Response shape

The endpoint returns an OpenAI-compatible list object: 
{
  "object": "list",
  "data": [
    {
      "id": "gpt-5.4",
      "object": "model",
      "created": 1782277907,
      "owned_by": "",
      "tier": "pro",
      "pricing": {
        "input": 0.5,
        "output": 4.5,
        "minimum_request_price_usd": 0.0001,
        "minimum_cache_tokens": 300,
        "currency": "USD",
        "unit": "1M tokens"
      },
      "pricing_mode": "token",
      "modalities": {
        "input": ["text"],
        "output": ["text"]
      },
      "context_window": {
        "tokens": 1050000,
        "chars": null
      },
      "usage_based_only": true,
      "stream": true,
      "json_mode": true,
      "reasoning": true,
      "tools_calling": true
    }
  ]
}

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Field reference

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object
string
The response container type. This is usually list.
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data
array
The currently available model records. Treat this as dynamic rather than a permanent catalog.
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data[].id
string
The model ID to pass as model in /v1/chat/completions. You can also use selectors such as default, fast, and pro where supported.
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data[].tier
string
The access tier for the model.turbo models are fast models available to anonymous and free-token users, subject to lower rate and token limits.pro models are available to Pro subscribers and users with a topped-up balance. Pro subscription allowance is calculated dynamically across the billing period and can be checked in the dashboard.
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data[].pricing
object
Per-model pricing metadata used to calculate request cost for paid usage and paid allowance accounting.
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data[].pricing.input
number
Input-token price in the listed currency and unit.
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data[].pricing.output
number
Output-token price in the listed currency and unit.
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data[].pricing.currency
string
The pricing currency, for example USD.
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data[].pricing.unit
string
The pricing unit, for example 1M tokens.
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data[].pricing.minimum_request_price_usd
number
Optional minimum cost applied to each request, even when the input and output token total would cost less.
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data[].pricing.minimum_cache_tokens
number
Optional cache accounting floor. When present, cache-related billing treats each request as using at least this many cache tokens.
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data[].pricing_mode
string
How pricing is calculated. token means usage is priced from input and output token counts.
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data[].modalities
object
Input and output types supported by the model. Models with image in modalities.input can accept image inputs for vision workflows. Output is usually text.
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data[].context_window
object
The maximum context the model can process in one request, including prompt input and generated output. Models may report this in tokens, chars, or both.
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data[].usage_based_only
boolean
true means the model is only available through paid usage accounting, such as a Pro allowance or topped-up balance.
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data[].stream
boolean
Whether the model supports streamed responses.
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data[].json_mode
boolean
Whether the model supports JSON mode.
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data[].reasoning
boolean
Whether the model supports reasoning-style behavior.
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data[].tools_calling
boolean
Whether the model supports tool and function calling.

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Access and limits

Access typeModelsToken availabilityRate limits
Anonymousturbo models500,000 tokens per day1 request/second, 10/minute, 60/hour
Free tokenturbo models1,000,000 tokens per day2 requests/second, 40/minute, 100/hour
Pro subscriptionpro and turbo modelsDynamic Pro allowance for the billing periodHigher paid limits
Topped-up balancepro and turbo modelsUsage billed from balanceHigher paid limits
After a Pro subscription allowance is reached, requests can continue from a topped-up balance and are billed from model pricing, token counts, and any per-request minimums.
You can see current Pro allowance and billing status in the LLM7.io dashboard.

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Estimating request cost

For token-priced models, calculate cost from the input and output token counts: 
cost = (input_tokens * pricing.input + output_tokens * pricing.output) / 1_000_000
If minimum_request_price_usd is present, the charged request cost is at least that value: 
charged_cost = max(cost, pricing.minimum_request_price_usd)
Use the live currency and unit fields instead of assuming all models share the same pricing unit forever.

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Choosing a model programmatically

Use the live fields instead of hard-coding model names: 
const response = await fetch("https://api.llm7.io/v1/models");
const { data: models } = await response.json();

const visionModels = models.filter((model) =>
  model.modalities?.input?.includes("image")
);

const jsonStreamingModels = models.filter(
  (model) => model.json_mode && model.stream
);

const affordableProModels = models.filter(
  (model) => model.tier === "pro" && model.pricing?.input <= 0.1
);
For most integrations, start with the selectors in Available models. Use this endpoint when you need to display live options, filter by capability, estimate cost, or validate that a specific model ID is still available.