GLM-5.2: The New Leading Open-Weights LLM in 2026

TL;DR: GLM-5.2, released by Z.ai in June 2026, is the highest-scoring open-weights LLM on the Artificial Analysis Intelligence Index, at 51 points. That puts it ahead of DeepSeek V4 Pro and MiniMax-M3 (both 44) and Kimi K2.6 (43). It ships under a permissive MIT license with a 1-million-token context window, and on the GDPval economic-task benchmark it scores effectively level with GPT-5.5 — bringing near-frontier capability to a model you can self-host.

Key Takeaways

• GLM-5.2 scores 51 on the Artificial Analysis Intelligence Index (v4.1) — an 11-point jump over GLM-5.1 and the highest of any open-weights model as of June 2026.
• It is a sparse Mixture-of-Experts (MoE) model: roughly 744B total parameters but only 40B active per token, so per-token inference compute is closer to a 40B dense model than to a 700B-class one.
• The MIT license plus a 1M-token context window (5x GLM-5.1's 200K) make it the first openly licensed model to seriously rival closed frontier systems on agentic and scientific reasoning.
• The largest gains over GLM-5.1 are in agentic and scientific tasks: TerminalBench v2.1 +16 (to 78%), CritPt +16 (to 21%), τ³-bench banking +15 (to 27%), and Humanity's Last Exam +12 (to 40%).
• On GDPval-AA v2 (graded real-world economic tasks) GLM-5.2 scores 1524, effectively level with GPT-5.5's 1514 and far ahead of other open models.
• API pricing is $1.40 / $4.40 per million input/output tokens, but the model is verbose — it spends ~43K tokens on a hard reasoning task — so cost-per-task (~$0.46) matters more than the sticker rate.

What Is GLM-5.2 and Why Does It Matter?

GLM-5.2 is the latest model in the General Language Model (GLM) family from Z.ai. It landed in June 2026 and immediately became the top-ranked open-weights model on the Artificial Analysis Intelligence Index, a composite benchmark that aggregates reasoning, coding, science, and agentic evaluations into a single comparable score.

Why this is a bigger deal than a routine point release: for most of the past two years, the frontier of capability belonged to closed, API-only models, while open-weights releases trailed by a generation. GLM-5.2 narrows that gap to near zero. It posts an Intelligence Index of 51 while remaining downloadable under an MIT license — meaning you can run it on your own hardware, fine-tune it, and ship it inside a commercial product without per-token licensing fees or data leaving your network.

That combination — frontier-adjacent quality, permissive license, and self-hostability — is what makes GLM-5.2 worth a close look for any team weighing the build-versus-rent decision for LLM infrastructure. If you have previously concluded that open models "aren't good enough" for your hardest workloads, GLM-5.2 is the release that forces a recheck of that assumption.

How Does GLM-5.2 Rank Against Other Open-Weights Models?

The headline number is the Artificial Analysis Intelligence Index (v4.1), a normalized 0–100 composite. GLM-5.2 sits clearly at the top of the open field:

A 7-point lead over the next-best open models (MiniMax-M3 and DeepSeek V4 Pro, both at 44) is substantial on this scale — the Index compresses a wide spread of task performance into single digits, so multi-point gaps usually reflect a visible difference in real use. GLM-5.2 also improved 11 points over its own predecessor, GLM-5.1, in a single generation, which is an unusually steep jump for an established model line.

The practical takeaway: if your selection criterion is "the most capable model I can run under a permissive license," GLM-5.2 is currently the answer, with DeepSeek V4 Pro as the strongest alternative — particularly if you value DeepSeek's track record and tooling ecosystem. For a broader look at how open models slot into agent stacks, see our AI Agent Frameworks guide.

What Do the Individual Benchmarks Reveal?

The composite Index hides where a model actually improved. Breaking GLM-5.2 down against GLM-5.1 shows the gains are concentrated in agentic execution and hard science — exactly the areas where open models have historically been weakest:

Two patterns stand out. First, GPQA Diamond is near saturation at 89% — most current frontier models cluster in the high 80s here, so there is little headroom left and the small +3 gain is expected. Second, the double-digit jumps on TerminalBench (+16) and τ³-bench (+15) are the meaningful story: these are agentic benchmarks that require the model to plan, call tools, and recover from errors across many turns. Open models have traditionally collapsed on long agentic chains, so a 78% TerminalBench score is what makes GLM-5.2 viable as the engine inside a coding agent rather than just a chat model.

