curl -sS 'https://api.cerebras.ai/public/v1/models' | jq
curl -sS 'https://api.cerebras.ai/public/v1/models?format=openrouter' | jq
curl -sS 'https://api.cerebras.ai/public/v1/models?format=huggingface' | jq
import httpx
# Default format
response = httpx.get("https://api.cerebras.ai/public/v1/models")
models = response.json()
# OpenRouter format
response = httpx.get(
"https://api.cerebras.ai/public/v1/models",
params={"format": "openrouter"}
)
models = response.json()
# HuggingFace format
response = httpx.get(
"https://api.cerebras.ai/public/v1/models",
params={"format": "huggingface"}
)
models = response.json()
// Default format
const response = await fetch('https://api.cerebras.ai/public/v1/models');
const models = await response.json();
// OpenRouter format
const response = await fetch(
'https://api.cerebras.ai/public/v1/models?format=openrouter'
);
const models = await response.json();
// HuggingFace format
const response = await fetch(
'https://api.cerebras.ai/public/v1/models?format=huggingface'
);
const models = await response.json();
{
"object": "list",
"data": [
{
"id": "gpt-oss-120b",
"object": "model",
"created": 1754438400,
"owned_by": "OpenAI",
"name": "OpenAI GPT OSS",
"description": "This model excels at efficient reasoning across science, math, and coding applications. It's ideal for real-time coding assistance, processing large documents for Q&A and summarization, agentic research workflows, and regulated on-premises workloads.",
"hugging_face_id": "openai/gpt-oss-120b",
"pricing": {
"prompt": "0.00000035",
"completion": "0.00000075"
},
"capabilities": {
"streaming": true,
"function_calling": true,
"structured_outputs": true,
"vision": false,
"json_mode": true,
"tools": true,
"tool_choice": true,
"parallel_tool_calls": false,
"response_format": true,
"reasoning": true
},
"supported_parameters": {
"temperature": true,
"top_p": true,
"seed": true,
"stop": true,
"max_completion_tokens": true,
"logprobs": true,
"top_logprobs": true,
"frequency_penalty": true,
"presence_penalty": true,
"logit_bias": true,
"repetition_penalty": false
},
"architecture": {
"modality": "text",
"tokenizer": "GPT",
"instruct_type": "harmony"
},
"limits": {
"max_context_length": 131072,
"max_completion_tokens": 40960,
"requests_per_minute": null,
"tokens_per_minute": null
},
"datacenter_locations": [],
"deprecated": false,
"preview": false,
"quantization": "FP16/8 (weights only)"
}
]
}
{
"data": [
{
"id": "gpt-oss-120b",
"hugging_face_id": "openai/gpt-oss-120b",
"name": "OpenAI GPT OSS",
"created": 1754438400,
"input_modalities": ["text"],
"output_modalities": ["text"],
"quantization": "fp16",
"context_length": 131072,
"max_output_length": 40960,
"pricing": {
"prompt": "0.00000035",
"completion": "0.00000075",
"request": "0",
"image": "0",
"input_cache_read": "0",
"input_cache_write": "0"
},
"supported_sampling_parameters": [
"temperature",
"top_p",
"stop",
"seed",
"frequency_penalty",
"presence_penalty",
"max_tokens",
"logprobs",
"top_logprobs",
"logit_bias"
],
"supported_features": [
"tools",
"json_mode",
"structured_outputs",
"reasoning"
]
}
]
}
{
"data": [
{
"id": "gpt-oss-120b",
"hugging_face_id": "openai/gpt-oss-120b",
"object": "model",
"created": 1754438400,
"owned_by": "OpenAI",
"context_length": 131072,
"pricing": {
"input": 0.35,
"output": 0.75
},
"capabilities": {
"streaming": true,
"function_calling": true,
"structured_outputs": true,
"vision": false
}
}
]
}
Public models
GET
/
public
/
v1
/
models
curl -sS 'https://api.cerebras.ai/public/v1/models' | jq
curl -sS 'https://api.cerebras.ai/public/v1/models?format=openrouter' | jq
