GLM 5.2 API

GLM-5.2 is Z.ai's open-weight flagship language model for long-horizon engineering, coding, and agent workflows. Its 1M-token context window is designed to keep large repositories, specifications, tool results, and execution history available across extended tasks, while flexible reasoning control lets applications trade latency for deeper analysis.

On iCreat, developers can access GLM-5.2 through an OpenAI-compatible Chat Completions API. The integration supports streaming, thinking, and reasoning-effort control, making it straightforward to evaluate the model in the Playground and move the same model ID into an application.

Model Positioning

GLM-5.2 is a text-only mixture-of-experts model aimed at project-scale work rather than short, isolated prompts. It is particularly relevant when a task spans planning, implementation, testing, debugging, and iterative optimization, or when the model must retain a large amount of technical context without repeatedly rebuilding state.

Its differentiator is not context size alone. GLM-5.2 combines a solid 1M-token window with stronger coding, configurable reasoning, interleaved thinking for tool workflows, and an MIT-licensed open-weight release.

Key Capabilities

Solid million-token context. GLM-5.2 can process up to 1M tokens, giving engineering agents room for repositories, issue history, logs, documentation, and prior tool outputs in one working context.

Long-horizon engineering. The model is optimized for multi-stage coding tasks that require planning, implementation, validation, recovery from errors, and delivery rather than one-shot code generation.

Flexible reasoning. Thinking is dynamically enabled by default. Applications can use reasoning_effort to skip thinking or select the model's effective High and Max reasoning levels according to task difficulty.

Tool-aware continuity. Interleaved thinking allows the model to reason between tool calls. Preserving the returned reasoning content helps maintain continuity across multi-step agent loops.

Efficient long-context architecture. IndexShare reuses one sparse-attention indexer across every four transformer layers, reducing indexer computation at long context lengths. An improved multi-token prediction layer also raises speculative-decoding acceptance.

Pricing

Use Cases

Repository-scale refactoring. Analyze dependencies, plan migrations, modify multiple modules, and validate behavior without repeatedly reloading the project context.

Performance engineering and debugging. Work through traces, benchmarks, source code, and test results while iterating on bottlenecks or difficult failures.

Tool-driven coding agents. Build agents that inspect files, run commands, call development tools, evaluate results, and adjust their next action over many steps.

Technical synthesis. Consolidate long specifications, architecture documents, incident records, or research material into implementation plans and decision-ready outputs.

High-volume code review. Review broad changesets for cross-file consistency, security risks, regression paths, and missing tests.

Model Comparison

GLM-5.2 vs Claude Fable 5 and GPT-5.6 Sol

Why Choose GLM-5.2?

Choose GLM-5.2 when a workload needs more than a large prompt limit: it needs to preserve engineering state, reason between tool calls, and continue making useful progress across a long sequence of actions. The combination of 1M context, 128K output, flexible effort, and an open-weight release makes it a practical candidate for repository-scale agents and technical workflows that require deployment flexibility.

For short chat, lightweight extraction, or latency-first classification, a smaller model may be more economical. GLM-5.2 is most valuable when the additional context and reasoning are actively used to reduce state loss, incomplete changes, and repeated work.

Specifications

Architecture

GLM-5.2 uses a sparse mixture-of-experts architecture with DeepSeek Sparse Attention. Its published configuration contains 78 layers and 256 routed experts, with eight routed experts and one shared expert active per token.

For million-token workloads, IndexShare places an indexer at the first of each four-layer group and reuses its selected indices across the group. Z.ai reports that this reduces per-token indexer FLOPs by 2.9× at 1M context. The model also applies IndexShare and KV sharing to its multi-token prediction path, increasing speculative-decoding acceptance length by up to 20%.

Production Notes

• Treat 1M tokens as capacity, not a requirement. Curate context, place stable instructions first, and keep recent task state easy to locate.
• Start with the lowest reasoning setting that passes a representative evaluation set, then raise effort for planning, debugging, or difficult verification.
• When building tool loops, retain the model's returned reasoning content exactly as required by the thinking mode instead of reconstructing it.
• Use streaming for long generations and define stop, retry, tool-timeout, and output-validation behavior before production rollout.
• GLM-5.2 accepts text only. Convert screenshots, diagrams, audio, or video into reliable textual inputs before sending them to this model.

FAQ

Does reasoning_effort: "low" create a separate Low reasoning mode?

No. GLM-5.2 accepts several compatibility values, but low and medium map to its effective High level, while xhigh maps to Max. Use none or minimal when the request should skip thinking, and benchmark High against Max for complex work.

Can GLM-5.2 inspect a UI screenshot while fixing frontend code?

No. GLM-5.2 is a text-input model. Provide the relevant source code, rendered error messages, DOM or accessibility output, and a textual description of the visual defect, or choose an image-capable model when direct visual inspection is required.

What should an agent do with reasoning_content between tool calls?

Return it complete and unmodified with the associated assistant message when the workflow preserves thinking. This allows GLM-5.2 to continue from its prior reasoning state after a tool result; dropping or rewriting it can break continuity.

Will filling the entire 1M-token window always improve the result?

No. A larger context can preserve more project state, but irrelevant files, duplicated logs, and stale instructions can make retrieval and prioritization harder. Use the window for necessary evidence, keep instructions unambiguous, and summarize obsolete execution history.