Reflection Beam Open Weights: Hardware, API Access and Limits

Reflection released Beam’s open weights on October 16, 2026. Its release package also includes a public beta API and the Mirror coding agent. Developers can now evaluate the coding and reasoning model through a hosted service or deploy it themselves. The practical decision comes down to hardware capacity and how much control the application needs.

What Reflection released

The release announcement lists BF16, FP8 and NVFP4 checkpoints, plus a speculative decoding drafter. Beam uses a mixture-of-experts architecture: it has roughly 501 billion parameters in total, with 23 billion active for each token. Its model card specifies text input and output, a one-million-token context window and Apache 2.0 licensing. That makes it relevant to teams building coding assistants or adapting a model to internal workflows.

Beam self-hosting hardware

The active parameter count describes the computation used to produce a token. The full collection of weights still needs somewhere to live. Hugging Face puts the BF16 checkpoint at about 1 TB and recommends planning for four B200 GPUs when serving NVFP4 with room for longer contexts and concurrent requests. Its deployment guide directs older GPUs toward FP8. These are server-scale deployment choices, even though each token uses a smaller fraction of the model.

For a small engineering team, a hosted evaluation is a reasonable first step. Take a few repository issues with known resolutions, run Beam through the same tools your current agent uses, and compare whether the changes pass tests. Record elapsed time and the amount of human correction required. Those results give you something useful to weigh against the cost of operating your own inference service.

Beam API compatibility and limits

There is an integration detail worth checking before switching providers. Reflection’s OpenAI compatibility documentation covers Chat Completions and Models. It does not support the Responses endpoint. A client using Chat Completions can change its base URL, key and model ID; a Responses-based application needs additional work. The API supports tool calls and JSON-schema outputs, but accepts text only. The documentation also says stop sequences have no effect and sampling is not seeded, so compatibility tests should cover those behaviors.

Reflection describes its public beta as free to start. Capacity still has limits. Its rate-limit documentation applies request and token allowances across an organization’s projects and keys. Daily limits renew at midnight UTC, and reasoning tokens count toward usage. A parallel coding workflow can hit those allowances faster than a chat demo, especially when several agents repeatedly read large repository contexts. Check the actual account allowance before scheduling a batch evaluation.

Evaluate Beam with its coding harness

Mirror provides a terminal interface with tool-call approval, resumable conversations and parallel agents in separate worktrees. Reflection says it trained Beam alongside this harness. That gives developers a reference configuration to evaluate, while also making the harness an important part of any comparison. Keep permissions, tools and retry budgets consistent when comparing results with another coding system.

Hugging Face published small, single-run trials at low and high reasoning effort. One math example returned the correct answer at low effort but omitted the requested justification; high effort supplied it. These partner-run examples are useful prompts to reproduce, with too few runs to establish general reliability.

Beam is worth evaluating when ownership or customization could solve a concrete deployment problem. Start with a bounded workload, establish the quality you need, then compare hosted costs with a realistic self-hosting plan that includes spare memory and operational work.

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