GLM 5.3 vs 5.2: Better at the same price

Z.ai released GLM 5.2 in June 2026 as an open-weight model built for long-horizon coding work. Two months later, it followed up with GLM 5.3. According to TechNode's coverage of the launch, the new version uses the same base model as GLM 5.2, with its gains coming from additional post-training focused on coding, long-horizon tasks, and cybersecurity.

In our Ready for Enterprise (RFE) LLM Benchmark, the difference is larger than a point release usually suggests. GLM 5.3 ranks #9, while GLM 5.2 ranks #23. Both are listed at the same token price, so for most teams already on GLM 5.2, the upgrade costs nothing extra per token.

Here's how the two versions compare on our GLM 5.3 vs 5.2 data, where the older model still holds up, and what the license change means before you switch.

GLM 5.3 vs GLM 5.2: Benchmark comparison

Source: FullStack RFE LLM Benchmark, data as of October 30, 2026. Rankings can change as new models ship, and results are added.

Our RFE score combines six independent benchmarks with price and speed, and the capability score reflects benchmark performance alone. GLM 5.2 hasn't been tested on Terminal-Bench 4.0 yet, so its scores are based on five of the six benchmarks. It also falls below our capability threshold, so it ranks after every model that reaches it. We show SWE-Bench Pro for reference only and don't build any comparisons on it.

What did Z.ai change in GLM 5.3?

Scaled post-training on the same base model

GLM 5.3 isn't built on a new base model. It uses the same base as GLM 5.2, and Z.ai put its effort into post-training instead. "Scaling post-training is all we did for GLM-5.3," the company wrote in its launch post. That scaled post-training added more diverse tasks and long-horizon task environments, including simulated professional work that Z.ai says would take a human engineer several days each to complete.

Z.ai reports large gains from that work on its own tests. On Terminal-Bench 3.0, for example, it reports GLM 5.3 at 28.3, up from 4.6 for GLM 5.2. On Z.ai's internal Code Bench, a private benchmark, it reports GLM 5.3 scoring 31.4% while using about 50,000 output tokens, compared with 29.5% for Claude Opus 4.8 at about 120,000. These are Z.ai's own results, not independent tests, but they suggest GLM 5.3 can be efficient with output tokens on complex coding tasks.

A faster-than-expected jump in cyber capability

Z.ai also trained GLM 5.3 on vulnerability discovery data and environments built for finding software flaws. The company says this cyber capability developed faster than its training schedule predicted, and that the model began to plan multi-step exploitation. Z.ai describes it as an emergent cyber capability that came from scale rather than a specific training goal.

That's why the weights came later than usual. Z.ai held them back for a safety evaluation before releasing them, which is closer to how some closed models are released than to earlier GLM launches. For teams using GLM 5.3 in security work, this is worth knowing in both directions: the model may be more useful for finding vulnerabilities, and it's also more capable in ways that call for care.

How Z.ai measured its results

Z.ai ran its evaluations in the Claude Code harness, with reasoning effort set to maximum and no web tools. For its security tests, the agent ran inside a task container, with network access limited to only essential domains needed for basic tool installation. Time limits on one test were based on API inference time rescaled by each model's measured speed, rather than real clock time, and Z.ai's footnotes name the judge model where one was used.

Those details matter because benchmark results depend heavily on setup. A model run at max effort in a specific harness won't always perform the same way in real-world usage scenarios, at a lower effort level, or with your own tools. Treat vendor-reported scores as a starting point and test on your own tasks.

Where does GLM 5.3 improve on GLM 5.2?

GLM 5.3 leads on four of the five benchmarks both versions were tested on. Its capability score is 55.7, compared with 34.6 for GLM 5.2, and that gap is enough to move it above our capability threshold.

Math shows the biggest gain

The largest jump is on FrontierMath, an advanced math test, where GLM 5.3 scores 68.8% versus 59.2% for GLM 5.2. For work that involves multi-step calculations or quantitative analysis, that's the most noticeable change.

Factual accuracy improves, but stays low

On SimpleQA Verified, which tests whether a model answers short factual questions correctly, GLM 5.3 scores 41.0% against 34.2% for GLM 5.2. That's a real improvement, but both scores are well below the roughly 70% that models like Claude Opus 5.5 and Gemini 3.8 Flash score in our benchmark. If your workload depends on the model recalling facts on its own, test it carefully, or ground its answers in your own documents.

