Muse Wonder vs GLM 5.5: Only One of These Models Has a Rate Card

There is no GLM 5.5. There is no model card, no API identifier, no price row, no Hugging Face repository and no page on Z.ai's own site carrying that name, and there never has been — it is community shorthand that the vendor has not adopted. Meta Muse Wonder, by contrast, shipped on October 12, 2026 with an endpoint, a published token price and two checkpoints, and you can call it today. So a literal comparison of Meta Muse Wonder against GLM 5.5 is a comparison against a rumour. The comparison that can actually inform a decision is Meta Muse Wonder against GLM-5.3, the flagship Z.ai genuinely shipped in August, and that pairing turns out to have a cleaner answer than most head-to-heads: a seven-point intelligence gap against roughly half the cost per completed task.

What exists, and what is only a name

The discipline this piece needs is a hard line between the two.

• Meta Muse Wonder — generally available since October 12, 2026 on Meta's Model API as muse-wonder-1.0. $4.00 per million input tokens, $0.20 cached input, $20.00 per million output. A 1,048,576-token context window. Reasoning always on, with effort levels max through minimal defaulting to medium.

• Meta Muse Wonder Full — a second checkpoint in the same launch, muse-wonder-full-1.0, scoring higher and sold only inside a Muse Code subscription rather than per token.

• GLM-5.3 — generally available since August 2026. Announced August 14, API live around August 18, open weights published August 28. $1.40 per million input tokens, $0.234 cached, $4.40 per million output. A 1,000,000-token window with 128,000 tokens of maximum output. Text in, text out. Reasoning controlled by reasoning_effort at low, high or max, and thinking cannot be disabled.

• GLM 5.5 — nothing. No vendor announcement, no identifier, no weights, no licence, no score.

That last bullet is not pedantry. Search interest in "GLM 5.5" is real, the name circulates in benchmark screenshots and forum threads, and pages that answer the query by printing a spec sheet are inventing the specification. Z.ai's own model organisation tops out at the GLM-5.3 series, including GLM-5.3-Flash and the FlashX tier; GLM-5.4, GLM-5.5 and GLM-6 have all been floated and the company has confirmed none of them.

Where the name came from, and when it might become real

The 5.5 label traces to a date, not a document. An analyst forecast relayed on June 25, 2026 put Z.ai's next flagship in August; that was a watch window rather than a vendor commitment, and August closed with GLM-5.3 shipping instead. Reading Z.ai's actual cadence — roughly one flagship every 54 to 70 days — the same analysts now project the next release into a window running October 8 to November 9, 2026, and most expect it numbered GLM-5.4 rather than GLM-5.5. Today is October 13 and that window is open, which is the most concrete thing anyone can say about the name.

If it does arrive, the things to check are the ones that decide whether it is this article's opponent:

• An API identifier on Z.ai's own platform, not just a leaderboard entry

• A published per-million-token price on both the input and output lines

• A context window and a maximum output figure

• A Hugging Face repository or an explicit statement that there will not be one

• A dated Artificial Analysis card, since aggregator listings lag vendor releases by days

Until three of those five exist, "GLM 5.5" is a search term and GLM-5.3 is a model.

The real matchup, on the numbers that decide it

Set the two shipping models side by side and the shape is a straight trade rather than a surprise. Note that the benchmark and cost figures below are Artificial Analysis's own, run on its harness at max effort, not either vendor's material.

• Intelligence Index — Muse Wonder (max) 52; GLM-5.3 (max) 45

• Cost per Intelligence Index task — Muse Wonder about $4.52; GLM-5.3 about $2.01

• Input price — Muse Wonder $4.00 per million; GLM-5.3 $1.40, so GLM-5.3 is roughly a third of the price

• Output price — Muse Wonder $20.00 per million; GLM-5.3 $4.40, so GLM-5.3 is under a quarter of the price

• Output tokens burned on the index run — Muse Wonder about 130M; GLM-5.3 about 210M, against a board median near 140M for its comparison class

• Speed on the same run — Muse Wonder is not documented by the evaluator in this pairing; GLM-5.3 is measured at about 66 tokens per second, described as faster than average

