GLM 5.2

GIGAZINE's July 7, 2026 coverage of Tencent's Hy3 release positions GLM-5.2 as a benchmark baseline alongside DeepSeek-V4-Pro, reporting GLM-5.2 at roughly 753B total parameters and showing Hy3 (295B total / 21B active with a 3.8B MTP layer) achieving parity with GLM-5.2 across several tasks. Hy3 is also reported to su The article is centered on Tencent Hy3 rather than GLM-5.2, is machine-translated from Japanese (with the publisher noting possible translation issues), and cites a vendor-produced benchmark table rather than independent reproductions, so the 753B parameter count and the parity/efficiency claims should be treated as th

I added GLM 5.2 to my security bug hunting benchmark when it came out, and found it to be a good performer, but not the best open model. The ...

On June 17, 2026, Z.ai published the GLM 5.2 benchmark scorecard that was withheld at the June 13 launch, alongside MIT-licensed open weights for both zai-org/GLM-5.2 and zai-org/GLM-5.2-FP8 on HuggingFace — arriving earlier than the originally promised "the following week" timeline. GLM 5.2 posts 62.1 on SWE-bench Pro The article frames GLM 5.2 as the first credibly open-weight model to lead an Anthropic or OpenAI flagship on a real-world SWE-bench Pro head-to-head, noting that teams using Claude Code, the Claude Agent SDK, Cursor, or the Vercel AI Gateway now have a frontier-tier open-weight backend they can self-host. It also reca

ThursdAI's June 2026 monthly roundup lists GLM-5.2 as one of 32 AI releases covered that month, attributing it to Z.ai (Zhipu AI) and categorizing it for developers and coding agents. The aggregator reports concrete model-intrinsic specs: a 753-billion-parameter open Mixture-of-Experts architecture and a 1M-token conte As a secondary podcast/newsletter aggregator, ThursdAI corroborates Z.ai authorship and the 753B open-MoE / 1M-context framing, but its single-source GPQA Diamond figure and other benchmark numbers are not independently verified against a Z.ai release post or model card within this candidate set. The roundup neverthele

Z.ai (formerly Zhipu AI / THUDM) released GLM-5.2 on June 13, 2026, as a 744-billion-parameter open-weight Mixture-of-Experts model under the MIT license, activating roughly 40B parameters per token across 384 experts and supporting a 1,000,000-token context window with a 131,072-token maximum output. The article docum Technically, GLM-5.2 introduces IndexShare sparse attention, which the source cites as achieving a 2.9x FLOP reduction at the full 1M context length, along with an improved Multi-Token Prediction speculative decoding path. The model uses a higher activation ratio than Kimi K2.7, and the article walks through local depl

Simon Willison reports that Z.ai released GLM-5.2 first to its coding plan subscribers on June 13, 2026, and then open-sourced the full weights on June 16, 2026 under an MIT license. The model is a 753B-parameter, 1.51TB Mixture-of-Experts design with 40B active parameters, and extends context length to 1 million token On accessibility, Willison confirms GLM-5.2 is available on OpenRouter through nine providers, most of which price it at $1.40 per million input and $4.40 per million output tokens—dramatically undercutting GPT-5.5 ($5/$30) and Claude Opus 4.5-4.8 ($5/$25) at frontier-tier intelligence. He also surfaces the Artificial

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Z.ai's GLM-5.2 has become the leading open-weights model on the Artificial Analysis Intelligence Index v4.1, scoring 51 and placing ahead of MiniMax-M3 and DeepSeek V4 Pro (both 44) and Kimi K2.6 (43). The release retains the same 744B total / 40B active parameter Mixture-of-Experts architecture as GLM-5.1 but delivers Beyond the headline intelligence score, GLM-5.2 posts a leading 1524 on GDPval-AA v2—placing it ahead of MiniMax-M3 (1418) and DeepSeek V4 Pro max (1328) and in line with proprietary GPT-5.5 (xhigh reasoning)—while also landing on the Pareto frontier of intelligence versus cost per task at $0.46 per task. The trade-off

Z.ai launched GLM-5.2 according to MarkTechPost coverage dated June 14, 2026, with three developer-facing details highlighted: a usable 1M-token context window, two selectable thinking-effort levels, and notably no public benchmarks disclosed at launch. The 'no benchmarks at launch' framing is itself a notable product For developers evaluating the model through the Lilac inference endpoint, the headline items translate into concrete capability changes: long-context workloads up to 1M tokens become feasible, and cost/latency can be tuned by switching between two reasoning modes without swapping models. The absence of published benchm

The U.S. National Institute of Standards and Technology's Center for AI Standards and Innovation (CAISI) published an independent assessment of Z.ai's open-weight GLM-5.2 model on July 8, 2026, roughly three weeks after its June 16, 2026 release. CAISI concluded that GLM-5.2 was probably the most capable open-weight AI On safeguards and security, CAISI found mixed results: GLM-5.2's safeguards permitted assistance with agentic cyber exploit development and blocked fewer sensitive biological questions than reference U.S. models, but it appeared potentially more robust than other evaluated PRC open-weight models against agent-hijacking

A Hacker News discussion (916 points, ~80 days old) centered on Artificial Analysis's leaderboard post declaring GLM-5.2 the new leading open-weights model. Commenters provide concrete behavioral observations: GLM 5.2 supports reasoning-effort tiers ("high" and "xhigh," with xhigh mapped to max effort), and at xhigh it The thread frames GLM 5.2 as a significant step up for open weights and getting close to frontier, while flagging reasoning efficiency as the next bottleneck relative to GPT 5.5. It also reinforces Z.ai's open-weights positioning by emphasizing the cost gap (GLM 5.2 expected to undercut Opus 4.8 and GPT 5.5 on price ev