The CritPt result (21%, up from 5%) is worth a caveat in the other direction: a 21% score on frontier physics problems is an improvement, but it is still a low absolute number. GLM-5.2 is dramatically better than its predecessor at the hardest scientific reasoning — and still far from solving it.

How Does GLM-5.2 Compare to Closed Models?

Open-vs-open is one question; open-vs-closed is the one most teams actually care about. Artificial Analysis runs GDPval-AA v2, which grades models on real economic tasks drawn from actual professional work rather than academic puzzles. Here GLM-5.2 is genuinely surprising:

GLM-5.2 at 1524 is effectively level with GPT-5.5 at 1514 — the gap is within noise. An open-weights model trading blows with a closed flagship on graded economic tasks is the headline most teams should internalize: the "open models are a generation behind" heuristic no longer holds for this class of work.

The honest framing: GLM-5.2 matches GPT-5.5 on this benchmark, not on every axis. Closed models still tend to lead on the very long tail of reasoning difficulty, multimodal breadth, and the polish of their managed tooling, safety layers, and SLAs. But for the large middle of real workloads — drafting, analysis, coding, structured extraction, agentic task completion — GLM-5.2 removes "the open option isn't good enough" as a blocking objection.

What's the Architecture Behind GLM-5.2?

GLM-5.2 is a sparse Mixture-of-Experts model. The key numbers:

The split between total and active parameters is the whole point of MoE. The model stores the knowledge of a ~744B-parameter network, but for any given token it only activates about 40B of those parameters. That means:

• Inference compute (FLOPs) per token scales with the 40B active count, not the 744B total — so GLM-5.2 generates tokens roughly as fast as a 40B dense model, not a 700B one.
• Memory footprint scales with the 744B total, because all experts must be resident to be routed to. This is the constraint that dominates self-hosting (see the cost section below).

Z.ai also expanded the context window to 1M tokens, a 5x increase over GLM-5.1's 200K. Critically, the model's AA-LCR (long-context reasoning) score rose to 71%, which suggests the larger window is usable for actual reasoning over long inputs rather than just nominally accepted. Long advertised context windows often degrade sharply in real retrieval — for the engineering discipline of actually keeping a model coherent across a large window, see Context Engineering for AI Agents.

How Much Does GLM-5.2 Cost to Run?

There are two cost models: rent it through an API, or self-host the weights.

Hosted API pricing (per 1M tokens):

The sticker price looks cheap next to closed flagships, but there is a catch that every cost model must account for: GLM-5.2 is verbose on hard problems. Artificial Analysis measured it spending roughly 43K tokens to complete a single Intelligence Index task (about 37K of that on reasoning), up from 26K on GLM-5.1. At a measured cost of ~$0.46 per task, the reasoning token volume — not the per-token rate — is the dominant cost driver on difficult work.

The practical implication: for cheap, high-volume, latency-sensitive calls, a smaller model is still the right tool (we compare those tradeoffs in the Gemini Flash vs Claude vs GPT speed-tier breakdown). Reserve GLM-5.2 for the hard tasks where its reasoning depth earns back the token spend.

Self-hosting economics: Because all ~744B parameters must be resident in memory, the weights alone occupy roughly 750 GB in 8-bit precision — on the order of ten H100-80GB GPUs — plus additional memory for the KV cache, which is significant at 1M-token context. Quantizing to 4-bit roughly halves the weight footprint to ~375 GB (about five H100s) at some quality cost. The upside is that the 40B active-parameter count keeps throughput high once the model is loaded, so a correctly provisioned cluster serves tokens quickly. Self-hosting only pencils out at high, sustained utilization; below that, the hosted API is almost always cheaper. This is the same build-vs-rent calculus we walk through for smaller models in Local AI Coding Agents: Small Models vs Cloud.

How Do You Run GLM-5.2?