• Weights — GLM-5.3's are published under Z.ai's own licence; Muse Wonder's are not published at all

• Input surface — Muse Wonder lists text, image, speech and video; GLM-5.3 is text-only

• Context — 1,048,576 tokens on Muse Wonder against 1,000,000 on GLM-5.3, which is a rounding difference in practice

Two things stand out. The first is that GLM-5.3 is the cheaper model on both the rate card and the completed-task bill — about 2.25 times cheaper per task — which is unusual in this class and worth checking rather than assuming: the open-weight labs have been closing the token-efficiency gap, and at 210M output tokens against Muse Wonder's 130M, GLM-5.3 is the more talkative of the two yet still lands at less than half the cost, purely because its rate card is so far below Meta's.

The second is what the seven-point gap costs you in the other direction. On a composite spanning a field whose median sits near 27, 45 and 52 are different classes of model, and the difference shows up first on the hardest long-horizon work: multi-step agentic tasks, sustained tool use, and the professional-task benchmarks where Meta's own table shows its two checkpoints spreading 44 points apart. If your task is one where the model either completes the job or does not, seven points is not a rounding difference and the cheaper model is a false economy. If your task is one where both models succeed and you are paying for volume — classification, extraction, summarisation, first-pass triage — the price gap is 2.25x and the index gap is invisible.

Open weights change the question entirely

GLM-5.3's weights landed on August 28 under Z.ai's own licence, and that is the one axis on this table where Muse Wonder cannot compete on any terms. Muse Wonder is a proprietary model with exactly one commercial route — Meta's own API and Muse Code — and the higher-scoring Full checkpoint is not sold per token at all.

Published weights mean a checkpoint you can audit, quantise, fine-tune, self-host or freeze at a version you control. They do not mean a locally runnable model: a frontier-scale MoE checkpoint is a datacentre artefact, not something that fits on a workstation, and the licence terms decide what you may then do with it. But for a team whose compliance posture forbids sending data to a third-party endpoint, the choice between these two models is not close — one of them has an exit.

Text-only is the price of that. If your workload involves screenshots, video or audio, GLM-5.3 is out of the running before price enters the conversation, and Muse Wonder's advertised speech and video input is the capability that does the work.

Running the cheap-first pattern without betting the pipeline on it

The practical answer for most teams reading a 2.25x cost gap next to a seven-point score gap is not to pick one but to sequence them, and that is a routing problem rather than a model problem.

GLM-5.3 is on OrcaRouter at Z.ai's own list rate — $1.40 per million input and $4.40 per million output, with cache reads at $0.234, the full million-token window and reasoning effort selectable per request. It carries no markup from us, so a Z.ai price change appears here the same day it is announced rather than at the next contract renewal. Muse Wonder is not on our catalogue and we will not pretend otherwise; if you want it, Meta's Model API is the route.

The pattern the gap invites is a cheap-first cascade: send the volume traffic to GLM-5.3, escalate to a frontier model only when the cheap pass is not confident, and let the routing DSL compose that as one call rather than three integrations in your own code. Automatic failover matters more than usual in that shape, because the whole design depends on the escalation path being available when the first pass stalls — a provider outage on either leg turns a cost optimisation into an incident. Muse Spark 1.2, Meta's multimodal reasoning checkpoint at $1.25 in and $4.25 out, is the other cheap leg worth wiring if your escalations are multimodal rather than text.

What to do this week

If you are deciding between these two models rather than between the models in this headline: pick GLM-5.3 when the work is high-volume, text-only, and both models would succeed; pick Muse Wonder when the work is multimodal or when a seven-point index gap is the difference between a completed job and a failed one; pick GLM-5.3's weights over either if self-hosting or auditability is a hard requirement.

If what brought you here was the name GLM 5.5, the honest instruction is to wait for the identifier. Watch for the October 8 to November 9 window, and judge the arrival on whether a price row and an API model ID appear on Z.ai's own surfaces. A leaderboard screenshot is not a release, and until there is a rate card there is nothing to compare — which is a shorter and more useful answer than a spec sheet somebody typed from memory.