Cerebras Inference vs Jais 3

Cerebras Inference

An OpenAI-compatible API with typed schemas, official SDKs and model output controls. Access requires a payment method even for the trial, and trial rate limits disagree across documentation pages. Shared-model availability and dedicated-model availability differ.

Jais 3

Jais 3 runs on the owner's hardware through OpenAI-compatible servers in vLLM and SGLang, with tool calls, three reasoning levels and a 512K context, from a 2.2 GB 0.9B model to a 758 GB 375B one. The weights download without sign-up under Apache-2.0, but every launch command trusts remote code, and no technical report or security contact is published.

Cerebras Inference

1. Set CEREBRAS_API_KEY and use https://api.cerebras.ai/v1 with an OpenAI-compatible client.
2. Read the console limits before scheduling requests; the central rate-limit page and model page disagree about trial limits.
3. Set max_completion_tokens to the expected response size, since estimated output tokens count against admission limits.
4. Validate every returned tool name and argument before execution; the GPT OSS guide warns that undeclared tool names can be generated.

Jais 3

1. Start vLLM or SGLang with --reasoning-parser k2_horizon, and add --tool-call-parser k2_horizon (vLLM also --enable-auto-tool-choice) for tools, or tool calls come back as raw text
2. Pass reasoning_effort (high, medium or low) and tool_call_format inside chat_template_kwargs. Other values return a template error
3. Send each earlier assistant turn back with reasoning_content as a string, even an empty one. A missing or null thinking field makes the template raise an error
4. Send your own system message, or set default_system_prompt to an empty string. Without one the template inserts an identity prompt naming IFM, Inception42 and Cerebras
5. Budget at least 32,768 output tokens at high effort, which the cards recommend for every request