GPT-6.1 Sol vs GLM-5.5: The Matchup Z.ai Hasn't Shipped, and the Bill You Pay While You Wait

The comparison you searched for has one side missing. GPT-6.1 Sol has been on sale since September 29, 2026, at $2.00 per million input tokens and $10.00 per million output. GLM-5.5 does not exist as a product: Z.ai has published no model card, no API identifier, no price, no weights and no benchmark run under that name, and its own catalogue still tops out at GLM-5.3, released in August. What follows is therefore not a head-to-head between two models. It is the comparison a buyer can actually run today, plus an honest account of what would have to change for the matchup in the title to become real — and what the waiting costs in the meantime.

The facts here come from Z.ai's and OpenAI's published model and pricing documentation, Artificial Analysis, and the public record of Z.ai's announcements, all read on October 27, 2026. The distinction between a vendor claim and an independent measurement is kept in every sentence that carries a number, because the entire GLM-5.5 story so far consists of numbers nobody has verified.

Four dates that have already passed

The GLM-5.5 rumour has a track record, and it is the most useful thing anyone can tell you about it. In late June 2026, a research note reported by Reuters put a next Z.ai flagship in August, with open weights and a trillion-plus parameters. August came and went. Z.ai shipped GLM-5.3 on August 14 and GLM-5.3-Flash on August 26, and never used the 5.5 name in either announcement. The leak cycle then moved the date to September. September passed the same way.

In the last week of October, Chinese-language reports revived the story with a new window — late October to early November — and a new parameter count, somewhere between 2.5 and 3.5 trillion. The count has now been reported as one trillion, 1.6 trillion and 3.5 trillion at different points in the same year, which is what a rumour looks like when it is being amplified rather than sourced. The only next-generation name Z.ai has used in public is not GLM-5.5 at all: on its August 31 earnings call the company referred to GLM-6.0, with no date attached.

None of that means Z.ai is not working on something. It means the name in your search bar is a placeholder, and any page that hands you GLM-5.5 benchmark scores or a GLM-5.5 price is inventing them. There is no primary source to check because there is no primary source.

What Z.ai does sell, and what it costs

GLM-5.3 is a real, shipping, open-weight model. Z.ai released it on August 14, 2026 and opened the API four days later. It is a mixture-of-experts model in the 750-billion-parameter class with roughly 40 billion active parameters — Artificial Analysis lists 753 billion total and 40 billion active — with a one-million-token context window and text-only input. Thinking is mandatory: the thinking.type field accepts only enabled, and passing disabled now fails the request. Depth is controlled by reasoning_effort at low, high or max, defaulting to max.

The list price is $1.40 per million input tokens and $4.40 per million output. On our catalogue it passes through at $1.26 and $3.96, which is the same model at a lower provider rate rather than a discount we are running. Cached input is heavily discounted on both. For a text-only coding workload, that is roughly a third of what GPT-6.1 Sol costs on output, and it is the number that makes the wait expensive.

The comparison you can run tonight: GPT-6.1 Sol against GLM-5.3

This is the matchup that exists. Both models are generally available, both are on our catalogue, and both have an independent score on the same version of the same suite.

• Independent index — GPT-6.1 Sol 52 vs GLM-5.3 45 on Artificial Analysis v4.3.2, a seven-point gap.

• Cost per index task — GPT-6.1 Sol $0.72 vs GLM-5.3 $2.01, so the cheaper model per token is nearly three times more expensive per completed task.

• Output speed — GPT-6.1 Sol 69.2 tokens per second vs GLM-5.3 94.3, a real advantage for the open-weight model.

• Price — $2.00 / $10.00 per million vs $1.40 / $4.40 list, or $1.26 / $3.96 through us.

• Context — 1,050,000 input and 128,000 output for Sol; 1,000,000 for GLM-5.3.

• Input modality — Sol takes text, images and files; GLM-5.3 is text-only.

• Weights — GLM-5.3 is open-weight; Sol is proprietary.

The per-task line is the one that reframes the price sheet. GLM-5.3 is cheaper per token and faster per second, yet Artificial Analysis's cost accounting puts it at $2.01 to complete one index task against Sol's $0.72. The gap comes from volume: the cheaper model writes far more tokens to reach its answer. A team that budgets from the per-token rate and measures nothing else will be surprised by the invoice, and the surprise is larger than the discount.

