{"article":{"slug":"reflection-beam-early-access","kind":"model","source":{"title":"Introducing Beam: Reflection's 501B open-weight model","url":"https://reflection.ai/blog/introducing-beam","date":"2026-10-05"},"headline":"Reflection previews Beam with competitive older-baseline scores and pending weights","whatChanged":"Reflection introduced Beam on October 5 as a text-only mixture-of-experts model with 501 billion total parameters and 23 billion active per token. It invited early-access registrations while final red-teaming continued. Public weights, the technical report, model card, and developer artifacts were promised later in October under Apache 2.0. This was an announcement, not an immediate public download.\n\nReflection's coding table, explicitly updated October 8, puts Beam at 44.4 on DeepSWE v1.1, close to GLM 5.2's 44.0 and below GLM 5.3's 61.0. On Terminal Bench v2.1, Beam scores 80.1 versus GLM 5.2's 81.0 and GLM 5.3's 88.2. These are Reflection's published comparisons, not our independent tests.\n\nIts efficiency figures estimate generation compute from active parameters and generated tokens. They exclude prompt prefill, context-dependent attention, and serving overhead. The claim of less inference compute is not a measured hosting bill or API price.","whyItMatters":"Beam is close to GLM 5.2 on the cited tests, while the same table shows newer competitors ahead. That matters if the purchase depends on current coding performance rather than availability of public weights. The provider-selected results do not show a universal winner or matched production settings.\n\nCompanies interested in controlling their own deployment can prepare a bounded coding test while waiting for the actual artifacts. Separate total parameters from active parameters when asking a host about memory and throughput. Require measured expense per completed, checked task. The missing technical report and final red-teaming are reasons to keep the announcement out of a production replacement decision this week.","issuer":"Reflection","glance":"Reflection announced Beam October 5 with early-access registration; public weights and reports are promised later in October.","keyFact":"501B total, 23B active; weights still pending","action":"Prepare a reproducible coding task and ask for measured hosting cost when Beam artifacts become available.","visuals":[{"url":"https://reflection.ai/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fsp40emik%2Fproduction%2F954e6d4da152cd6bcf8ff1d4705c06c25efc6975-2070x1206.png%3Fw%3D3840%26fit%3Dmax&w=3840&q=75","alt":"Reflection coding table compares Beam with seven open-model peers; Beam scores 44.4 on DeepSWE v1.1 and 80.1 on Terminal Bench v2.1.","caption":"Reflection's coding comparison, updated October 8. DeepSWE v1.1 scores are 44.4 for Beam, 44.0 for GLM 5.2, and 61.0 for GLM 5.3; Terminal Bench v2.1 scores are 80.1, 81.0, and 88.2. The table labels total and active parameters and prospective licenses, but does not establish equal effort, tools, or serving cost. Reflection reports these results; public Beam weights were still promised for later October.","credit":"Reflection","source":{"title":"Introducing Beam: Reflection's 501B open-weight model","url":"https://reflection.ai/blog/introducing-beam","date":"2026-10-05"}}],"evidenceNote":"The benchmark image was verified. No released Beam technical report, model card, or public weights were linked in the retrieved announcement; those artifacts remain promised for later October, so none is attached as an available document.","companyLabel":"Public edition"},"issue":{"id":"2026-10-12","publishedLabel":"Week of October 12, 2026","isLatest":true},"position":{"index":3,"total":5},"next":{"slug":"ico-agentic-ai-evidence-call","headline":"The UK seeks agent evidence before writing further privacy guidance"},"session":null}