curl -sS 'https://api.cerebras.ai/public/v1/models?format=huggingface' | jq
import httpx
# Default format
response = httpx.get("https://api.cerebras.ai/public/v1/models")
models = response.json()
# OpenRouter format
response = httpx.get(
"https://api.cerebras.ai/public/v1/models",
params={"format": "openrouter"}
)
models = response.json()
# HuggingFace format
response = httpx.get(
"https://api.cerebras.ai/public/v1/models",
params={"format": "huggingface"}
)
models = response.json()
// Default format
const response = await fetch('https://api.cerebras.ai/public/v1/models');
const models = await response.json();
// OpenRouter format
const response = await fetch(
'https://api.cerebras.ai/public/v1/models?format=openrouter'
);
const models = await response.json();
// HuggingFace format
const response = await fetch(
'https://api.cerebras.ai/public/v1/models?format=huggingface'
);
const models = await response.json();
{
"object": "list",
"data": [
{
"id": "gpt-oss-120b",
"object": "model",
"created": 1754438400,
"owned_by": "OpenAI",
"name": "OpenAI GPT OSS",
"description": "This model excels at efficient reasoning across science, math, and coding applications. It's ideal for real-time coding assistance, processing large documents for Q&A and summarization, agentic research workflows, and regulated on-premises workloads.",
"hugging_face_id": "openai/gpt-oss-120b",
"pricing": {
"prompt": "0.00000035",
"completion": "0.00000075"
},
"capabilities": {
"streaming": true,
"function_calling": true,
"structured_outputs": true,
"vision": false,
"json_mode": true,
"tools": true,
"tool_choice": true,
"parallel_tool_calls": false,
"response_format": true,
"reasoning": true
},
"supported_parameters": {
"temperature": true,
"top_p": true,
"seed": true,
"stop": true,
"max_completion_tokens": true,
"logprobs": true,
"top_logprobs": true,
"frequency_penalty": true,
"presence_penalty": true,
"logit_bias": true,
"repetition_penalty": false
},
"architecture": {
"modality": "text",
"tokenizer": "GPT",
"instruct_type": "harmony"
},
"limits": {
"max_context_length": 131072,
"max_completion_tokens": 40960,
"requests_per_minute": null,
"tokens_per_minute": null
},
"datacenter_locations": [],
"deprecated": false,
"preview": false,
"quantization": "FP16/8 (weights only)"
}
]
}
{
"data": [
{
"id": "gpt-oss-120b",
"hugging_face_id": "openai/gpt-oss-120b",
"name": "OpenAI GPT OSS",
"created": 1754438400,
"input_modalities": ["text"],
"output_modalities": ["text"],
"quantization": "fp16",
"context_length": 131072,
"max_output_length": 40960,
"pricing": {
"prompt": "0.00000035",
"completion": "0.00000075",
"request": "0",
"image": "0",
"input_cache_read": "0",
"input_cache_write": "0"
},
"supported_sampling_parameters": [
"temperature",
"top_p",
"stop",
"seed",
"frequency_penalty",
"presence_penalty",
"max_tokens",
"logprobs",
"top_logprobs",
"logit_bias"
],
"supported_features": [
"tools",
"json_mode",
"structured_outputs",
"reasoning"
]
}
]
}
{
"data": [
{
"id": "gpt-oss-120b",
"hugging_face_id": "openai/gpt-oss-120b",
"object": "model",
"created": 1754438400,
"owned_by": "OpenAI",
"context_length": 131072,
"pricing": {
"input": 0.35,
"output": 0.75
},
"capabilities": {
"streaming": true,
"function_calling": true,
"structured_outputs": true,
"vision": false
}
}
]
}
Retrieve details about publicly available Cerebras models without an API key, including context length, pricing, and supported features. This endpoint supports multiple response formats for compatibility with different platforms.
This endpoint is public and does not require an API key.
