Z.ai Chat Mode in 2026: How It Works, When to Use It & vs Agent Mode

/chat mode in Z.ai is a basic interface for quick, interactive conversations with the GLM-5 model. It provides instant responses without additional overhead from tools or planning.

Spoiler: Chat is lightweight completions with history support, system prompt, and streaming, ideal for RAG, chatbots, and text generation, unlike Agent mode with autonomous tool-use.

📚 Article Contents

• 📌 What happens inside chat mode (LLM inference pipeline)
• 📌 Context and history management
• 📌 Support for system instructions
• 📌 Limitations of chat mode
• 📌 Use case examples (chatbot, RAG, text generation)
• 📌 Comparison with ChatGPT and Claude Chat
• ❓ Frequently Asked Questions (FAQ)
• ✅ Conclusions

Detailed overview of the Z.ai platform (architecture, Chat vs Agent): here

🎯 What happens inside chat-mode (LLM inference pipeline)

Chat-mode acts as a lightweight interface for quickly getting responses: the model operates in direct decode mode without the overhead of agent logic or tool orchestration.

Detailed breakdown of the inference pipeline in chat mode (using GLM-5 as an example):

1. Input data preparation: the client sends an array of messages (role: system/user/assistant). The system prompt (if present) becomes the first element. The entire conversation history is included in the request without automatic truncation or summarization.
2. Tokenization and context formation: the tokenizer converts text into a sequence of tokens (BPE-like). The context is limited to 200,000 tokens (GLM-5); if exceeded, the client must manually truncate the history. The model receives the full context without prior compression.
3. Model processing:

   • MoE layers: 256 experts, top-8 activation (~40B active parameters per token, sparsity ~5.9%).
   • DeepSeek Sparse Attention (DSA): replaces classical attention, dynamically allocates attention only to relevant tokens, reducing computational complexity from O(n²) to closer to linear on long sequences.

4. Thinking mode (if enabled): the parameter thinking: {"type": "enabled"} activates interleaved thinking — the model generates internal thoughts between decode tokens. This improves quality on complex queries but increases the number of generated tokens and latency.
5. Generation: autoregressive decode using specified parameters (temperature, top_p, max_tokens, etc.). Both full response and streaming (stream: true) are supported — tokens are sent to the client as they are generated.
6. Completion: the response is returned as a message with role: "assistant". There is no automatic continuation or verification — the process ends after generation.

Official /chat/completions documentation |

Thinking mode in chat mode

Pipeline differences from Agent mode

In Agent mode, after each decode step, the model can:

• Decide to call a tool (tool_calls)
• Perform planning and self-check
• Iterate (plan → execute → observe → revise)

In chat mode, there is no such cycle — the response is generated in a single pass (single forward pass + decode), without an external loop or orchestration.

Technical trade-offs

The simplicity of the pipeline provides:

• Minimal latency for the first response (~0.5–2 seconds for short queries)
• Low resource consumption compared to Agent mode
• Predictable behavior without unexpected tool calls

At the same time, this limits capabilities: the model cannot independently correct errors, verify facts via tools, or perform multi-step tasks.

Conclusion: The chat mode inference pipeline is a classic one-time completions process with MoE + DSA and optional interleaved thinking, optimized for speed and simplicity in interactive dialogues without elements of autonomous agent logic.

Context and history management

The multi-turn mechanism in chat mode is a simple transmission of the full history in each request, without built-in state management or automatic memory management on the server.

Detailed description of context handling in chat mode (using GLM-5 as an example):

• Context formation: client code (e.g., via an OpenAI-compatible SDK) passes an array of messages in the request to /v4/chat/completions. Message order is important: system (first, if present), then alternating user/assistant from the beginning of the session. The model does not store state between requests — each request is independent and contains all necessary history.
• Model-side processing: GLM-5 receives the full context (up to 200,000 tokens) and passes it through MoE layers with DeepSeek Sparse Attention (DSA). DSA ensures stable attention quality even at maximum length without significant needle-in-haystack degradation. Context is not cached by the server in basic chat mode (unlike some specialized endpoints or future API updates).
• Length limitation: if the total number of tokens in messages exceeds 200,000, the request is rejected with an error (usually 400 Bad Request or 413 Payload Too Large). The model does not truncate context automatically — this is the client's responsibility.
• Client-side history management: for long sessions, it is necessary to:

