GPT-6 Astra vs GLM-5.5: One Side of This Comparison Cannot Be Tested

On October 6, 2026, a third-party technology site published a report whose headline asserted that GLM-5.5 beats Mythos 5.1 and GPT-6 Astra. That claim is the reason this page exists, and it is worth being precise about what it is: a headline about two things that are not the same kind of thing. GPT-6 Astra is a shipped model — O​penAI released it on September 3, 2026, it carries the identifier gpt-6-astra, it has four published meters, a documented 1,050,000-token context window, and a standing on the Artificial Analysis Intelligence Index that anyone can read. GLM-5.5 is a version number that has never appeared on a Z.ai release log, in a model card, in a rate card, or in any repository the lab owns. As of October 15, 2026 there is no artefact behind it to benchmark, call, or price.

So the honest form of this comparison is not a head-to-head. It is a description of what each side can support as evidence, and of the concrete, checkable things that would have to appear before the matchup became real. That turns out to be more useful than a spec sheet would have been, because the pattern — a leaked version number acquiring a benchmark claim it cannot carry — is the one readers keep running into in this product line.

What GLM-5.5 is, in one paragraph

It is a name inside a lineage that skipped it. Z.ai's release log runs GLM-5.3 on August 18, 2026 and GLM-5.3-Flash on August 26; the company's documentation lists those two plus GLM-5.2 and stops. The 5.4 and 5.5 slots were assumed upward from an older analyst projection, and the company's own August 31 earnings call named GLM-6.0 as the next-generation base — bypassing both numbers. The full nine-surface audit, with the URLs and the method so it can be re-run, is on our reference page for the name. What matters here is narrower: a leaked number is not a specification, and a benchmark claim attached to a leaked number cannot be checked by anyone, including the people who published it.

Two models, two kinds of evidence

Put the two sides against the same dimensions and the asymmetry stops being rhetorical:

• Rate card — GPT-6 Astra $10.00 in, $1.00 cached, $12.50 cache writes, $50.00 out per 1M vs GLM-5.5 nothing published, because there is no pricing page to publish on

• Long-prompt band — GPT-6 Astra repriced at $20.00 / $75.00 above 272,000 input tokens vs GLM-5.5 no terms to state

• Context window — GPT-6 Astra 1,050,000 tokens in, 128,000 out vs GLM-5.5 unstated

• Input modality — GPT-6 Astra text, image and file in, text out vs GLM-5.5 unstated

• Identifier — GPT-6 Astra gpt-6-astra, resolvable on O​penAI's documentation and on our catalogue vs GLM-5.5 no identifier on any vendor surface

• Independent score — GPT-6 Astra 53 on the Artificial Analysis Intelligence Index, 44.6 output tokens per second, $3.26 per completed index task vs GLM-5.5 no entry, and no way to produce one

• Benchmark claim — GPT-6 Astra a vendor table (98% FrontierMath Tier 4, 99.9% ARC-AGI-3, 100% ExploitBench, 57.9% Terminal-Bench 4.0), each figure attributable to O​penAI and each caveated below vs GLM-5.5 a comparative assertion with no figure, no harness and no model

• Weights — GPT-6 Astra closed, API-only vs GLM-5.5 none to release

The part of the Astra record that survives scrutiny, and the part that does not

Because one side of this page has a full evidence base and the other has none, the more valuable thing this article can do is say precisely how much of the Astra record is load-bearing.

The independent half is solid. Artificial Analysis measures GPT-6 Astra at 53 on its current Intelligence Index over ten evaluations, seventh of the 216 models on that board, at $3.26 per completed index task with roughly 60M output tokens generated across the run — more concise than the board median of about 85M. It is measured at 44.6 output tokens per second, which places it 168th of 185 on speed, and that is not a rounding artefact: for an agent that makes many sequential calls, throughput is a cost line. Those figures come from an evaluator that publishes its methodology, and they are the numbers a reader should plan against.

The vendor half needs its labels attached. O​penAI reports 98% on FrontierMath Tier 4 and describes the tier as saturated; 99.9% on ARC-AGI-3 with a claim of human action-efficiency parity on 96% of levels; 100% on ExploitBench, with Astra the first O​penAI model to cross the company's Critical cybersecurity threshold; and 57.9% on Terminal-Bench 4.0 against 37.3% for GPT-5.6 Sol. None of those has been independently reproduced. The ARC-AGI-3 number is the one that most needs its footnote, and ARC Prize supplied it: under the standard harness, where the model may carry forward only notes it writes for itself, Astra scored 62.7%; the 99.9% was measured under a provider adapter that preserves reasoning state between requests. Both columns were published. The 37-point spread is the size of the harness, and anyone quoting 99.9% against a rival's standard-harness figure is comparing two different experiments.

