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API compatibility - 6 documented limits

Cerebras Inference API compatibility

Cerebras Inference documents an OpenAI-compatible API at https://api.cerebras.ai/v1 (chat/completions). Its docs do not mention an Anthropic-compatible endpoint. The docs list 6 limitations: 6 for the OpenAI-compatible layer.

Verified against Cerebras Inference's own documentation. Not documented means the docs read that day do not say; it never means No.

Compare every provider in the API compatibility matrix.

OpenAI-compatible API

Supported
Base URL
https://api.cerebras.ai/v1
Endpoints
  • chat/completions
Auth
CEREBRAS_API_KEY passed as the OpenAI client's api_key
Notes
Compatible 'for many common workflows'. With Shared Inference the model field is an exact Cerebras model ID; with Dedicated Inference it is your endpoint ID. Cerebras-specific parameters (reasoning_format, clear_thinking) go through extra_body. Cerebras also ships its own Python and TypeScript SDKs.

From the docs

The Cerebras API is compatible with OpenAI client libraries for many common workflows. Existing applications can use Cerebras by changing the API key, base URL, and inference target.
inference-docs.cerebras.ai/resources/openai

Anthropic-compatible API

Not documented
Notes
The Cerebras OpenAI compatibility page read today does not mention an Anthropic-compatible endpoint.

Checked: inference-docs.cerebras.ai/resources/openai

Documented limitations

Parameters, content types and features Cerebras Inference's docs say its compatibility layers ignore, reject or change, each with the line it comes from. A layer with nothing listed may still differ in ways the docs do not spell out.

OpenAI-compatible layer6

Example from Cerebras Inference's docs

Copied verbatim from the provider's documentation, placeholders included.

Provider example
import os
import openai

client = openai.OpenAI(
    base_url="https://api.cerebras.ai/v1",
    api_key=os.environ.get("CEREBRAS_API_KEY")
)

response = client.chat.completions.create(
    model="qwen-3.8-27b",
    messages=[{"role": "user", "content": "Explain this code."}],
)

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