  • Delete the oldest messages when approaching the limit
  • Use summarization of previous history (e.g., ask the model to condense the previous 10 messages into 1–2 paragraphs)
  • Apply context caching if the API supports it (cached input costs $0.2/million, but in basic chat mode, this is not always available without additional settings)

Official /chat/completions documentation (messages and context) |

Context caching in Z.ai API

Practical implications and trade-offs

Advantages of the approach:

• Full transparency — the client knows exactly what the model sees
• Absence of unexpected history truncations by the server
• DSA efficiency on long contexts without quality loss

Disadvantages:

• Increased token costs and latency with long history (each request reprocesses the entire context)
• Need for manual client-side management, complicating implementation for simple applications
• Lack of automatic state management (unlike some chat platforms with built-in session cache)

In production scenarios for long sessions, it is recommended to:

• Periodically compress history via a separate request to the model
• Use vector databases for RAG instead of full context
• Switch to Agent mode or specialized endpoints with caching for complex multi-turn interactions

Conclusion: Context management in Z.ai's chat mode is based on transmitting the full history in each request, which ensures predictability but requires active length control from the client side and does not provide for automatic caching or truncation on the server.

Support for system instructions

The system instruction is the only stable element of the context that does not change when new messages are added, ensuring consistent model behavior throughout multi-turn interaction.

Technical implementation in Z.ai API (OpenAI-compatible /v4/chat/completions):

• The system prompt is passed as the first element of the messages array: {"role": "system", "content": "..."}.
• It is included in the context once at the beginning and remains unchanged for all subsequent messages in that session (the client should not resend it in each request unless they want to change the instruction).
• If the system prompt is absent — the model uses the default GLM-5 behavior (usually a neutral, helpful assistant without specialization).
• The system prompt is processed as a regular part of the context: tokenization → passing through MoE + DSA → influence on attention and decode. Its length is accounted for in the overall 200K token limit.
• Changing the system prompt is only possible with a new request containing a new messages array (i.e., by restarting the session or explicit replacement).

Official messages and system role documentation

Practical aspects and examples

The system prompt allows for precise model configuration for a specific task. Typical use scenarios:

• Stylistic constraints: {"role": "system", "content": "Respond only in Ukrainian. Avoid profanity and politics."}
• Role specialization: {"role": "system", "content": "You are a senior backend developer with 15 years of experience. Analyze code critically, point out potential vulnerabilities, and suggest optimizations."}
• Response format: {"role": "system", "content": "Respond in a structured manner: 1. Brief conclusion 2. Step-by-step analysis 3. Recommendations. Use markdown."}
• Hallucination limitation: {"role": "system", "content": "If you are unsure of a fact, say 'I don't have enough data' instead of fabricating."}

Limitations and trade-offs

• The system prompt is fixed throughout the session — for dynamic behavior changes, a new request with a new prompt is needed (or using multiple system messages, which is not recommended).
• A long system prompt reduces the available space for conversation history (counted in the 200K limit).
• The model may "forget" or ignore part of the prompt with very long contexts (DSA helps, but does not completely eliminate the problem).
• There is no built-in multi-system prompt or conditional instructions — everything is limited to a single content line.

In production recommendations: keep the system prompt concise (200–500 tokens) to maximize context usage for history. For complex scenarios with dynamic instructions, it's better to switch to Agent mode or use several separate sessions.

Conclusion: Support for system instructions in Z.ai's chat mode is a stable and predictable mechanism for configuring model behavior via role: "system", which is maintained throughout the session and applied to the entire context without re-sending.

Limitations of chat mode

Chat mode implements direct inference without an external loop, thus excluding any forms of autonomous task execution, which differentiates it from Agent mode.

