LLM Chatreasoningtools

GLM 5.2

by Z.ai · Released 2026

Zhipu AI's (Z.ai) flagship agentic coding model from the GLM-5 family. A 262K-context model purpose-built for long-horizon software engineering — multi-turn tool calling, native reasoning, and reliable structured output across large codebases.

LLM Chat

GLM 5.2

Powered by Z.ai · General Language Model (GLM), Mixture-of-Experts

Context Window

262K

Parameters

MoE

Max Output

8K

Category

LLM Chat

Overview

GLM 5.2 is Zhipu AI's (Z.ai) flagship agentic coding model, the most capable entry in the GLM-5 family. It pairs a very large 262,144-token context window with native reasoning and robust multi-turn function calling, making it well-suited for autonomous coding agents that plan changes across many files, call tools to read and edit code, run tests, and iterate — all while keeping the full project context in a single window.

The model is tuned for agentic coding workflows: it follows tool-calling instructions precisely, emits reliable structured output for tool payloads, and uses a `reasoning_effort` thinking toggle (low/medium/high) to trade latency for depth on harder problems. Its bilingual Chinese/English heritage from the GLM family carries through, so it remains strong on multilingual technical content.

On CallMissed, GLM 5.2 is fully OpenAI-compatible on `/v1/chat/completions` with streaming, tool calling, and the reasoning toggle. The 262K context handles large repositories, long design documents, and extended agent transcripts in one pass — pair it with a planner/executor loop for repository-scale refactors, or use it directly for complex single-shot coding and analysis tasks.

Pricing

MetricPrice
Input /1M tokens₹189.0000
Output /1M tokens₹594.0000

1 credit = ₹1 = $0.01 USD. Prices shown from provider; CallMissed passes through with ~35% markup.

Key Highlights

  • Flagship agentic coding model — built for long-horizon engineering
  • 262K context for repository-scale tasks in a single pass
  • Native reasoning with a low/medium/high effort toggle
  • Reliable multi-turn tool calling and structured output

Benchmarks

BenchmarkScore
Context262K
Tool CallingYes
ReasoningYes

Technical Details

  • Architecture: General Language Model (GLM) mixture-of-experts
  • Context window: 262,144 tokens
  • Native reasoning with reasoning_effort control
  • Multi-turn + parallel tool/function calling
  • OpenAI-compatible on the CallMissed gateway with streaming
  • Bilingual Chinese/English strength from the GLM family

Strengths

  • Purpose-built for agentic coding and long-horizon tool use
  • Very large 262K context for whole-codebase reasoning
  • Reasoning toggle balances latency vs depth per request
  • Reliable structured output keeps tool-call loops stable

Limitations

  • Premium pricing relative to the fast GLM 4.7 Flash tier
  • Reasoning mode increases time-to-first-token on hard prompts
  • Coding-optimized — general chat may prefer a cheaper model

Use Cases

Agentic codingRepository-scale refactorsLong-context analysisTool-using agents

API Example

curl https://api.callmissed.com/v1/chat/completions \
  -H "Authorization: Bearer cm_YOUR_KEY" \
  -d '{"model": "glm-5.2", "messages": [{"role": "user", "content": "Refactor this module and add tests"}]}'

Endpoint: POST /v1/chat/completions · Model ID: glm-5.2

Try GLM 5.2 now

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