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Model details

GLM-5.2

GLM-5.2 is Z.ai's flagship model purpose-built for long-horizon tasks, representing a substantial leap over its predecessor GLM-5.1. It ships as a fully open-weights release under an MIT license with no regional access limits, initially appearing to coding-plan subscribers before the full weights became publicly downloadable. The model is structured as a 753-billion-parameter Mixture-of-Experts architecture with around 40 billion active parameters, and it sustains a solid 1-million-token context window designed for stable operation across extended agent trajectories rather than merely accepting very long inputs.

Z.ai highlights several technical improvements in GLM-5.2, including an architectural feature called IndexShare that reuses the same indexer across every four sparse attention layers to cut per-token compute at long context lengths, alongside refinements to the model's multi-token prediction layer that boost speculative decoding acceptance. The release emphasizes advanced coding capabilities with configurable thinking effort levels so developers can trade latency for performance, and independent coverage notes the model ranks competitively on agentic front-end coding leaderboards. GLM-5.2 is particularly well suited to project-level software engineering, long-running coding agents that must retain engineering context through multi-step workflows, and complex automation pipelines that demand consistent tool use over extended sessions.

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Quick Info

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Provider
Friendli
Model key
zai-org/GLM-5.2
Release date
Jun 13, 2026
Last updated
Jun 13, 2026
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$1.40
Output token cost
$4.40

Limits

Output tokens
131,072 tokens
Context window
1,048,576 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about GLM-5.2

SiliconFlow

CoverageBenchmark

A third-party guide updated September 9, 2026, separates independently verified GLM-5.2 benchmark results from Zhipu's self-reported numbers across categories. GLM-5.2's clearest independently verified result is its #1 finish on Design Arena's Code Categories leaderboard, a blind human-preference test where it ranks ro The guide notes that specific point scores on SWE-bench Pro (62.1), Terminal-Bench 2.1, and FrontierSWE come from Zhipu's own technical report and model card — directionally corroborated by independent evaluators but not reproduced exactly by a third party using Zhipu's precise methodology. The headline framing of 'nea

Together AI

CoverageBenchmark

Z.ai (formerly Zhipu AI) released GLM-5.2 on June 16, 2026, as a 753-billion-parameter open-weights large language model engineered for long-horizon autonomous coding and engineering tasks, according to VentureBeat reporting. The model ships under an MIT open-source license on Hugging Face, is also available via the Z. Architecturally, GLM-5.2 introduces an optimization called IndexShare, which reuses a single indexer across every four sparse attention layers, reducing per-token compute FLOPs by approximately 2.9x at the maximum 1-million-token context length. It also features an upgraded Multi-Token Prediction layer for speculative

Together AI

Coverage

NIST's Center for AI Standards and Innovation published an assessment of Z.ai's GLM-5.2 on July 8, 2026, reporting it was probably the most capable open-weight model at its June 16, 2026 release. CAISI found GLM-5.2's overall capabilities are similar to GPT-5.2 (December 2025) and its cyber capabilities are similar to Anthropic's Opus 4.6 (February 2026). The evaluation also flagged mixed safeguards performance: GLM-5.2 allowed assistance with agentic cyber exploit development, blocked fewer sensitive biological questions than reference U.S. models, but appeared potentially more robust against agent hijacking and jailbreaking than other evaluated PRC open-weight models. The report notes that prompt-based safeguards can still be circumvented when the open-weight model is self-hosted, a key caveat for developers.

SiliconFlow

CoverageBenchmark

An aggregator page presents all 17 published GLM-5.2 benchmark results grouped into reasoning, coding, and agentic categories, explicitly disclaiming that the figures are republished as published by the model authors and that the site does not run the evaluations. The long-horizon trio — FrontierSWE (74.4%, up to 20-ho Generation-over-generation deltas versus GLM-5.1 show uneven gains: +3.7 on SWE-bench Pro (62.1 vs 58.4), +17.5 on Terminal-Bench 2.1 (81.0 vs 63.5), +28.2 (2.6×) on DeepSWE (46.2 vs 18.0), +12.8 on ProgramBench (63.7 vs 50.9), +5.0 on MCP-Atlas, +7.5 on Tool-Decathlon (48.2 vs 40.7), and +9.5 on HLE (40.5 vs 31.0). Th

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