Zhipu AI / Z.ai open-weight MoE model (2025): 355B/32B active, 200K context, MIT license. Strong at coding, agentic tasks and reasoning.
Context window
200K
tokens
Parameters
355B (32B aktywnych, MoE)
parameters
Release date
30 September 2025
Access:APIDownloadHostedDeployment:☁ Cloud💻 Local
Overview
Access & deployment
APIDownloadHosted
CloudLocal
Weights: Open weights
Key parameters
📏 Context: 200K
🧩 Parameters: 355B (32B aktywnych, MoE)
✓ Tools · ✓ Fine-tuning
📥 Input: text, structured data, documents
Platforms
Technical specification
Context window
200K
tokens
Parameters
355B (32B aktywnych, MoE)
parameters
License
MIT (otwarte wagi)
Hardware requirements
Open weights — can be self-hosted on GPUs; requires significant resources (a ~355B-parameter MoE model).
Features:✓ Tool use✓ Fine-tuning
Modalities
⬇ Input
textstructured_datadocuments
⬆ Output
textcodestructured_data
Capabilities and applications
Native model capabilities
Advanced reasoning
The ability to perform multi-step, structured reasoning: analysing problems, planning steps, and drawing conclusions from hypotheses. Reasoning-first models (e.g. GPT-5.1 Thinking) dedicate a portion of inference to chains of thought before responding.
Category: reasoning
Extended thinking mode
A reasoning-model variant with a larger inference budget: more thinking cycles, higher answer precision at the cost of response time. Choice between 'standard' and 'extended' thinking is left to the user (e.g. the selector in GPT-5.2 Pro).
Category: reasoning
Adaptive reasoning effort
The model decides how much 'thinking' to allocate to a given query: simple questions are answered quickly, complex problems receive more inference cycles. A GPT-5.1 feature (both Instant and Thinking) that shortens time on easy tasks and extends it for hard ones.
Category: reasoning
Multi-step reasoning
Carrying out multi-step chains of reasoning across long, complex tasks.
Category: reasoning
Mathematical reasoning
The model's ability to solve mathematical tasks requiring multi-step reasoning — equations, proofs, combinatorics, geometry, calculus and competition-level problems.
Category: reasoning
Coding
Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.
Category: coding
Agentic coding
Multi-hour, multi-step programming tasks performed autonomously by the model: cloning a repository, running tests, iterating on fixes, integrating with CLI tools. Characteristic of Codex variants (GPT-5.1-Codex-Mini, Codex-Max).
Category: coding
Agentic capability
The model's ability to autonomously plan and execute multi-step tasks by sequentially using tools, maintaining context, and adapting to intermediate results.
Category: planning
Tool use
The model's ability to call external functions, APIs and tools during a conversation: calculator, search engine, code editor, database. The model decides when and how to use a tool and interprets its result.
Category: planning
Long context
Support for large context windows — tens to hundreds of thousands (or millions) of input tokens. Enables analysis of entire codebases, long documents, and many parallel conversations without losing earlier information. GPT-5.1 supports 400,000 tokens.
Category: language
Prompt caching
Cost-performance optimisation: repeated prompt fragments (e.g. system prompt, long documentation) are cached server-side and cheaper in subsequent calls. Significantly reduces cost for applications with long contexts.
Category: other
Multilingual
Competence in many natural languages (from a few to over a hundred): understanding, generation, translation, and code-switching within a single conversation. Frontier models support a wide range of languages with comparable quality.
Category: language
Application domains
Technical architecture
Core Architecture
Model Form
Training Techniques
Deployment and security
☁ Available on platforms