GPT-6.1 Sol vs Reflection Beam: The 3–4× Efficiency Claim, and the Benchmark Tables That Undercut It

The most interesting number in Reflection Beam's launch material is not a benchmark score. Reflection's announcement of 2026-10-05 says Beam runs at roughly three to four times fewer inference FLOPs than GLM 5.2 at comparable quality — an efficiency claim, made against a specific named rival, in a market where per-token cost is the constraint most teams actually operate under. It is also a claim that the same announcement's own benchmark tables complicate rather than support: in those tables Reflection Beam beats GLM 5.2 on several suites and trails GLM 5.3, Kimi K3 and others on DeepSWE, SWE-Bench Pro v2-Hard, SWE Atlas Codebase QnA, MCP Atlas, AutomationBench, BrowseComp and DeepSearchQA. Set that against GPT-6.1 Sol, Open​AI's release of 2026-09-29, and you get a comparison between a model whose efficiency is the argument and a model whose efficiency is already priced, measured and published.

Both models are 2026 frontier-tier releases from October and late September respectively, and the difference in how much can be said about each of them is the substance of this article. Everything below comes from Reflection's launch material, Open​AI's model and pricing pages, our own catalogue records and independent measurements from Artificial Analysis, read on 2026-10-12. Where a figure is the vendor's own and nobody has reproduced it, the sentence carrying it says so.

What Reflection Beam is, as far as anyone outside Reflection knows

Beam is a 501-billion-parameter mixture-of-experts model with 23 billion parameters active per token. That ratio is the design decision worth noticing before anything else: a 501B/23B MoE is built to be cheap to serve, because only a small fraction of the network runs for any given token. The efficiency claim in the announcement is not a surprise bolted onto the architecture — it is the architecture's stated purpose.

• Total and active parameters — 501B total, 23B active per token, mixture-of-experts.

• Modality — text only. No vision, no audio, no image input.

• Context — 256,000 tokens in the beta API, with a 128,000-token maximum output and a knowledge cutoff of 2026-06-30.

• Weights — not released. Reflection has said the weights will be published under Apache 2.0 later in October, which is the single most consequential open question about this model and the reason the launch is a preview in practice.

• Price — none published. There is no rate card for Beam's API, so no cost figure for it appears anywhere in this article, and any comparison built on per-token arithmetic has to leave its column empty.

• Independent benchmarks — none. Artificial Analysis has no row for Reflection Beam, so every score in the section below is Reflection's own run, with Reflection's own baselines attached.

The vendor tables, read honestly

Reflection published a set of benchmark comparisons alongside the launch, and the honest way to report them is to say what they show in both directions, because they do not show one direction.

• Where Beam wins. On the suites Reflection chose to headline, Beam outperforms GLM 5.2 — the model the efficiency claim is made against. Taken at face value, that is a genuine result: better output at roughly a quarter to a third of the inference cost is a strong position for anyone serving at volume.

• Where Beam loses. The same tables put Beam behind GLM 5.3, Kimi K3 and other current models on DeepSWE, SWE-Bench Pro v2-Hard, SWE Atlas Codebase QnA, MCP Atlas, AutomationBench, BrowseComp and DeepSearchQA. Those are not peripheral suites; they are the software-engineering, tool-use and deep-research benchmarks that determine whether a model is usable as an autonomous agent.

Read together, the tables say something coherent and narrower than "state of the art": Beam appears to be a strong open-model release that beats the previous generation on Reflection's chosen ground and does not beat the current generation on the agentic ground. That is a legitimate thing for a model to be. It is not the same thing as being the best available model for agentic work, and the announcement's framing invites the stronger reading.

One structural point about the efficiency number itself, since it is the headline. A FLOPs-per-token comparison against another model is a property of the architecture and the serving configuration, not of outcome quality — and 3–4× fewer FLOPs is a vendor-measured figure with no independent reproduction. It is a reasonable reason to expect Beam to be cheap to serve. It is not evidence that Beam is cheap to use, and the difference between those two things is the entire cost discussion on this page: a model that spends more tokens to reach an answer can be cheaper per FLOP and more expensive per task at the same time.

The shipped side of the comparison

Here is the asymmetry this article exists to document. GPT-6.1 Sol went live on Open​AI's API on 2026-09-29. It has a published rate card — $2.00 per million input tokens, $10.00 per million output, $0.10 cached input, $2.50 cache writes — a documented 1,050,000-token context window, a 128,000-token maximum output, and an independent Intelligence Index row of 52 at maximum reasoning effort with a cost-to-run of $0.72 per task.

That cost figure is the one worth setting against Reflection's FLOPs claim. $0.72 per task is not a FLOP count and not a rate card; it is what the work costs when it is finished, measured by a third party that ran the model. It is the number that would have to be beaten for Beam's efficiency argument to land as a procurement argument — and Beam has no equivalent figure, because Beam has no published price and no independent run.

Two other differences are concrete and checkable today:

• Context and output — GPT-6.1 Sol documents 1,050,000 tokens of context against Beam's 256,000 in beta, and both cap output at 128,000 tokens. For repository-scale or document-set work that is a four-fold difference in how much material fits in one call.

• Modality — GPT-6.1 Sol accepts text, images and files; Beam is text-only. If your pipeline reads screenshots, scans or diagrams, Beam is not a candidate regardless of its scores.

Where Beam has an argument that GPT-6.1 Sol structurally cannot answer, it is the open-weights one. If Apache 2.0 weights arrive on the promised schedule, a 501B/23B model whose active parameter count is 23B becomes a self-hosting candidate in a way that a proprietary model never will be. Twenty-three billion active parameters is a number a well-equipped team can reason about; the 256,000-token context in beta and the absence of published weights are why that argument has to wait for the actual release rather than being settled at launch.

Who should act on which model, now

The practical answer differs depending on what you are buying, and the split is clean enough to state directly.

If you need a model in production this week for agentic coding, tool use or long-context document work, the shipped model is GPT-6.1 Sol and the decision is about its price, not about its existence. Its output rate is the meter that matters, its cached input rate is unusually low at $0.10 per million, and above 272,000 input tokens the whole request reprices upward — a threshold worth designing around before you scale a loop that reads a large prefix. It is in our catalogue at Open​AI's list rates with 0% markup, so the rate on the card is the rate on the invoice, and a vendor price change appears in the catalogue the same day rather than after a reseller's update cycle: https://www.orcarouter.ai/models/openai/gpt-6.1-sol.

If your interest in Beam is the open-weights release rather than the beta API, the thing to watch is the Apache 2.0 publication Reflection has promised for later this month. Until the weights exist, every capability number in this article is the vendor's, the price is unknown, and neither a self-hosting plan nor a cost comparison can be finished. If it ships on schedule, Beam becomes immediately interesting as the efficient tier in a two-model setup — and that is a configuration one endpoint with automatic failover across 200-plus models handles without a second integration, which is the argument for trying an unproven model on a routing layer instead of committing a production path to it.

Reflection Beam is not something we serve. When its weights and its API open up properly it will be, like most open releases, on the vendor's own platform and several third-party services — and this page will be worth rewriting then, because the two things it currently cannot say, what Beam costs and how it scores when someone other than its maker runs it, are exactly the two things the answer depends on.