GLM-5.2
GLM-5.2 by Z AI: flagship MoE model for long-horizon tasks with solid 1M context, IndexShare attention, flexible thinking effort, and MIT open-source license.
Model Overview
Capabilities, design details, and architectural traits
GLM-5.2 - Built for Long-Horizon Tasks
GLM-5.2 is a flagship MoE model from Z AI designed to sustain quality across extended, multi-step engineering workflows. Its defining idea is that long context must be engineering-usable - not merely accepting more tokens, but maintaining coherent execution across long, messy coding-agent trajectories. The model can carry forward engineering judgments formed earlier in a session into subsequent stages, reducing context fragmentation in prolonged tasks.
| Trait | Detail |
|---|---|
| IndexShare | Every 4 transformer layers share a lightweight indexer |
| Solid 1M context | Trained extensively for coding-agent scenarios at 1M tokens, maintaining quality across large-scale implementation, debugging, and optimization |
| DSA architecture | Dense-Sparse-Alternating network with IndexShare sparse attention |
| Flexible thinking effort | Multiple effort levels including Max let users explicitly balance capability against latency and computational cost |
| MTP with IndexShare and KVShare | Improved speculative decoding draft layer increases acceptance length by up to 20% |
| MIT open-source | No regional limits or technical access borders |
Engineering Judgment Persistence
A core differentiator is that GLM-5.2 does not just read more context - it retains module boundaries, architectural constraints, API contracts, directory structures, and historical decisions throughout long-running tasks. This makes it suited for project-level codebase takeover, long-horizon refactoring, and production-grade standards adherence where consistency across hundreds of rounds of execution matters.
Mobile and Cross-Platform Workflows
The model covers practical mobile engineering end to end, including client-side architecture, streaming messages, long-connection states, and real-device validation using ADB, logcat, and screenshots - extending from code implementation to on-device debugging in a single task.
Benchmark Performance
Independent evaluations · Artificial Analysis
Accuracy & Capability Details
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| Model Details | |||
|---|---|---|---|
| General Info | |||
| Provider | Z AI | Anthropic | Anthropic |
| Release Date | June 16, 2026 | September 22, 2026 | September 28, 2026 |
| Knowledge Cutoff | - | - | Jun 2026 |
| Context & Limits | |||
| Context Window | 1M | 1M | 1M |
| Pricing (per 1M tokens) | |||
| Input Pricing | $1.40 Best Input Pricing | $4 | $2 |
| Output Pricing | $4.40 Best Output Pricing | $20 | $10 |
| Modalities | |||
| Inputs | text | textimagefile | textimagefile |
| Outputs | text | text | text |
| Benchmarks (0-100) | |||
| Intelligence Index | 33.7 | 57.6 Best Intelligence Index | 56.0 |
| Coding Index | 68.8 | - | - |
| Agentic Index | 38.4 | - | - |
Humanity's Last Exam
Extremely difficult logical reasoning and knowledge.
Long Context Reasoning
Logical reasoning over long context windows.
SciCode Benchmark
Scientific coding and mathematical modeling.
Independent evaluation data provided by Artificial Analysis. To view the latest benchmarks and full details, visit their official site.
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