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Model details

GLM-5

GLM-5 is designed for agentic engineering, where coding, planning, and tool use must remain productive across complex, long-running tasks. Its architecture builds on the GLM family’s mixture-of-experts and long-context direction, while the model’s multiple thinking modes let users balance deeper reasoning against response time. This makes it particularly relevant to repository-scale programming and workflows that require an agent to sustain context, make plans, and call external tools.

Z.AI positions GLM-5 as a new-generation foundation model with leading open-source coding and agent performance, while emphasizing practical engineering reliability over isolated benchmark gains. Its open-weight availability supports local experimentation and adaptation, and its reasoning and tool-oriented design suits developers building coding assistants, systems-engineering agents, and other multi-step automation. The model is less suited to lightweight interactions where maximum reasoning depth would add unnecessary delay.

TokenGoz-ai/glm-5glm

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Provider
TokenGo
Model key
z-ai/glm-5
Release date
Feb 12, 2026
Last updated
Feb 12, 2026
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.89
Output token cost
$3.2647

Limits

Output tokens
131,072 tokens
Context window
204,800 tokens

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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CoverageAnalysis

Z.ai shipped GLM-5.3-Flash on August 26, 2026, as a distinct 320B-parameter mixture-of-experts model that activates only 18B parameters per token, runs natively in FP8, and supports a 1,048,576-token (1M) context window. Per the model card, it starts from a newly trained base model with a redesigned architecture and tr The flash variant beats GLM-5.2 across six coding/agentic benchmarks at roughly one-tenth the price, with API pricing set at $0.15 per million input tokens and $0.50 per million output tokens, and the vLLM inference recipe is already public. It approaches Claude Opus 4.8 on long-horizon agent tasks. Notably, the flagsh

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