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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteReflection AI announced Beam on October 5, 2026, as its first open-weight model: a sparse Mixture-of-Experts (MoE) system with 501 billion total parameters and 23 billion active parameters. The company positions it for coding, reasoning, and agentic workloads. Its benchmark results and release plans are company-reported; the announcement does not establish that the weights or supporting materials have since been released.
What Beam is—and what its parameter counts mean
Beam is a sparse MoE model. Its 501 billion total parameters describe the model’s overall parameter count; 23 billion active parameters is the portion Reflection says is active for a given inference pass. The active count is not the model’s total size, nor does it by itself establish how much memory a local deployment needs.
Reflection says Beam is intended for coding, reasoning, and agentic work—tasks in which a model may use tools or carry out multi-step actions. The announcement also mentions an effective context length of 1 million tokens in its discussion of midtraining. That is an announced figure, not confirmation of the final released configuration; the eventual model card would be the place to verify supported context settings.
What Reflection reports about training
Reflection says Beam was pretrained on 23.8 trillion tokens drawn from web sources and proprietary licensed datasets. The company 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 Reflection, not independently verified measurements. The training hardware count does not specify what hardware is required to run Beam for inference.
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Beam’s announced benchmark results
The following scores are from Reflection’s October 2026 announcement. They are company-reported results, not independently reproduced results established here. Scores from different benchmarks use different tasks and scoring systems, so they should not be ranked against one another as if they shared a scale.
| Benchmark | Reflection-reported score |
|---|---|
| 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 as competitive with larger open models such as GLM 5.2 and as approaching Qwen 3.8-Max on coding and agentic tasks, while saying Kimi K3 remains ahead on raw capability. Those are the company’s comparisons; the announcement’s reported scores should not be treated as settled comparative performance without independent reproduction using comparable benchmark versions and evaluation setups.
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What the 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. Its explanation estimates compute using generated-token counts and active parameter count. It excludes prompt prefill, context-dependent attention operations, and serving overhead.
That estimate is not a measurement of end-to-end latency, speed, hosting expense, or a customer’s bill. A practical comparison would need the same workload, prompt and output lengths, evaluation setup, and serving conditions, alongside task success and reliability.
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Release, license, and safety status as of October 5, 2026
At announcement, Beam was still undergoing final red-teaming and evaluations. Reflection 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. The company also stated its intention to release the weights under an Apache 2.0 license and provide documentation and a stack for running, evaluating, and fine-tuning the model. These were plans at the time of announcement, not confirmation that the releases or terms were subsequently finalized.
Reflection describes internal safety and alignment training and said safety-evaluation results would appear in the technical report. That report and those results were pending when the announcement was published; the announcement alone does not establish independent safety certification.
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“Open-weight” is the supported description. A plan to publish model weights does not, by itself, establish that training data or training code will also be released, and the announcement does not claim that Beam is open source in that broader sense.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can you run Beam locally?
The announcement does not establish the model’s current availability, supported inference frameworks, minimum GPU or memory requirements, or final access terms. It also does not name distribution or hosting partners that are live. The fact that Reflection used GB300 GPUs for training is not an inference hardware specification, so it cannot support a local hardware recommendation.
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Before choosing hardware or building an integration, check the released model card and developer artifacts for the exact model configuration, quantization options, runtime support, memory requirements, license text, and access conditions. Until those details are available, local-run feasibility remains undetermined.
How to assess Beam against other models
For a useful comparison, check more than parameter counts or a single headline score:
Quick Recap
- Compare the same benchmark version and evaluation setup, and distinguish company-reported results from independent reproductions.
- Look at task success and reliability on the coding or agentic work you need, not only a benchmark aggregate.
- Compare inference-token use and serving costs under equivalent workloads; an active-parameter estimate alone does not capture total serving expense.
- Verify the released context configuration, hardware requirements, access terms, license, and safety documentation against the actual model materials.
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