Zhipu AI Flagship

GLM 4.6

GLM 4.6 scales mixture-of-experts intelligence to a 357B-parameter frontier model with a 200K-token context window, thinking-mode tool orchestration, and Claude-level coding performance. This page distills the released summary into actionable guidance for builders, strategists, and researchers.

200K

Token Context

357B

MoE Parameters

15T

Pre-train Tokens

Launch Overview Explore Modules Click to Use GLM 4.6 Free

Signal Snapshot

GLM 4.6 lifts multi-turn coding win rates to 48.6% versus Claude Sonnet 4 and trims token usage by ~15% vs GLM 4.5.

The mixture-of-experts topology improves tool routing, while alignment refinements produce more natural dialogue and creative personas.

What is GLM 4.6

GLM 4.6 is Zhipu AI's flagship large language model built on a Mixture-of-Experts transformer architecture with roughly 357 billion parameters (about 32B active per token). It inherits the GLM 4 lineage yet advances context comprehension to 200K tokens, enabling the ingestion of entire product manuals, codebases, or research corpora in a single session without context loss. The model is bilingual in Chinese and English, expanding to multiple languages and strengthening translation performance.

Beyond the raw scale, GLM 4.6 integrates a "thinking mode" orchestrator that permits internal chain-of-thought reasoning and on-demand tool invocation. When thinking mode is enabled, GLM 4.6 autonomously decides when to trigger web search, calculators, code execution, or other registered tools, turning the model into an agentic problem solver. Streaming token output, JSON-native responses, and function calling let teams embed GLM 4.6 into production workflows without custom scaffolding.

GLM 4.6 Architecture Pillars

  • MoE routing to specialized expert blocks accelerates inference while preserving depth.
  • Intelligent context packing sustains coherence across 200K tokens and returns up to 128K-token completions.
  • Alignment fine-tuning enhances persona consistency for customer support, creative writing, and role-play.

GLM 4.6 Agentic Capabilities

  • Dynamic tool use via thinking mode with fine-grained control per request.
  • Structured outputs (JSON, form schemas) simplify downstream automation.
  • Streaming responses improve UX in chat and agent dashboards.

GLM 4.6 Feature Constellation

The release emphasizes coding proficiency, complex reasoning, efficient token usage, and open deployment flexibility. Each module below distills what changed from GLM 4.5 and how it drives user impact.

Model Weights

GLM 4.6 Context Mastery

A 56% context jump (128K to 200K tokens) lets GLM 4.6 analyze books, legal archives, or repositories at once. Intelligent context management ensures salient instructions and retrieved data persist deep into conversations.

GLM 4.6 Coding Supervision

CC-Bench experiments reveal a 48.6% head-to-head win rate against Claude Sonnet 4 across 74 multi-turn coding tasks. GLM 4.6 crafts polished front-end layouts and reduces iteration loops through better tool usage.

GLM 4.6 Reasoning Orbit

The thinking mode orchestrator enables deliberate chain-of-thought, step decomposition, and tool invocation mid-completion. Researchers can blend web search with reasoning to synthesize fresh insights.

GLM 4.6 Persona Alignment

Enhanced style alignment delivers smoother dialogue, more convincing role-play, and consistent brand tone. Virtual characters stay in persona over multi-turn contexts without rigidity.

GLM 4.6 vs GLM 4.5 vs Claude Sonnet 4 — Key Signals
Signal GLM 4.6 GLM 4.5 Claude Sonnet 4
Context Window 200K tokens input / 128K output 128K tokens input / 64K output Promoted long-context parity
Coding Win Rate (CC-Bench) 48.6% vs Claude Sonnet 4 Trailing GLM 4.6 by double digits 51.4% vs GLM 4.6 (near parity)
Token Efficiency ~15% fewer tokens per task Baseline usage Competitive
Agentic Tool Use Dynamic thinking mode with API control Thinking mode preview Strong, proprietary
Availability Open weights (Hugging Face, ModelScope) API only Closed, API access

How to Use GLM 4.6

Developers can access GLM 4.6 through Zhipu's REST API, supported SDKs, or by deploying the openly licensed weights. The following playbook shows how to enable thinking mode, request streaming responses, and integrate with agent frameworks.

GLM 4.6 API Sequence

  1. 01 Provision an API key at open.bigmodel.cn.
  2. 02 Call POST /v4/chat/completions with "model": "glm-4.6".
  3. 03 Enable tool-aware reasoning via "thinking": {"type": "enabled"}.
  4. 04 Stream responses by setting "stream": true and handling SSE events.
  5. 05 Register JSON schema functions to capture structured tool outputs.

GLM 4.6 Sample Request

{
  "model": "glm-4.6",
  "messages": [
    {"role": "system", "content": "You are a coding agent."},
    {"role": "user", "content": "Refactor this Vue component for accessibility."}
  ],
  "thinking": {"type": "enabled"},
  "stream": true,
  "functions": [
    {
      "name": "run_tests",
      "description": "Execute unit tests",
      "parameters": {"type": "object", "properties": {"suite": {"type": "string"}}}
    }
  ]
}

Swap to Zhipu's Python SDK or OpenAI-compatible clients by changing the base URL and model name.

GLM 4.6 Agent Loop

Use thinking mode to autonomously schedule tool calls in orchestrators like LangChain, LlamaIndex, or custom planners.

GLM 4.6 Coding Stack

Claude Code, Cline, Kilo Code, and Roo Code expose GLM 4.6 as a selectable backend for richer IDE feedback.

GLM 4.6 Local Deploy

Serve the 357B MoE checkpoint with vLLM or SGLang; community 4-bit quantization reports sub-128GB RAM footprints.

GLM 4.6 Benchmark Trajectory

Zhipu AI published benchmark deltas across general reasoning, scientific QA, and software engineering suites. GLM 4.6 closes the gap with Anthropic's Claude family on evaluations like AIME, GPQA, HLE, and SWE-Bench while increasing transparency through open CC-Bench trajectories.

GLM 4.6 Quant Highlights

  • Claude-level scores on eight flagship benchmarks including SWE-Bench and GPQA.
  • Transparent release of CC-Bench prompts, responses, and tool traces for reproducibility.
  • Token economy upgrade brings faster, cheaper sessions compared with GLM 4.5.

GLM 4.6 Use-Case Boosts

  • Product teams deliver multi-stage workflows (analyze, plan, act) in a single conversation.
  • Role-play chatbots maintain tone across long sessions, improving immersion.
  • Researchers leverage search-augmented responses for deep research and synthesis.

GLM 4.6 Ecosystem & Deployment

GLM 4.6 Cloud Access

Access through Zhipu's API platform, the GLM Coding Plan, or routing services such as OpenRouter. Switching from GLM 4.5 is as simple as updating the model identifier. Subscribers receive higher usage limits at lower per-token cost than competing offerings.

Route via OpenRouter

GLM 4.6 Local Pipeline

Weights ship under MIT terms on Hugging Face and ModelScope, empowering enterprises to fine-tune, deploy privately, or experiment with quantization. Guidance includes running with vLLM or SGLang for optimized throughput.

View on ModelScope

GLM 4.6 Key Use Cases

Coding Agents

Power IDE copilots, CI/CD automation, and multi-step debugging with executable tool calls.

Knowledge Workflows

Summarize archives, draft compliance materials, and orchestrate research with long context.

Immersive Dialogue

Maintain tone-perfect virtual hosts for marketing, entertainment, and customer success.

GLM 4.6 FAQ

Ten essential answers covering launch details, deployment tips, and performance guarantees.