Head to head
GLM-5 vs GLM-5.3
GLM-5 from Z.ai (Zhipu) against GLM-5.3 from Z.ai (Zhipu) — specification, price and every benchmark both makers have published, in one table.
At a glance
Benchmarks
No shared benchmarks
These two models have no benchmark in common with published figures for both, so there is nothing to compare directly. The specification and price rows below are still like for like.
Price
Not directly comparable
At least one of these does not have a published per-token price, so cost cannot be compared like for like. The open-weight model can be run on your own hardware instead.
What actually differs
- GLM-5.3 takes 1M tokens of context against 200K — 5.0× more room for long documents or a large codebase.
- Only GLM-5.3 reads images. If your input includes screenshots, charts or documents, that decides it.
Scorecard
Which is better at what
Maths, coding, reasoning and the rest — one line each, averaged over the benchmarks both models actually report.
GLM-5.3 has no published benchmark scores
Its maker has not released figures for any of the evaluations tracked here, so there is nothing to put in a score column. Rather than estimate, infer from a sibling model, or quote the base model's numbers as if they were its own, this page leaves those rows empty and compares what genuinely can be compared: parameters, context window, modalities, licence and cost.
The moment those figures are published they go in — send them with a link to the source.
| Category | GLM-5 | GLM-5.3 | Better at this |
|---|---|---|---|
| ReasoningNeither model reports this | — | — | Not comparable |
| MathsNeither model reports this | — | — | Not comparable |
| CodingOnly one model reports this | — | — | Not comparable |
| KnowledgeNeither model reports this | — | — | Not comparable |
| MultimodalNeither model reports this | — | — | Not comparable |
| Instruction followingNeither model reports this | — | — | Not comparable |
| Human preferenceNeither model reports this | — | — | Not comparable |
| Categories won | No category has a test both models report | ||
Each category averages only the benchmarks every model here reports, so no one is credited for a test the other did not run. A category with no shared test is marked Not comparable rather than guessed at.
Side by side
GLM-5 and GLM-5.3, row by row
| Attribute | GLM-5Z.ai (Zhipu) | GLM-5.3Z.ai (Zhipu) |
|---|---|---|
| Specification | ||
| MakerWho built it | Z.ai (Zhipu) | Z.ai (Zhipu) |
| Released | 2026-02 | 2026-08 |
| ParametersTotal, and active per token for a mixture of experts | 744B total / 40B active | 753B total / 39B active |
| Architecture | MoE | MoE |
| Context windowHow much can go in at once | 200,000 tokens | 1,000,000 tokens |
| Max output | 128,000 tokens | Not reported |
| Input | Text | Text, Image |
| ReasoningSpends extra tokens thinking before it answers | Not reported | Yes |
| Tool calling | Yes | Yes |
| Licence | Not stated | GLM-5.3 License |
| Open weightsCan you download and run it yourself | Yes | Yes |
| Price | ||
| Input priceUSD per million tokens in | Not reported | $1.40 |
| Output priceUSD per million tokens out | Not reported | $4.40 |
| Cached input | Not reported | $0.26 |
| Blended 3:1A 3-in-to-1-out million-token mix — a fairer single number than input price alone | Not reported | $2.15 |
| Price note | First-party API rate. | Open weights — this is a representative hosting rate, not a first-party price. Running it yourself costs only hardware. |
| Published benchmarks | ||
| SWE-bench Verified500 human-validated GitHub issues from real Python repositories. The model must produce a patch that makes the project's own tests pass. | Not reported | |
| Terminal-Bench 2.0The 2.0 task set of the terminal agent benchmark — the version most launches reported from late 2025 to spring 2026. Not comparable with 2.1 or 4.0. | Not reported | |
| Links | Full page | Hugging Face · Full page |
| Row verified | 2026-10 | 2026-09 |
Where these numbers come from
Every score on this page is a published figure, taken from the model's own card, system card, technical report or release post, or from a public leaderboard. CorX Labs did not run these evaluations. Most are self-reported by the lab that built the model, which means they were produced under that lab's own choice of prompt, scaffold and number of attempts — so treat them as a starting point for a shortlist, not as a settled ranking.
A score someone other than the model's maker measured is marked Independent and names its measurer. Those are the stronger numbers on this page — an outside harness has no reason to flatter anyone — and there are not many of them.
Where a figure has not been published, the cell reads Not reported rather than an estimate. Nothing here is inferred, interpolated or guessed. Each model records the month its row was last checked. Full method and caveats.
Questions
GLM-5 or GLM-5.3?
Is GLM-5 better than GLM-5.3?
These two models have no benchmark in common with published figures for both, so there is nothing to compare directly. The specification and price rows below are still like for like.
Which is cheaper, GLM-5 or GLM-5.3?
At least one of these does not have a published per-token price, so cost cannot be compared like for like. The open-weight model can be run on your own hardware instead.
What is the difference between GLM-5 and GLM-5.3?
GLM-5.3 takes 1M tokens of context against 200K — 5.0× more room for long documents or a large codebase. Only GLM-5.3 reads images. If your input includes screenshots, charts or documents, that decides it.