GLM-5.2 is served by Z.ai's first-party API and by several third-party inference providers — including DeepInfra, Novita, Fireworks, Baseten, Nebius, Parasail, SiliconFlow, and GMI Cloud. Most expose an OpenAI-compatible endpoint, so you can use the standard OpenAI SDK and just swap the base URL and model id:

For self-hosting, an inference server such as vLLM can serve the weights with tensor parallelism across multiple GPUs. A minimal launch looks like this:

Because GLM-5.2 is verbose, two production defaults pay off immediately: (1) cap max_tokens on tasks where you do not need extended reasoning, and (2) lean on prompt caching — the $0.26/M cache-hit rate is a ~5.4x discount over fresh input tokens, which matters a lot for agentic loops that resend a large, stable system prompt on every turn.

When Should You Choose GLM-5.2 Over a Closed Model?

GLM-5.2 is the right default when one or more of these is true:

• Data residency or privacy is non-negotiable. Self-hosting means prompts and outputs never leave your infrastructure — decisive for regulated industries, sensitive codebases, or air-gapped environments.
• You want to fine-tune or modify the model. The MIT license permits adaptation and commercial redistribution; closed APIs do not give you the weights.
• You are building agentic systems. The +16 TerminalBench and +15 τ³-bench gains make it credible as the engine inside a tool-using agent, not just a chatbot.
• You need long-context reasoning at a predictable cost. A usable 1M-token window plus self-hostable economics beats per-token API billing for document-heavy or repository-scale workloads.

Stick with a closed flagship when you need the absolute top of multimodal capability, the broadest managed safety/compliance tooling, or a turnkey SLA without operating any inference infrastructure. And for high-volume, latency-critical, low-complexity calls, a dedicated speed-tier model remains more cost-effective than routing everything through a heavyweight reasoner.

What Are the Limitations and Caveats?

No model is free of sharp edges, and GLM-5.2 has a few worth naming:

• Hallucination is still material. On the AA-Omniscience evaluation, GLM-5.2 posts ~25.1% accuracy with a 28.1% hallucination rate and a 47% attempt rate. It improved over GLM-5.1 (29.4% hallucination), but it still confidently produces wrong answers on a meaningful fraction of knowledge questions — retrieval grounding and verification remain mandatory for factual workloads.
• Verbosity is a real cost. The ~43K tokens spent per hard task is great for transparency of reasoning but expensive at scale; budget for it and constrain output where you can.
• Self-hosting is heavy. The ~744B total parameter count means meaningful GPU capital or rental even though only 40B activate per token. Small teams will rent before they host.
• Benchmarks are a snapshot, not a guarantee. A 51 Intelligence Index reflects a specific evaluation suite at a specific date. Always validate on your own representative tasks before committing — leaderboard position rarely transfers perfectly to a particular production workload.

FAQ

Is GLM-5.2 really better than DeepSeek V4 Pro?

On the Artificial Analysis Intelligence Index, yes — GLM-5.2 scores 51 versus DeepSeek V4 Pro's 44, and it leads more decisively on the GDPval-AA v2 economic-task benchmark (1524 vs 1328). DeepSeek V4 Pro remains a strong, MIT-licensed alternative with a larger 1.6T-parameter MoE and an established ecosystem, so the gap is about measured benchmark performance, not a knockout. Validate both on your own tasks before standardizing on one.

Can I run GLM-5.2 on my own hardware?

Yes — it is released under an MIT license, so you can download, run, fine-tune, and ship it commercially. The practical constraint is memory: at ~744B total parameters, the weights need roughly 750 GB of VRAM in 8-bit (about ten H100-80GB GPUs), or ~375 GB in 4-bit. The 40B active-parameter design keeps generation fast once loaded, but the resident-memory requirement means most teams will rent it through a hosted provider until their utilization justifies a dedicated cluster.

How does GLM-5.2 compare to closed models like GPT-5.5?

On the GDPval-AA v2 benchmark of graded real-world economic tasks, GLM-5.2 scores 1524 — effectively level with GPT-5.5's 1514. That makes it the first open-weights model to match a closed flagship on this measure. Closed models still tend to lead on the hardest reasoning, multimodal breadth, and managed tooling, but for the large middle of production workloads GLM-5.2 closes the historical open-vs-closed quality gap.

What is GLM-5.2's context window, and is it usable?

GLM-5.2 supports a 1-million-token context window, a 5x increase over GLM-5.1's 200K. Unlike many large windows that degrade in practice, its long-context reasoning score (AA-LCR) rose to 71%, indicating the window is usable for actual reasoning over long inputs rather than nominal acceptance. As always, test retrieval accuracy on your own long documents, since real-world performance depends on how information is distributed across the context.