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
string
Response format. Options:
openrouter, huggingface. Omit for default Cerebras format.Response
Default Format
string
The object type, which is always
list for list responses or model for single model responses.array
Array of model objects (only in list responses).
Show model properties
Show model properties
string
The model identifier (e.g.,
gpt-oss-120b).string
The object type, which is always
model.integer
The Unix timestamp (in seconds) of when the model was created.
string
The organization that owns the model.
string
Human-readable model name.
string
Description of the model’s capabilities and recommended use cases.
string
The HuggingFace repository identifier for this model.
object
object
Boolean flags for supported features.
Show capability fields
Show capability fields
boolean
Whether streaming is supported.
boolean
Whether function calling is supported.
boolean
Whether structured outputs are supported.
boolean
Whether vision/image input is supported.
boolean
Whether JSON mode is supported.
boolean
Whether tool use is supported.
boolean
Whether tool choice is supported.
boolean
Whether parallel tool calls are supported.
boolean
Whether response format control is supported.
boolean
Whether the model supports reasoning.
object
Boolean flags for supported API parameters.
object
object
boolean
Whether this model is deprecated.
boolean
Whether this model is in preview.
string
Quantization method used for the model.
OpenRouter Format
array
Array of model objects with extended metadata.
Show extended properties
Show extended properties
string
The model identifier.
string
The HuggingFace repository identifier.
string
Human-readable model name.
array
Supported input types (e.g.,
["text"]).array
Supported output types (e.g.,
["text"]).integer
Maximum context window size in tokens.
integer
Maximum output length in tokens.
object
Pricing information per token.
array
List of supported sampling parameters.
array
List of supported features (e.g.,
["tools", "json_mode", "structured_outputs"]).HuggingFace Format
array
Array of model objects.
Show properties
Show properties
string
The model identifier.
string
The HuggingFace repository identifier.
string
The object type, which is always
model.integer
The Unix timestamp (in seconds) of when the model was created.
string
The organization that owns the model.
object
integer
Maximum context window size in tokens.
object
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
string
required
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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