Models API

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.

Response shape

The endpoint returns an OpenAI-compatible list object:

{
  "object": "list",
  "data": [
    {
      "id": "gpt-5.4",
      "object": "model",
      "model_type": "chat",
      "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
    }
  ]
}

Field reference

string

The response container type. This is usually list.

array

The currently available model records. Treat this as dynamic rather than a permanent catalog.

string

The model ID to pass as model. Chat workflows can also use selectors such as default, fast, and pro where supported. Image generation and edit workflows require a concrete image model ID, such as gpt-image-2.

string

The broad model family or endpoint type. Video-capable models use model_type: "video".

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.

object

Per-model pricing metadata used to calculate request cost for paid usage and paid allowance accounting.

number

Input-token price in the listed currency and unit.

number

Output-token price in the listed currency and unit.

number

Per-unit price for non-token billing modes. For video models this is the compatible, base, or minimum per-second price; route-level prices in pricing.route_prices_usd_per_second can be the source of truth for the final hold.

array

Optional route-level video prices. When present, select the route that matches the submitted model, request type, and other route dimensions, then use selected_route.public_price_usd_per_second for per-second billing.

string

The pricing currency, for example USD.

string

The pricing unit, for example 1M tokens.

number

Optional minimum cost applied to each request, even when the input and output token total would cost less.

number

Optional cache accounting floor. When present, cache-related billing treats each request as using at least this many cache tokens.

string

How pricing is calculated. token means usage is priced from input and output token counts. second means generated media is priced per second, using route-level video prices when available and pricing.price as the fallback.

object

Input and output types supported by the model. Models with image in modalities.input can accept image inputs for vision or reference workflows. Models with image in modalities.output can produce image outputs through image endpoints. Models with video in modalities.output can produce video outputs through the Video API.

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.

boolean

true means the model is only available through paid usage accounting, such as a Pro allowance or topped-up balance.

boolean

Whether the model supports streamed responses.

boolean

Whether the model supports JSON mode.

boolean

Whether the model supports reasoning-style behavior.

boolean

Whether the model supports tool and function calling.

object

Endpoint-specific capability metadata. Video models expose fields such as video_generation, video_async, supported_seconds, supported_sizes, max_reference_images, max_reference_image_bytes, requires_reference_image, atlascloud_video, and atlascloud_routes.

boolean

true when the video model is backed by Atlas routes.

array

Atlas route metadata for video models. Use this with pricing.route_prices_usd_per_second to validate supported request types, route choices, and route-priced billing.

Access and limits

Access type	Models	Token availability	Rate limits
Anonymous	`turbo` models	500,000 tokens per day	1 request/second, 10/minute, 60/hour
Free token	`turbo` models	1,000,000 tokens per day	2 requests/second, 40/minute, 100/hour
Pro subscription	`pro` and `turbo` models	Dynamic Pro allowance for the billing period	Higher paid limits
Topped-up balance	`pro` and `turbo` models	Usage billed from balance	Higher 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.

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. For video models with pricing_mode: "second", estimate the reserved hold from the requested duration. When pricing.route_prices_usd_per_second is present, route-level prices are authoritative:

reserved_hold = seconds * selected_route.public_price_usd_per_second

Fall back to seconds * pricing.price only when the model does not include pricing.route_prices_usd_per_second. The hold is consumed when video generation completes successfully and released when upstream generation fails or task creation is not accepted.

Video model records

Video-capable models are listed with model_type: "video" and asynchronous video generation capabilities:

{
  "id": "seedance-2.0-mini",
  "object": "model",
  "model_type": "video",
  "pricing_mode": "second",
  "pricing": {
    "price": 0.01,
    "currency": "USD",
    "unit": "second",
    "route_prices_usd_per_second": [
      {
        "route": "seedance-2.0-mini",
        "request_type": "text-to-video",
        "public_price_usd_per_second": 0.01
      },
      {
        "route": "seedance-2.0-fast",
        "request_type": "text-to-video",
        "public_price_usd_per_second": 0.02
      }
    ]
  },
  "modalities": {
    "input": ["text", "image"],
    "output": ["video"]
  },
  "capabilities": {
    "video_generation": true,
    "video_async": true,
    "supported_seconds": [5, 8],
    "supported_sizes": ["480p", "720p", "1080p"],
    "max_reference_images": 2,
    "max_reference_image_bytes": 8388608,
    "requires_reference_image": false,
    "atlascloud_video": true,
    "atlascloud_routes": [
      {
        "route": "seedance-2.0-mini",
        "request_types": ["text-to-video", "image-to-video"]
      },
      {
        "route": "seedance-2.0-fast",
        "request_types": ["text-to-video", "image-to-video"]
      }
    ]
  }
}

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 imageOutputModels = models.filter((model) =>
  model.modalities?.output?.includes("image")
);

const videoModels = models.filter(
  (model) =>
    model.model_type === "video" &&
    model.capabilities?.video_generation === true
);

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 chat 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. For image workflows, use a concrete image model ID with the Image generation and edits endpoints. For video workflows, use a concrete video model ID with the Video generation endpoint.

Getting started

Text generation

Images

Videos

AI tools

Response shape

Field reference

Access and limits

Estimating request cost

Video model records

Choosing a model programmatically

​Response shape

​Field reference

​Access and limits

​Estimating request cost

​Video model records

​Choosing a model programmatically

Response shape

Field reference

Access and limits

Estimating request cost

Video model records

Choosing a model programmatically