GLM-5.2 vs Claude Opus | Tech Stackups

Skip to main content<br>GLM-5.2 just came out, and it's another step forward for what open models can do.

Naturally, the internet freaked out. There's a lot of hype around it right now, and it can be hard to tell what the model actually is, how you can use it, and what it can and can't do.

This guide helps you navigate the hype. We'll show you what people are saying, the pros and the cons, then run our own vibe test pitting Claude Opus against GLM-5.2.

What is GLM-5.2

Navigating the hype

Our vibe test

Here's a preview of the two games the models built. Both are browser games written from scratch, with no game engine or 3D rendering library like Three.js. The 3D models are provided by Kenney.

What Opus made​

What GLM-5.2 made​

What is GLM-5.2​

GLM-5.2 is Z.ai's latest flagship model. It's open weights under an MIT license, so you can download it, run it yourself, or call it through Z.ai's API.

It's built for long-horizon tasks, the kind of long, multi-step coding-agent work that runs for hours. It ships with a 1M-token context window and two thinking effort levels, High and Max, that trade speed for capability.

note<br>GLM-5.2 is text-only, not multimodal. It can't read images, so workflows built around screenshots or diagrams still need a model like Claude Opus.

Z.ai positions it roughly between Claude Opus 4.7 and 4.8 at similar token usage. Here's their announcement, if you want to read more:

@Zai_org on X

Pricing and access​

Because it's open weights, GLM-5.2 is cheap. Through an API it costs a fraction of Opus, and you can run it yourself for free if you have the hardware.

Pricing, per 1M tokens (vendor docs):

InputCache readOutputClaude Opus 4.8$5$0.50$25GLM-5.2$1.4$0.26$4.4<br>On output tokens, GLM-5.2 is less than a fifth the price of Opus.

The weights are on Hugging Face and ModelScope under an MIT license, with no regional restrictions. You can serve it locally with frameworks like vLLM, SGLang, or Transformers.

The benchmarks​

Z.ai published these benchmark numbers alongside the release, on its model card.

* = Anthropic self-reported.

BenchmarkGLM-5.2Opus 4.8GPT-5.5Gemini 3.1 ProReasoning HLE40.549.8*41.4*45HLE (w/ tools)54.757.9*52.2*51.4*AIME 202699.295.798.398.2GPQA-Diamond91.293.693.694.3IMOAnswerBench91.083.5–81Coding SWE-bench Pro62.169.258.654.2NL2Repo48.969.750.733.4DeepSWE46.2587010ProgramBench63.771.970.839.5Terminal Bench 2.1 (Terminus-2)81.0858474Terminal Bench 2.1 (best harness)82.778.983.470.7SWE-Marathon13.026.012.04.0Agentic MCP-Atlas (public)76.877.875.369.2Tool-Decathlon48.259.955.648.8<br>An independent run by ArtificialAnalysis broadly agrees:

Intelligence Index v4.1: 51 (leading open-weights; MiniMax-M3 44, DeepSeek V4 Pro 44, Kimi K2.6 43).

TerminalBench v2.1: 78% (vs 81 / 82.7 on the model card — different harness).

Output tokens per task: ~43k (GLM-5.1: 26k).

These benchmarks span three areas: reasoning (hard math and science exams), coding (fixing bugs and building whole projects), and agentic tool use (calling and chaining real tools). For what each one tests, see the benchmark notes at the end.

Navigating the hype​

It can be hard to tell what's real and what isn't online these days. So we compiled a couple of real-world examples to give you the general vibe of what people are saying about GLM-5.2.

"It keeps up with the top closed models"​

This tweet compares GLM-5.2 against Claude Opus 4.8 (high), Claude Fable 5, and GPT-5.5 (high). The video shows each model rendering a 3D scene and building a few assets from scratch.

@OmedVibeCodes on X

The takeaway people draw is that an open model now lands near the best closed models in the world.

But this is also the kind of thing that shades into astroturfing. The constraints aren't clear, and it's not obvious the task really pits the models against each other.

So treat it as a vibe, not a result. It's a basic demo that impresses on sight, with no technical scrutiny required.

A lot of what you'll see online is exactly this.

"This model is insane at design"​

Another common sentiment is that it's strong at user-interface design, on par with the top closed models. This tweet had GLM-5.2 and Opus 4.8 each build a landing page.

@nutlope on X

The two are hard to tell apart. Design is subjective, so have a look yourself.

It also flags the price: the GLM build cost $0.06 against Opus's $0.49, over six times cheaper and faster. That cheap-and-open angle is a big part of why people are hyped.

"It can't read images"​

Not all the talk is positive. This tweet points out that GLM-5.2 can't read an attached image, because it isn't multimodal.

@maria_rcks on X

Models like Claude Opus take images natively, which matters for workflows built around screenshots, diagrams, or design mockups.

We ran our own vibe test​

To cut through the vibes, we ran our own test. We gave Opus 4.8 and GLM-5.2 the same one-shot prompt: build a 3D platformer game from scratch, in raw WebGL, with...