Compare the listed model data for GLM-5 by Zhipu AI and GLM-4.5 by Zhipu AI. Benchmark and pricing records are not a controlled test and may change.
Data last updated: 2026-10-16. Verify current model details with each provider.
What the listed data says
GLM-5: 128K tokens; GLM-4.5: 128K tokens. The listed context-window values are equal. GLM-5 is listed for coding, reasoning, agentic workflows, long-context tasks; GLM-4.5 is listed for coding, reasoning, agentic workflows, long-context tasks. Shared catalog benchmark entries: MMLU: GLM-5 88.0%; GLM-4.5 85.0%; LiveCodeBench: GLM-5 60.0%; GLM-4.5 55.0%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
Catalog source links
Use these model and provider links to verify current specifications, pricing, and availability.
The listed context windows are 128K tokens and 128K tokens; the larger is 1× the smaller.
On shared numeric benchmark entries, MMLU: GLM-5 − GLM-4.5 = +3 percentage points (88.0% vs 85.0%); LiveCodeBench: GLM-5 − GLM-4.5 = +5 percentage points (60.0% vs 55.0%).
A release-date gap cannot be calculated from the catalog values (GLM-5: 2025; GLM-4.5: 2025).
Cost example: 1M input and 1M output tokens
Using only the catalog's listed per-million-token rates, 1M input plus 1M output tokens costs about $0.3 for GLM-5 and $0.3 for GLM-4.5. This excludes provider-specific discounts, caching, and other fees.
Choose GLM-5 if…
Choose GLM-5 when your task aligns with catalog-listed uses or strengths such as Chinese enterprise AI requiring GLM-5 quality, Zhipu AI's flagship GLM-5 model, Best bilingual Chinese-English in GLM family, Apache 2.0.
Choose GLM-4.5 if…
Choose GLM-4.5 when your task aligns with catalog-listed uses or strengths such as Bilingual Chinese-English enterprise AI with agentic workflows, GLM-4.5 generation — significant improvement over GLM-4, Strong tool use and agentic capabilities, Apache 2.0.
Figures are transcribed from LITHOVEX model records. Benchmark versions and evaluation setups may differ; pricing and specifications can change. Check each provider's current documentation before making a decision.
Frequently Asked Questions
What is different between GLM-5 and GLM-4.5?
GLM-5: 128K tokens; GLM-4.5: 128K tokens. The listed context-window values are equal. GLM-5 is listed for coding, reasoning, agentic workflows, long-context tasks; GLM-4.5 is listed for coding, reasoning, agentic workflows, long-context tasks. Shared catalog benchmark entries: MMLU: GLM-5 88.0%; GLM-4.5 85.0%; LiveCodeBench: GLM-5 60.0%; GLM-4.5 55.0%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
What benchmark values are listed for GLM-5 and GLM-4.5?
MMLU: GLM-5 88.0%; GLM-4.5 85.0%; LiveCodeBench: GLM-5 60.0%; GLM-4.5 55.0%. The catalog values may use different versions and evaluation setups, so they are reference points rather than a controlled head-to-head result.
Which model has the larger listed context window?
GLM-5: 128K tokens; GLM-4.5: 128K tokens. The listed context-window values are equal.
What are the listed costs per million tokens for GLM-5 and GLM-4.5?
GLM-5: input $0.1, output $0.2 per 1M tokens (Apache 2.0 — free). GLM-4.5: input $0.1, output $0.2 per 1M tokens (Apache 2.0 — free). Where separate numeric input and output rates are available, the worked example uses 1M input plus 1M output tokens. Confirm current terms with each provider.
How far apart are the listed release dates for GLM-5 and GLM-4.5?
The catalog lists GLM-5 as 2025 and GLM-4.5 as 2025; a release-date gap cannot be calculated from both records.
When was the comparison data last updated?
The model catalog records 2026-10-16 as its data update date. Provider specifications and prices may have changed since then.
How should I choose between GLM-5 and GLM-4.5?
Compare the listed specifications and capabilities, review the benchmark notes, then test both with the same representative prompt for your task. This page does not claim a universal winner.