Beam 1.0 vs SambaCloud

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 It is a free public beta with unpublished daily limits and no per-token price, reasoning can't be turned off, and three API incidents were posted between 14 and 16 October 2026.

SambaCloud

Per-token prices and the model list are readable without a key at /v1/models, and a free tier needs no card. Production models get two to three weeks' notice before removal, 13 model ids left between March and June 2026, and the docs disagree with the live catalogue on MiniMax-M2.7.

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

SambaCloud

1. Call GET https://api.sambanova.ai/v1/models at start-up and use only ids it returns. The docs name models the endpoint no longer lists
2. Read x-ratelimit-remaining-requests and x-ratelimit-remaining-requests-day on every response. The free tier allows 20 requests a day per model
3. Treat 429 queue_full and 503 maintenance as retryable after a delay, and 410 model_deprecated as a signal to change model
4. Validate JSON output yourself. Schema enforcement is best effort and strict: true changes nothing
5. Check max_completion_tokens per model. DeepSeek-V3.1 caps output at 7,168 tokens and Llama 3.3 70B at 3,072