Reflection AI announced Beam on October 5, 2026, describing it as a sparse Mixture-of-Experts model with 501 billion total parameters and 23 billion active parameters. The company says it is designed for coding, reasoning, and agentic workloads. At announcement, Beam’s weights and supporting release materials were still planned for later in October, so availability, final license terms, and local-running requirements were not yet established.
What Beam is—and what its parameter counts mean
Beam is Reflection AI’s first model the company describes as open-weight. Its sparse Mixture-of-Experts (MoE) design has 501 billion total parameters, while Reflection reports 23 billion active parameters. In an MoE model, total parameters describe the model’s overall learned weights; active parameters describe the portion used for a given computation. The two figures therefore answer different questions and should not be treated as interchangeable measures of model size or hardware needs.
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Reflection positions Beam for coding, reasoning, and agentic work: tasks in which a model may need to use tools or take multiple steps toward a goal. That is the company’s intended use, not an independent finding that Beam is reliable on every such task.
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The precise description supported by Reflection’s announcement is open-weight, not necessarily open source. Reflection said it planned to publish the model weights and developer materials, but the announcement did not say that all training data or training code would be released. The company stated that it planned to use an Apache 2.0 license; the final license text and completed release terms were not established in the announcement.
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What performance results did Reflection publish?
Reflection reported the following benchmark results in its October 2026 announcement. These are company-reported scores, not independently reproduced results. Scores from different benchmarks use different tasks and evaluation methods, so they should not be compared as though they shared one scale.
| Benchmark | Score reported by Reflection |
|---|---|
| SWE-bench Verified | 80.9 |
| Terminal-Bench v2.1 | 80.1 |
| SWE-bench Pro v2-Hard | 77.2 |
| DeepSWE v1.1 | 44.4 |
| AIME 2026 | 97.8 |
| GPQA Diamond | 90.5 |
| MCP Atlas | 78.7 |
| AutomationBench public | 37.0 |
Reflection characterizes Beam’s coding and agentic results as competitive with larger open models such as GLM 5.2 and approaching Qwen 3.8-Max, while saying Kimi K3 remains ahead in raw capability. Those are the company’s comparisons; the announcement does not establish independent replication or a common evaluation setup across all models.
What Reflection’s compute-efficiency claim does—and does not—show
Reflection says Beam achieves advanced-reasoning scores comparable to GLM-5.2 with three to four times less inference compute. The company describes this as an estimate based on generated-token counts and active parameter count. It excludes prompt prefill, context-dependent attention operations, and serving overhead. It is therefore not a measured comparison of end-to-end speed, operating cost, or a customer’s bill.
Training figures and context length
Reflection says Beam was pretrained on 23.8 trillion tokens from web sources and proprietary licensed datasets. It also reports that its reinforcement-learning run generated more than 100 million rollouts on 10,500 NVIDIA GB300 GPUs over four weeks. These are figures reported by the company, not independently verified measurements. The use of GB300 GPUs for training does not specify what hardware Beam will require to run.
Reflection also discussed a 1-million-token effective context length in its midtraining explanation. That announcement does not establish the context length of a released configuration; users should look to the eventual model card for the supported setting.
Release status, access, and safety information
As of Reflection’s October 5 announcement, Beam was undergoing final red-teaming and evaluations. The company said selected users could sign up for early access and that it planned to release the weights, technical report, model card, and developer artifacts later in October 2026. It also said documentation and a stack for running, evaluating, and fine-tuning the model were planned. These were future plans at the time of the announcement, not confirmation that the materials or access were subsequently available.
Reflection says it conducted internal safety and alignment training and planned to publish safety-evaluation results in the technical report. Because that report and its results were still pending in the announcement, it does not establish independent safety certification.
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The announcement does not provide the information needed for a dependable local hardware recommendation. It does not establish minimum GPU memory, supported inference frameworks, current access terms, or a released configuration. The 23-billion active-parameter figure alone is not enough to calculate practical memory needs: the answer also depends on such details as weight format, context length, runtime, and serving setup.
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Before planning a deployment, check the released model card and developer artifacts for the actual license, supported configurations, hardware guidance, and evaluation results. Until those are available, a specific GPU or memory recommendation would be speculation.
How to assess Beam against other models
For a useful comparison, match the conditions rather than relying on model size or a headline score:
Quick Recap
- Compare results on the same benchmark version and evaluation setup.
- Check whether scores were independently reproduced and whether the model’s success is reliable across tasks.
- Measure inference tokens and serving costs on comparable workloads; do not substitute an approximate compute estimate for an end-to-end cost measurement.
- Verify the released model’s context length, hardware requirements, access terms, license, and safety documentation.
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