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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Reflection AI has introduced Beam, its first open-weight model, but its weights are not yet available. Reflection says it plans to publish them later in October 2026 under the Apache 2.0 license. Separately, Axios reports that Mistral plans to release open weights for Large 4 on October 27. Neither model should be treated as broadly downloadable as of October 7.
What has been announced—and what is still pending
Reflection announced Beam on October 5 as a sparse mixture-of-experts model for coding, reasoning and agentic workloads. Its announcement describes a model with 501 billion total parameters and 23 billion active parameters. Reflection says the weights, technical report, model card and developer artifacts are due later in October; the company has not supplied a specific release day in the cited announcement. The planned Apache 2.0 license is also a company-stated plan, not yet a license attached to publicly released weights. Reflection’s announcement
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Axios reported on October 6 that Mistral was finishing Large 4, a multimodal model with one trillion total parameters and 49 billion active parameters. The report says Mistral planned to release its weights on October 27 after further reinforcement-learning and safety work. That date is a reported target, not a guarantee. Axios’s October 6 report
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Axios’s October 4 story forecast other Western releases during the month, citing unnamed sources. The October 6 follow-up identifies Reflection and Mistral; the evidence here does not establish a confirmed full roster of additional models. Axios’s October 4 report
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Will Reflection AI’s first model rival top Chinese open-weight models?
That is Reflection’s claim, not an independently established verdict. The company says Beam is competitive with GLM 5.2 and approaches Qwen 3.8-Max on some coding and agentic tasks. Its published table is mixed rather than a single across-the-board win: it reports 80.9 on SWE-bench Verified, 80.1 on Terminal Bench v2.1 and 90.5 on GPQA Diamond. These scores belong to different tests and cannot be compared directly with one another.
Reflection also says Beam’s reasoning scores are comparable to GLM 5.2 while requiring three to four times less inference compute. That efficiency comparison is likewise the company’s claim. TechCrunch reported that Reflection’s performance claims had not been independently verified at publication, and no independent evaluation is established in the cited reporting. TechCrunch’s report
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What Reflection says went into Beam
Reflection says Beam was pretrained on 23.8 trillion tokens. The company also reports more than 100 million reinforcement-learning rollouts conducted over four weeks on 10,500 NVIDIA GB300 GPUs. These figures describe Reflection’s account of its training process; they are not independently audited in the cited sources. Reflection’s announcement
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Those training details do not resolve how Beam will perform in outside testing or at real-world deployment costs. The benchmark figures and compute comparison remain vendor-published until independent results or a reproducible public evaluation are available.
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How Beam and Mistral Large 4 differ on current evidence
| Model | Scope and stated size | Availability and access | Performance evidence in cited reporting |
|---|---|---|---|
| Reflection Beam | Text-only, according to TechCrunch; 501 billion total parameters and 23 billion active parameters, according to Reflection. | Weights and developer artifacts planned for later in October; Reflection says Apache 2.0. No specific Beam release day is stated in the announcement. | Reflection-published benchmark scores and comparisons; independent verification was not established by TechCrunch’s report. |
| Mistral Large 4 | Multimodal, according to Axios; one trillion total parameters and 49 billion active parameters, as reported by Axios. | Axios reported an October 27 planned weight release, following additional reinforcement-learning and safety work. A release guarantee or license is not stated in the cited report. | Performance scores are not stated in the cited report. |
Model parameter counts describe different things: total parameters are the model’s full parameter pool, while active parameters refer to those engaged for a given inference path in a sparse model. They are not a direct measure of quality, nor do they establish comparable serving costs across architectures.
Why companies want open weights—and the security tradeoff
Published weights can give organizations more control than a hosted-only model: they may run the model on their own infrastructure, adapt it, and make decisions about how it interacts with organizational data. Reflection CEO Misha Laskin told Axios that when organizations spend heavily on intelligence, they may want to move “from renting it to owning it yourself,” and described open models as customizable. Mistral VP of science Pierre Stock told Axios he did not want a future in which an oligopoly controls closed access to this kind of intelligence. Axios’s October 6 report
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That control comes with a security tradeoff. Axios notes that once weights are released, downstream users can more easily remove safeguards. Organizations considering self-hosting therefore need to evaluate not only model capability and operating cost, but also access controls, monitoring, policy enforcement and the consequences of modifications. Open weights do not automatically make a system secure, private or suitable for every use.
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Reflection said on October 5 that Beam’s weights and supporting artifacts would arrive later in October 2026, under Apache 2.0. It did not give a day in the cited announcement. Until the weights and accompanying materials are actually published, Beam remains an announced model rather than a generally downloadable one.
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