Supported Formats
The endpoint supports three response formats via theformat query parameter:
- Default (Cerebras) - Native Cerebras format
- OpenRouter - OpenRouter-compatible format
- HuggingFace - HuggingFace-compatible format
Query Parameters
Response format. Options:
openrouter, huggingface. Omit for default Cerebras format.Response
Default Format
The object type, which is always
list for list responses or model for single model responses.Array of model objects (only in list responses).
Show model properties
Show model properties
The model identifier (e.g.,
gpt-oss-120b).The object type, which is always
model.The Unix timestamp (in seconds) of when the model was created.
The organization that owns the model.
Human-readable model name.
Description of the model’s capabilities and recommended use cases.
The HuggingFace repository identifier for this model.
Boolean flags for supported features.
Show capability fields
Show capability fields
Whether streaming is supported.
Whether function calling is supported.
Whether structured outputs are supported.
Whether vision/image input is supported.
Whether JSON mode is supported.
Whether tool use is supported.
Whether tool choice is supported.
Whether parallel tool calls are supported.
Whether response format control is supported.
Whether the model supports reasoning.
Boolean flags for supported API parameters.
Whether this model is deprecated.
Whether this model is in preview.
Quantization method used for the model.
OpenRouter Format
Array of model objects with extended metadata.
Show extended properties
Show extended properties
The model identifier.
The HuggingFace repository identifier.
Human-readable model name.
Supported input types (e.g.,
["text"]).Supported output types (e.g.,
["text"]).Maximum context window size in tokens.
Maximum output length in tokens.
Pricing information per token.
Show pricing fields
Show pricing fields
Cost per prompt token.
Cost per completion token.
Cost per request (typically
"0").Cost per image token (typically
"0" for text-only models).Cost per cached input token read (typically
"0").Cost per cached input token write (typically
"0").List of supported sampling parameters.
List of supported features (e.g.,
["tools", "json_mode", "structured_outputs"]).HuggingFace Format
Array of model objects.
Show properties
Show properties
The model identifier.
The HuggingFace repository identifier.
The object type, which is always
model.The Unix timestamp (in seconds) of when the model was created.
The organization that owns the model.
Maximum context window size in tokens.
Boolean flags for supported features.
curl -sS 'https://api.cerebras.ai/public/v1/models' | jq
curl -sS 'https://api.cerebras.ai/public/v1/models?format=openrouter' | jq
curl -sS 'https://api.cerebras.ai/public/v1/models?format=huggingface' | jq
import httpx
# Default format
response = httpx.get("https://api.cerebras.ai/public/v1/models")
models = response.json()
# OpenRouter format
response = httpx.get(
"https://api.cerebras.ai/public/v1/models",
params={"format": "openrouter"}
)
models = response.json()
# HuggingFace format
response = httpx.get(
"https://api.cerebras.ai/public/v1/models",
params={"format": "huggingface"}
)
models = response.json()
// Default format
const response = await fetch('https://api.cerebras.ai/public/v1/models');
const models = await response.json();
// OpenRouter format
const response = await fetch(
'https://api.cerebras.ai/public/v1/models?format=openrouter'
);
const models = await response.json();
// HuggingFace format
const response = await fetch(
'https://api.cerebras.ai/public/v1/models?format=huggingface'
);
const models = await response.json();
{
"object": "list",
"data": [
{
"id": "gpt-oss-120b",
"object": "model",
"created": 1754438400,
"owned_by": "OpenAI",
"name": "OpenAI GPT OSS",
"description": "This model excels at efficient reasoning across science, math, and coding applications. It's ideal for real-time coding assistance, processing large documents for Q&A and summarization, agentic research workflows, and regulated on-premises workloads.",
