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Reflection AI’s Beam has 501 billion parameters in total, but the company says it activates 23 billion per token. That distinction matters because the active count helps estimate the model’s compute for generating each token; the total count describes the full model. It does not, by itself, tell you Beam’s latency, serving cost, or overall performance.
What “23B active parameters” means
Beam is a sparse mixture-of-experts (MoE) model, according to Reflection AI’s October 5, 2026 announcement. In an MoE model, different expert components can be selected for different tokens rather than running every parameter for every token. Reflection reports 501 billion parameters overall and 23 billion active per token.
The two numbers describe different things, not competing estimates of Beam’s size. The 501B figure is the model’s total parameter count. The 23B figure is the subset Reflection says is active for each token. That smaller active count helps explain why token-generation compute can be lower than the total size alone might suggest.
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Why active parameters matter—and what they cannot tell you
Generating text requires the model to process tokens. For a sparse model, the amount of computation used for a token depends in part on which parameters are activated. Reflection estimates generation forward-pass compute as roughly two times the number of active parameters multiplied by the mean number of generated tokens per attempt. The company says that generated-token count includes both reasoning and final-answer tokens.
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Using active parameters makes its estimate more relevant to token-generation compute than treating every parameter in the full model as active on every token. But it is not a complete measure of inference. Reflection says its comparison excludes prompt prefill, context-dependent attention operations, and serving overhead. It is therefore an approximate compute comparison, not a direct measurement of end-to-end latency, power use, or deployment cost.
For that reason, a smaller active count does not establish that Beam will be faster or cheaper in a particular deployment. Those outcomes also depend on factors beyond the per-token parameter count, including the prompt and context, generation settings, and serving implementation.
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How to interpret Reflection’s 3–4× compute claim
Reflection says Beam’s advanced-reasoning scores are comparable to GLM-5.2 while using 3–4× less inference compute. The company describes this as an estimate based on generation forward-pass compute, with the exclusions above—not as a measured reduction in serving cost or response time.
Reflection explains its treatment of MoE models this way: “For mixture-of-experts models, we used the parameters activated per token rather than the total model size.” That is the company’s stated basis for its comparison, not an independently established standard or independent validation of the result.
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When evaluating the claim, keep the task and measurement method together. Compare the benchmark task and protocol, score, token-generation settings, treatment of context and prefill, and whether results are vendor-reported or independently reproduced. A compute estimate and a benchmark score answer different questions; neither can be reduced to a single ranking.
What Beam’s published benchmarks show
Reflection’s announcement publishes results across coding and terminal tasks, reasoning, tool-calling and search, and general capability. The examples below are company-published scores; the announcement does not independently validate them. A missing entry means that the comparison row does not report a score for that model.
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| Benchmark | Beam | Other reported scores | What the row indicates |
|---|---|---|---|
| SWE-bench Verified | 80.9 | Inkling 77.6; Nemotron 3 Ultra 70.7 | Beam is above the other scores listed in this row; other listed models are unreported. |
| Terminal Bench v2.1 | 80.1 | GLM 5.2 81.0; GLM 5.3 88.2; Kimi K3 88.3; Qwen 3.8 Max 86.6; DeepSeek V4.1 Flash 90.6 | Beam is below every other score listed in this row. |
| AIME 2026 | 97.8 | Inkling 97.1; GLM 5.2 99.2 | Beam is above Inkling and below GLM 5.2; other listed entries are unreported. |
| GPQA Diamond | 90.5 | GLM 5.2 91.2; GLM 5.3 91.7; Kimi K3 93.5; Qwen 3.8 Max 92.6; DeepSeek V4.1 Flash 90.9 | Beam is below each other score listed in this row. |
These rows show why parameter counts and performance should not be conflated. Beam leads some reported comparisons and trails in others. Reflection characterizes it as competitive with GLM 5.2 and approaching Qwen 3.8-Max on coding and agentic tasks, while acknowledging that Kimi K3 remains ahead on raw capability. Those are Reflection’s assessments of its own published results.
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Reflection reports pretraining Beam on 23.8 trillion tokens. It also says the model underwent more than 100 million reinforcement-learning rollouts over four weeks on 10.5K NVIDIA GB300 GPUs, using approximately 1.3 billion sandboxes for training and grading. These are vendor-reported figures, not independently audited measurements established by the announcement.
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Reflection gives two context figures for different stages: a maximum context length of 256K tokens during the high-compute reinforcement-learning run, and an effective context length extended to 1M tokens through midtraining. They should not be treated as interchangeable descriptions of one training stage.
Availability, modality, and safety status
As of the October 5, 2026 announcement, Beam was undergoing final red-teaming and evaluations, with access offered to selected users through an early-access waitlist. Reflection said it planned to release weights under an Apache 2.0 license, along with a technical report, model card, documentation, and developer artifacts later in October 2026. These were plans stated at announcement time, not confirmation that the weights or materials had already been released.
Reflection describes Beam as text-only. It can work with information from other modalities when that information is represented as text, but the announcement does not describe Beam itself as a multimodal model. Reflection said safety evaluation results would appear with the future technical report; the announcement did not provide those results or establish independent safety validation.
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How to use the 23B and 501B figures
- Use 501B to describe Beam’s reported total parameter count.
- Use 23B to describe the parameters Reflection says are active per token and to understand the basis of its generation-compute estimate.
- Do not treat the active count as Beam’s total size or as a direct predictor of end-to-end speed, cost, or power use.
- Read the 3–4× figure as Reflection’s approximate generation-compute comparison against GLM-5.2, with the stated exclusions.
- Read benchmark scores as company-published results, and compare them task by task rather than inferring a universal winner.
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