Key technical and functional limitations of chat mode (using GLM-5 as an example):

• Lack of automatic tool-calling: the model cannot independently decide to call functions or tools. If asked to describe a tool, it will provide a text description but will not form tool_calls or execute it. To use tools, they must be explicitly passed and the response handled by the client (manual cycle).
• One-time generation nature: after providing a response, the process ends. There is no built-in iteration mechanism (plan → execute → observe → revise). The model does not analyze its own response, verify facts, or correct errors without a new user request.
• Absence of final artifact generation: the response is limited to text or code in the content field. There is no native creation of files (.docx, .pdf, .xlsx), websites, or other deliverables — this is exclusively the prerogative of Agent mode with built-in skills.
• Limited effect of thinking mode: interleaved thinking improves quality on complex queries but does not provide full agent autonomy. Thoughts remain internal and do not lead to iterative execution or self-check. Latency increases (adding 20–50% tokens and time), but the result is still a single response.
• Speed on long context: when approaching 200K tokens, generation speed drops due to compute overhead of MoE + DSA (from 25–30 tokens/s on short queries to 10–15 tokens/s on maximum context). Streaming helps with first-token latency but does not eliminate the problem of slow completion.
• Lack of built-in state management: the session is not stored on the server — each request is independent. The client must pass the entire history themselves, which increases token costs and the risk of exceeding the limit.

Consequences of limitations in real-world scenarios

These limitations manifest in tasks requiring:

• Autonomous verification and correction (e.g., bug-fixing with code execution — impossible without a manual cycle)
• Multi-step execution with tools (search → analysis → report generation — requires manual processing)
• Generation of final products (report in .docx, table in .xlsx — only text description)
• Long-term interaction without intervention (self-correction in long sessions — absent)

To overcome these limitations, you need to:

• Switch to Agent mode for autonomy and deliverables
• Implement your own client-side cycle (e.g., LangChain / LlamaIndex with tool-calling)
• Use history summarization to save context

Section conclusion: Z.ai's chat mode is limited to one-time direct inference without mechanisms for autonomy, iterative execution, or artifact generation. It is effective for quick dialogues and generation but unsuitable for tasks requiring planning, verification, or final products — Agent mode is designed for this.

Use case examples (chatbot, RAG, text generation)

Chat mode is optimized for tasks requiring high response speed and context support, where the model acts as a direct text generator rather than an autonomous task executor.

Main use scenarios for chat mode in Z.ai (GLM-5, 2026):

• Interactive chatbots (tech support, customer service, internal assistants): supporting conversation context over several turns, quick answers to user questions. For example: handling typical queries like "how to reset password", "check order status" taking into account previous client messages. Advantage — low latency and ability to store history up to 200K tokens.
• RAG pipelines (Retrieval-Augmented Generation): searching for relevant document fragments → inserting into context → generating a response. Chat mode allows processing large volumes of extracted information (up to 200K tokens) without the need for tool-calling. Example: corporate knowledge base search, legal documents, technical documentation — the model synthesizes a response based on the extract without additional verification cycles.
• Text and content generation: creating articles, social media posts, email newsletters, translations, product descriptions, role-playing games. The system prompt allows setting style, tone, and constraints (e.g., "write in Ukrainian, without marketing phrases"). Suitable for one-time generations or short iterations with user clarifications.
• Code generation and analysis (rapid prototyping, code review): explaining code snippets, generating functions/scripts, suggesting optimizations, debugging. For example: "explain this code" or "write a function for parsing CSV with 200K lines". Advantage — 200K context allows loading large code files without chunking.
• Brainstorming, concept explanation, learning: step-by-step analysis of complex topics (mathematics, programming, business logic), idea generation, role-playing simulations. Thinking mode (interleaved) improves quality on queries requiring deep analysis, without transitioning to a full agent cycle.

When chat mode is the optimal choice

Scenarios where chat mode outperforms Agent mode:

• First response needed in <2 seconds (low latency without planning)
• Query is simple or medium complexity, does not require external tools
• Session context is important but does not exceed 200K tokens
• Budget is limited — chat mode consumes fewer tokens than Agent with thinking and tool-calls
• Final artifacts (files, websites) are not needed — text/code is sufficient

In production recommendations: combine chat mode with RAG (vector search + context insertion) or use it as a "first stage" before transitioning to Agent mode for complex tasks.