Apply that discipline to the October 6 claim and it fails at the first step. There is no harness to name, no score to compare, no configuration to hold constant, and no model identifier to pin. "Beats GPT-6 Astra" is not a claim that can be true or false yet; it is a claim waiting for a subject.

The GLM you can actually run against Astra

If the question behind the search is "should I use a GLM or an O​penAI flagship," the name in the report is not the name in the answer. Z.ai's shipping text flagship is GLM-5.3, released August 18, 2026 with weights on Hugging Face from August 25 — a 753-billion-parameter mixture-of-experts model with roughly 40B parameters active per token, a 1,000,000-token context window, a 128,000-token output ceiling, and text in and text out only. Thinking is always on; depth is set with reasoning_effort. It carries a custom licence rather than an open one, which is a real constraint if you intend to build on the weights rather than call the API.

Against GPT-6 Astra the shape of the trade is a price-and-verbosity trade, and the second half of it is the half nobody quotes:

• Price — GPT-6 Astra $10.00 / $50.00 per 1M vs GLM-5.3 $1.40 / $4.40 on Z.ai's own rate card, which reads $1.26 / $3.96 on our catalogue

• Independent index — GPT-6 Astra 53 vs GLM-5.3 45, both on the current Artificial Analysis scale

• Cost per completed index task — GPT-6 Astra $3.26 vs GLM-5.3 $2.01

• Output tokens on that run — GPT-6 Astra 60M vs GLM-5.3 210M, against a board median near 85M

• Throughput — GPT-6 Astra 44.6 tokens per second vs GLM-5.3 58.2

That fourth line is the one that changes decisions. GLM-5.3's rate card is roughly seven times cheaper than Astra's on input and eleven times cheaper on output, and it still costs $2.01 to finish an index task against Astra's $3.26 — a gap far narrower than the rate cards suggest. The reason is verbosity: 210M output tokens against Astra's 60M for the same suite. A cheap token multiplied by a talkative model does not stay cheap. This is also why the $1.26 / $3.96 our catalogue shows matters less than it looks: a 10% discount on a meter that the model spends 3.5x as much of is a small effect next to the token-count difference.

One correction that belongs on the record rather than in a footnote, because it affects anyone holding an older number: earlier coverage in this line, including our own August write-up, cited GLM-5.3 at 60 on the Artificial Analysis Intelligence Index. That was the reading on the index version current in August. The same model reads 45 on the scale running today, because the index itself was revised. The number did not fall; the ruler changed. Any comparison that mixes a figure from one index version with a figure from another is not a comparison, which is the same failure mode as quoting Astra's 99.9% ARC-AGI-3 against a standard-harness rival.

Where routing fits

There is a practical test in all of this, and it is the one a catalogue performs. GPT-6 Astra resolves as openai/gpt-6-astra on OrcaRouter and GLM-5.3 resolves as z-ai/glm-5.3, both behind one O​penAI-compatible endpoint at provider list rates passed through with no markup of ours — so a Z.ai rate change and an OpenAI rate change both land on the same key the day they are published, and moving traffic between the two is a change to a model string rather than a second contract. A name that resolves to nothing is a different kind of signal, and the query is cheap to run.

That has a specific use here. The way to settle a GLM-versus-Astra question for your own traffic is to run your own task set against both, and the cost of setting that experiment up is exactly what a shared endpoint removes. It also gives you the cheaper half of the answer without an experiment: Astra's $20.00 / $75.00 band above 272,000 tokens sits precisely where GLM-5.3's flat $1.26 / $3.96 does not, so a long-context workload has a price reason to prefer the GLM before any benchmark is consulted.

No route is claimed for GLM-5.5 because there is nothing to route: it is a name, and a catalogue query for it returns a not-found rather than a model.

What would make this page outdated

The list is short and each item is independently checkable, which is the point. GLM-5.5 becomes real when a model card appears on Z.ai's documentation with an identifier, when a rate card row carries it, when a Hugging Face repository owned by the lab publishes weights under a named licence, when an independent evaluator has a page for it, and when a query against a catalogue returns a model instead of a 404. Any one of those is a strong signal. Until at least one arrives, a report describing how GLM-5.5 performs against GPT-6 Astra is describing a comparison between a product and a projection.

The defensible reading of this matchup today is therefore not a verdict but a division of labour. GPT-6 Astra is the better-evidenced model and the more expensive one, and its weakest ground is exactly where a GLM is strongest: long context, high volume, and work where output verbosity is the dominant cost. GLM-5.3 is the cheaper meter with the slower spend, the independent score is 8 points lower, and it cannot see an image at any price. If a GLM-5.5 does ship, the number to watch first is not its benchmark — it is its output-token count on a fixed suite, because that is the figure that has historically narrowed GLM's price advantage to almost nothing.

A release is also the moment to check what has gone wrong before: the documented AI incidents and agent failures around models in this class are collected in the OrcaRouter AI Incident Archive , where each entry carries its own sources, severity, confidence grade and disputed flag.