Coding and chat are slightly ahead

GLM 5.3 also leads on Arena WebDev (1623 versus 1606) and Arena Text (1481 versus 1475), though the gaps are small. It scores 41.8% on Terminal-Bench 4.0, which tests agents completing real command-line tasks. GLM 5.2 hasn't been tested there yet, so we can't compare the two on agentic coding from our data.

Where does GLM 5.2 still hold up?

GLM 5.2 scores slightly higher on GPQA Diamond, a graduate-level science test, at 91.9% versus 90.9%. A one-point gap is small enough that you're unlikely to notice it in practice, but it's the one benchmark where the older version comes out ahead.

The other reason to stay on GLM 5.2 is the license, which we cover below.

Does GLM 5.3 cost more than GLM 5.2?

No. The two versions have an equal list price. In our benchmark listing, both versions cost $1.40 per million input tokens and $4.40 per million output tokens, or $2.15 blended using our 3:1 input-to-output ratio. Launch coverage of GLM 5.3's API pricing also reported the rates as unchanged from GLM 5.2.

Both versions also generate output at 80 tokens per second in our measurements, so switching won't change response speed. Since the price per token is the same, the real cost difference comes from how many output tokens each version uses on your tasks. Compare cost and compare latency on a full run of your own workload before you budget for the switch.

Reasoning effort affects both. Thinking is mandatory on GLM 5.3, so there's no thinking-off option, but you can choose an effort level of low, high, or max. If you don't set one, the reasoning_effort setting defaults to max. Max effort gives the model room for deeper reasoning on long-horizon coding, but it also uses more tokens, so a lower effort level may be the better fit for simpler tasks.

If cost matters more than top capability, Z.ai also released GLM 5.3 Flash, a smaller version that costs $0.15 input and $0.50 output per million tokens in our listing, or about $0.24 per million tokens blended. It ranks #15 in our benchmark, but its capability score of 41.3 falls below our threshold, so test it on your own tasks before relying on it.

^{For more on what it costs to run open-weight models yourself, download our guide, Is Running Your Own AI Cheaper?}

What changed in the GLM 5.3 license?

This is the change most likely to affect larger companies. GLM 5.2 was released under the MIT license, which places very few limits on how the model is used. GLM 5.3 uses a new GLM 5.3 License instead.

According to The New Stack, the new license requires companies that host the model, and that have more than $10 billion in revenue over any 12-month period, to pass a security review by Z.ai before using it commercially. The license still allows running, deploying, and fine-tuning the model, and individual users are largely unaffected. GLM 5.3 Flash stayed on the MIT license.

For most teams, the new terms won't change anything. If your company is above that revenue threshold and plans to host GLM 5.3 itself, have your legal team review the license before you upgrade.

The release timing changed too. GLM 5.2’s weights followed within days of its launch. For GLM 5.3, Z.ai launched the API in mid-August 2026 and released the weights on Hugging Face on August 28, after what it described as two additional weeks of safety evaluations.

How do you get access to GLM 5.3?

GLM 5.3 is available through Z.ai's API and the GLM Coding Plan, and it works with third-party tools like Claude Code. Coding Plan users can use it as part of their plan. If you'd rather host it yourself, the weights are on Hugging Face under the GLM 5.3 License.

GLM 5.3 has a maximum context length of 1 million tokens and a maximum generation length of 128,000 tokens. GLM 5.2 also supports a 1 million-token context window, so neither version has a larger context window. That means your context management strategy shouldn't need to change when you upgrade.

Should you upgrade from GLM 5.2 to GLM 5.3?

For most teams, yes. GLM 5.3 scores higher on our benchmark, reaches our capability threshold, and costs the same per token. Here's how to decide:

• Upgrade if you use GLM 5.2 through an API, especially for math or coding work.
• Check the license first if your company has more than $10 billion in annual revenue and hosts the model itself.
• Test before switching if your workload depends on factual recall. GLM 5.3 is better than 5.2 there, but still well behind leading frontier models.
• Check tool behavior in agent workflows. Post-training changes can affect how a model calls tools, so watch for invalid tool calls during testing.

Whichever version you use, keep the model ID in configuration rather than hard-coded. That gives you a rollback path to GLM 5.2 if something doesn't work as expected, and lets you test new versions without rewriting code.

How FullStack can help

A capable open-weight model like GLM 5.3 can be a strong alternative to closed models, and it gives you more control over where your AI runs and how it's trained. We help teams decide when an open-weight model is the right fit, and we build and fine-tune custom models on your own data, deployed in your own environment.

^{Compare these models in the RFE benchmark.}