On vendor-reported capability the two are closer than the index suggests, with one caveat that matters for anyone buying GLM for security work. Z.ai's own evaluation sheet reports Terminal-Bench 3.0 at 28.3, Terminal-Bench 2.1 at 88.2, DeepSWE v1.1 at 66.9 and CyberGym at 84.5 — all vendor runs, none independently reproduced. CyberGym at 84.5 is the number Z.ai leads with, and it is a genuine capability claim about offensive security work. OpenAI's comparative claim for Sol is different in kind: parity with its own GPT-6 Astra on DeepSWE v1.1 at roughly one-fifth the cost, which is a statement about economics rather than a new capability ceiling.

Four tests GLM-5.5 would have to pass

Rather than speculate about a model nobody has seen, it is more useful to write down what would make the title of this page true. Four things, in order of how quickly they would settle it.

• A model identifier in Z.ai's API documentation — a string a caller can put in a request. This is the cheapest signal and the hardest to fake.

• A published price with a cache rate and a context boundary. A launch post without a rate card is an announcement, not a product.

• An Artificial Analysis entry with a configuration, so the score can be compared with Sol's 52 at max effort rather than quoted in isolation.

• Weights on Hugging Face, if Z.ai follows its own pattern. GLM-5.3 shipped with weights after a short safety delay, and that delay is itself a signal worth watching: Z.ai held them back for an evaluation period before publishing them in August.

Until at least two of those land, a "GLM-5.5 vs" page can only compare a shipping model with a rumour. The four tests are also the checklist for spotting a fake benchmark table: a score with no configuration, a price with no cache rate, and a parameter count that changes between posts are the tells.

What waiting costs, in dollars

Suppose your workload is 30 million input tokens and 6 million output tokens a month, text-only, no caching. On GPT-6.1 Sol that is $60 of input at $2.00 and $60 of output at $10.00: $120 a month. On GLM-5.3 at our pass-through rates it is $37.80 of input at $1.26 and $23.76 of output at $3.96: $61.56. The difference is $58.44 a month, or just under half the bill.

Now suppose you wait for GLM-5.5 before deciding, on the theory that the next model will be better and cheaper. Every month of waiting on Sol instead of GLM-5.3 costs that $58.44. Four months is $233.76. If the wait stretches to the next plausible Z.ai release window and then slips the way the August and September windows did, the accumulated premium can exceed the cost of simply running both models for a quarter and measuring which one your workload actually prefers.

That is the argument against waiting on a rumour: the option you are holding has a monthly carrying cost, and the thing you are waiting for has no shipping date. The argument for waiting is real but narrower — if your work depends specifically on a capability GLM-5.3 lacks, and you believe the next Z.ai model will supply it, then the premium is a bet with a defined maximum loss and an undefined payoff. That is a legitimate bet. It should be made with the number in front of you.

Holding both options open without a migration

The practical way to run this decision is to stop treating it as a commitment. GPT-6.1 Sol and GLM-5.3 are both on OrcaRouter, on one key at 0% markup over each provider's list price — Sol at $2.00 and $10.00, GLM-5.3 at $1.26 and $3.96 — so a comparison costs a model string rather than a second contract and a second billing relationship. That is also the cheapest way to be ready for GLM-5.5: the day Z.ai publishes an identifier and a price, the switch is a routing change, not a re-platforming.

Two features do specific work here rather than generic ones. The routing DSL can split traffic by workload — send the high-volume, text-only batch to GLM-5.3 and the multimodal, long-context calls to Sol — without duplicating the client. Automatic failover covers the case that matters most during a model transition: a provider incident on the model you just moved to, which is exactly when a team discovers it has no second path.

What would change this page

Three triggers, and each one is checkable without trusting a blog. A glm-5.5 identifier appearing in Z.ai's API reference would make the matchup real and this page obsolete in its current form. An Artificial Analysis entry with a published configuration would give the comparison a shared measurement. A rate card would settle the only question that matters to a buyer, which is whether the next model is cheaper per task rather than cheaper per token.

Until then, the honest answer to "GPT-6.1 Sol or GLM-5.5" is that one of those is a product with a price and an index score, and the other is a name that has missed two predicted launch windows. The model worth benchmarking against Sol today is GLM-5.3, and it is cheap enough that the test costs less than the debate.