Jais 3 vs Beam 1.0

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.

Beam 1.0

The Reflection API is OpenAI-compatible, with an OpenAPI document, strict structured outputs and tool calls, and error codes that say when to retry, and the weights are Apache-2.0. It is a free public beta with unpublished daily limits and no per-token price, reasoning can't be turned off, and three API incidents were posted between 14 and 16 October 2026.

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

Beam 1.0

1. Send reasoning_effort low or medium for short tasks. The default is max, and none or minimal return 400 unsupported_value
2. Return every assistant message with its reasoning_content unchanged. If a turn comes back without it, start a new conversation, since later requests fail with missing_required_parameter
3. Don't rely on stop or seed. Both are accepted and ignored, so truncate output in your own code
4. Honour x-should-retry: false on a 429. It means a daily limit is spent and Retry-After counts to 00:00 UTC
5. Set max_completion_tokens yourself on long prompts. The default budget isn't reduced to fit the prompt and includes reasoning tokens