"hugging_face_id": "openai/gpt-oss-120b",
"pricing": {
"prompt": "0.00000035",
"completion": "0.00000075"
},
"capabilities": {
"streaming": true,
"function_calling": true,
"structured_outputs": true,
"vision": false,
"json_mode": true,
"tools": true,
"tool_choice": true,
"parallel_tool_calls": false,
"response_format": true,
"reasoning": true
},
"supported_parameters": {
"temperature": true,
"top_p": true,
"seed": true,
"stop": true,
"max_completion_tokens": true,
"logprobs": true,
"top_logprobs": true,
"frequency_penalty": true,
"presence_penalty": true,
"logit_bias": true,
"repetition_penalty": false
},
"architecture": {
"modality": "text",
"tokenizer": "GPT",
"instruct_type": "harmony"
},
"limits": {
"max_context_length": 131072,
"max_completion_tokens": 40960,
"requests_per_minute": null,
"tokens_per_minute": null
},
"datacenter_locations": [],
"deprecated": false,
"preview": false,
"quantization": "FP16/8 (weights only)"
}
]
}
{
"data": [
{
"id": "gpt-oss-120b",
"hugging_face_id": "openai/gpt-oss-120b",
"name": "OpenAI GPT OSS",
"created": 1754438400,
"input_modalities": ["text"],
"output_modalities": ["text"],
"quantization": "fp16",
"context_length": 131072,
"max_output_length": 40960,
"pricing": {
"prompt": "0.00000035",
"completion": "0.00000075",
"request": "0",
"image": "0",
"input_cache_read": "0",
"input_cache_write": "0"
},
"supported_sampling_parameters": [
"temperature",
"top_p",
"stop",
"seed",
"frequency_penalty",
"presence_penalty",
"max_tokens",
"logprobs",
"top_logprobs",
"logit_bias"
],
"supported_features": [
"tools",
"json_mode",
"structured_outputs",
"reasoning"
]
}
]
}
{
"data": [
{
"id": "gpt-oss-120b",
"hugging_face_id": "openai/gpt-oss-120b",
"object": "model",
"created": 1754438400,
"owned_by": "OpenAI",
"context_length": 131072,
"pricing": {
"input": 0.35,
"output": 0.75
},
"capabilities": {
"streaming": true,
"function_calling": true,
"structured_outputs": true,
"vision": false
}
}
]
}
Retrieve Specific Model
You can also retrieve information about a specific model by appending the model ID to the endpoint:GET https://api.cerebras.ai/public/v1/models/{model_id}
Path Parameters
The ID of the model to retrieve (e.g.,
gpt-oss-120b, zai-glm-4.7).curl -sS 'https://api.cerebras.ai/public/v1/models/gpt-oss-120b' | jq
import httpx
model_id = "gpt-oss-120b"
response = httpx.get(f"https://api.cerebras.ai/public/v1/models/{model_id}")
model = response.json()
const modelId = 'gpt-oss-120b';
const response = await fetch(
`https://api.cerebras.ai/public/v1/models/${modelId}`
);
const model = await response.json();
{
"id": "gpt-oss-120b",
"object": "model",
"created": 1754438400,
"owned_by": "OpenAI",
"name": "OpenAI GPT OSS",
"description": "This model excels at efficient reasoning across science, math, and coding applications. It's ideal for real-time coding assistance, processing large documents for Q&A and summarization, agentic research workflows, and regulated on-premises workloads.",
"hugging_face_id": "openai/gpt-oss-120b",
"pricing": {
"prompt": "0.00000035",
"completion": "0.00000075"
},
"capabilities": {
"streaming": true,
"function_calling": true,
"structured_outputs": true,
"vision": false,
"json_mode": true,
"tools": true,
"tool_choice": true,
"parallel_tool_calls": false,
"response_format": true,
"reasoning": true
},
"limits": {
"max_context_length": 131072,
"max_completion_tokens": 40960,
"requests_per_minute": null,
"tokens_per_minute": null
},
"deprecated": false,
"preview": false,
"quantization": "FP16/8 (weights only)"
}
Use Cases
Platform Integration - Use the OpenRouter or HuggingFace formats to integrate Cerebras models into existing platforms that support these standards. Model Discovery - Programmatically discover available models and their capabilities without authentication. Pricing Comparison - Compare pricing across different models using the structured pricing information. Feature Detection - Check which features (streaming, tools, JSON mode) are supported by each model.Was this page helpful?
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