Conclusion: Z.ai's chat mode is effective for fast interactive tasks with context support and text/code generation: chatbots, RAG, content generation, code review, brainstorming. It is optimal where a one-time response is sufficient without autonomous execution or artifact generation.

Comparison with ChatGPT and Claude Chat

Z.ai's chat mode offers competitive capabilities in terms of price and long context, but it falls short in speed, multimodality, and deep understanding of ambiguous tasks.

Comparative table of key characteristics (as of 2026, thinking mode enabled where available):

Analysis of key differences

Context and long sessions: Z.ai and Claude have an advantage in 200K+ context, which is critical for RAG, analyzing large documents, or codebases. ChatGPT in basic tiers is limited to 128K, although higher plans extend to 200K+.

Speed and latency: ChatGPT leads due to optimized inference. GLM-5 is slower due to its MoE architecture and thinking mode, making it less suitable for ultra-real-time chats.

Reasoning and quality: Claude Opus 4.5/4.6 maintains an advantage in nuanced, ambiguous tasks and situational awareness. GLM-5 is strong in technical reasoning and coding but may be less "intuitive" in creative or ethical scenarios.

Multimodality: ChatGPT and Claude have native vision/audio support; GLM-5 is limited to document generation and requires separate models for images/video.

Cost and accessibility: Z.ai has a significant advantage in API pricing, making it attractive for high-load or long-term applications.

Conclusion: Z.ai's chat mode (GLM-5) is competitive in terms of price and long context but falls short of Claude Chat in deep reasoning quality and ChatGPT in speed and native multimodality. The choice depends on task priorities: budget + context → Z.ai; speed + multimodality → ChatGPT; maximum reasoning accuracy → Claude.

❓ Frequently Asked Questions (FAQ)

What is the difference between chat and agent in Z.ai?

Chat mode provides quick responses without using tools and without automatic planning — it's direct inference for interactive dialogues, text, or code generation. Agent mode supports autonomous planning, tool-calling, multi-turn iterations with self-correction, and the generation of final artifacts (e.g., .docx, .pdf, .xlsx files, reports, websites). Agent mode transforms the model into a task executor, while chat mode is a classic chat interface without agent logic.

Does chat mode support thinking mode?

Yes, thinking mode is supported in chat mode. It can be enabled with the parameter thinking: {"type": "enabled"} (often activated by default in GLM-5). This is interleaved thinking — the model generates internal thoughts between decode tokens, which improves reasoning quality on complex queries. However, this increases latency and token consumption (by 20–50%), but it does not transform the mode into a full agent cycle.

What is the maximum context length in chat mode?

The maximum context length in chat mode is 200,000 tokens (officially stated for GLM-5). In individual tests (e.g., HLE w/Tools), values up to 202,752 tokens have been confirmed. The client must independently manage the message history in the messages array to avoid exceeding the limit — the model does not perform automatic truncation or summarization of the context. Exceeding the limit results in a request error.

✅ Conclusions

• 🔹 Z.ai's chat mode is a basic interface for fast inference with the GLM-5 model, implementing a standard completions pipeline without additional planning cycles or tool orchestration.
• 🔹 Key characteristics: context window up to 200,000 tokens, system prompt support for behavior customization, multi-turn history via a messages array, streaming responses, and optional interleaved thinking mode.
• 🔹 Advantages of the mode: low latency for the first response, efficiency on long contexts due to DSA, minimal resource consumption compared to Agent mode, predictable behavior without unexpected tool calls.
• 🔹 Limitations: absence of automatic tool-calling, iterative execution, self-correction, and final artifact generation; context management entirely on the client side; reduced generation speed on maximum context (~17–19 tokens/s in thinking mode).
• 🔹 Best application scenarios: interactive chatbots, RAG pipelines with large documents, text/code generation, quick analysis and explanation, brainstorming, where a one-time response is sufficient without autonomous task execution.

Main idea: Z.ai's chat mode is suitable for tasks where the priority is response speed, long context support, and simplicity of interaction without the need for autonomous planning, tool calls, or final product generation. For complex multi-step tasks with deliverables and self-correction, Agent mode should be used.