Reflection AI’s October 5, 2026 benchmark table shows Beam scoring below some named models on several coding and agentic tests, but coverage varies by benchmark. Reflection also claims Beam uses 3–4× less inference compute than GLM-5.2 on advanced reasoning benchmarks. That figure is an estimate of generation forward-pass compute—not a verified advantage in speed, energy use, or full serving cost—and Reflection’s performance claims had not been independently verified.
What Beam is—and what is available
Reflection describes Beam as its first open-weight model, built for coding, reasoning, and agentic workloads. The company’s October 5, 2026 announcement calls it a sparse mixture-of-experts model with 501 billion total parameters and 23 billion active parameters per token. Reflection AI’s announcement said the weights, technical report, model card, and developer artifacts were still forthcoming while the model underwent final red-teaming and evaluations. An announcement of an open-weight model is not the same as public availability of its weights.
How Beam compares on the reported coding and agentic tests
The scores below are from Reflection’s published table. Compare only entries in the same benchmark row: the set of models reported varies, and NR means Reflection did not report a result for that model in that row.
| Benchmark | Beam | Other reported results |
|---|---|---|
| SWE Bench Pro v2-Hard | 77.2 | GLM 5.3: 84.3; Kimi K3: 88.2 |
| Terminal Bench v2.1 | 80.1 | GLM 5.3: 88.2; Kimi K3: 88.3; DeepSeek V4.1 Flash: 90.6 |
| SWE Bench Pro v1 | 65.5 | Qwen 3.8-Max: 67.7; GLM 5.2: 62.1 |
| SWE-bench Verified | 80.9 | Most comparison cells are NR in Reflection’s table |
On the two rows with Beam and multiple higher reported scores, Beam is below the listed models: GLM 5.3 and Kimi K3 on SWE Bench Pro v2-Hard; and GLM 5.3, Kimi K3, and DeepSeek V4.1 Flash on Terminal Bench v2.1. The SWE Bench Pro v1 row is mixed: Beam is below Qwen 3.8-Max but above GLM 5.2. The SWE-bench Verified score does not establish a broad rank because most comparison entries are NR.
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These results do not show that Beam trails every leading open model on coding. Model coverage differs across tests, and Reflection says it used Artificial Analysis and DataCurve data for other models. A score comparison should therefore be read as a benchmark-by-benchmark snapshot, not a universal ranking. Reflection’s announcement and benchmark table are the source for these figures.
What the 3–4× inference-compute claim means
Reflection says Beam achieves scores comparable to GLM-5.2 on advanced reasoning benchmarks while using 3–4× less inference compute. Its estimate covers generation forward-pass compute, using approximately 2 × active parameter count × mean generated tokens per attempt. For mixture-of-experts models, the calculation uses active parameters per token rather than total parameters.
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The estimate excludes prompt prefill, context-dependent attention operations, and serving overhead. It is therefore an approximate comparison of one part of inference, not a measurement of total deployment cost or end-to-end performance. The ratio should not be read as proof that Beam is 3–4× faster, cheaper to serve, or more energy-efficient. TechCrunch’s October 5, 2026 report noted that Reflection’s performance claims had not been independently verified.
Scale and training figures Reflection reports
Reflection’s announcement also describes the scale of the model and its training. These are company-reported figures, not independent audits:
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- 501 billion total parameters, with 23 billion active per token.
- 23.8 trillion pretraining tokens.
- More than 100 million reinforcement-learning rollouts.
- 10,500 NVIDIA GB300 GPUs used for four weeks in the reported reinforcement-learning run.
- Approximately 1.3 billion sandboxes used for training and grading.
The GB300 figure describes Reflection’s reported training run; it does not establish hardware requirements for users. The announcement does not specify a reader-facing deployment configuration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to conclude from the announcement
Beam’s published results place it below some named models on particular coding and agentic benchmarks, while another reported comparison is mixed and one lacks enough reported peers to establish a broad rank. Reflection’s lower-compute claim concerns an approximate generation-compute calculation against GLM-5.2 on advanced reasoning benchmarks, not a verified end-to-end cost or speed advantage. As of October 5, 2026, the model weights and supporting technical materials had not yet been released.
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