Open-weight 1M-context MoE model built for repo-level coding agents and long-horizon engineering tasks.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Why GLM 5.2 Holds Up on Long-Horizon Coding
Benchmarks
GLM 5.2 Coding Benchmarks: What Third Parties Confirm
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Comparison
GLM 5.2 vs GLM-5.1: What Changes for Coding Agents
GLM 5.2 is the direct successor to GLM-5.1 and shares its MoE architecture and OpenAI-compatible wire format, so migration is mostly a model-ID swap. Both target coding and long-horizon agentic work. The practical decision is whether the newer generation's larger context and higher coding scores justify moving. GLM 5.2 expands the usable context roughly fivefold to 1M tokens, adds the IndexShare sparse-attention scheme to cut long-context cost, and posts clearly higher coding numbers — notably Terminal-Bench 2.1 jumping from around 62–63 to 81.0, alongside a step up on SWE-bench Pro.
Choose GLM 5.2 when your workload needs whole-repository context in a single pass, long-running agent trajectories, or the strongest available GLM coding performance — repo migrations, cross-file refactors, and multi-platform builds. Choose GLM-5.1 when your tasks fit comfortably within a ~200K window, you have already tuned prompts and cost around it, and you do not need the extra context or the newer coding gains. GLM 5.2's higher verbosity also means more reasoning tokens per task, so 5.1 may remain cheaper for shorter, well-bounded jobs.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Repo-Level Coding Agents
GLM 5.2's core fit. The 1M-token context lets an agent hold an entire codebase — backend, frontend, config, tests, and conventions — while it audits architecture, plans changes, implements across files, and verifies. Z.ai reports it tracks cross-file dependencies in a single pass, enabling whole-repo refactors without repeated retrieval. Strong SWE-bench Pro and Terminal-Bench scores back the coding focus. Still pair generated changes with tests, review, and sandboxing.
Codebase Migration and Refactoring
GLM 5.2 is designed for cross-file, multi-step, long-chain tasks: module decoupling, API migration, directory restructuring, SDK adaptation, and cross-language refactoring. It decomposes the goal, identifies dependencies and risks, then implements and closes work in stages while preserving API contracts and engineering standards. The large context reduces fragmentation across a migration. Budget for its verbosity on long sessions, and gate all changes behind CI and human review.
Long-Horizon Agent Automation
For multi-step automation that needs task decomposition, intermediate verification, and observable progress, GLM 5.2 exposes reasoning-effort control, streaming, tool streaming, and function calling. That combination suits agent systems that must maintain state and standards across long-running trajectories. Set xhigh effort for complex sessions and lower effort for routine steps to manage cost and latency, and validate tool outputs before acting on them.
Long-Document and Repository Analysis
The 1M-token window makes GLM 5.2 usable for inspecting large design documents, technical specifications, logs, or full repositories together, reducing the need for aggressive chunking. This helps architecture audits and cross-document reasoning. Because a large window does not remove hallucination risk, verify extracted facts against source and prefer schema-validated structured outputs for downstream data.