GLM-5.3 Explained: Features, Benchmarks, Pricing & Access

TL;DR GLM-5.3 is Z.ai’s newest flagship AI model, released on August 14, 2026, with a sharp focus on software engineering, autonomous agents, long-horizon tasks, and cybersecurity.

Unlike a conventional model upgrade, GLM-5.3 keeps the same underlying base model as GLM-5.2. Z.ai says the major gains come from post-training at scale, using real-world expert workflows rather than simply increasing model size.

The headline results are significant. Z.ai reports a 50% improvement in coding performance over GLM-5.2 on its internal Code Bench, while public benchmark scores include 66.9 on DeepSWE v1.1, up from 46.2 for GLM-5.2, and 28.5 on Agents’ Last Exam (CLI), up from 23.8. On cybersecurity, GLM-5.3 scored 84.5% on CyberGym, narrowly ahead of Anthropic’s Mythos 5 at 83.8%, while ExploitBench increased from 24.4% to 54.4%.

Key Takeaways

• GLM-5.3 uses the identical base model as GLM-5.2; every improvement comes from scaled post-training on long-horizon environments.
• Coding performance jumps dramatically: Terminal-Bench 3.0 from 4.6 → 28.3; DeepSWE v1.1 from 46.2 → 66.9; ~50% better on internal Z.ai Code Bench with higher token efficiency.
• Emergent cyber capabilities: CyberGym 84.5% (SOTA), ExploitBench more than doubles (24.4% → 54.4%). Real-world discovery of 2,436 vulnerabilities (1,097 medium-to-high severity) across 269 projects.
• Specs: Text-only, 1M context, 128K max output tokens, always-on thinking with low/high/max effort levels.
• Availability: Live now on GLM Coding Plan & ZCode; API and MIT-style open weights planned ~2 weeks post-launch after safety review.
• Strong positioning for agentic engineering, long-horizon coding, and defensive security research.
• CometAPI provides easy, discounted access to the broader GLM series (and 500+ models) via a single OpenAI-compatible API—ideal while waiting for native GLM-5.3 endpoints.

What Is GLM-5.3?

GLM-5.3 is Z.ai’s (formerly Zhipu AI) latest flagship large language model, released on August 14, 2026. It belongs to the GLM (General Language Model) family, which has evolved rapidly from earlier ChatGLM versions into a series of powerful open-weight models optimized for coding, reasoning, and agentic workflows.

Unlike a full pre-training overhaul, GLM-5.3 is built on the exact same base model as its predecessor GLM-5.2 (a large Mixture-of-Experts architecture with roughly 743–744 billion total parameters and ~40 billion activated per token). All performance gains come from extreme post-training scaling: dozens of times more long-horizon task environments, greater diversity of environments, and significantly more compute spent on reinforcement learning (RL) and related techniques.

Z.ai describes the goal as moving beyond “vibe coding” toward true agentic engineering—models that can take ownership of substantial, multi-day professional work units rather than just generating isolated code snippets. Training environments now include realistic production scenarios (e.g., diagnosing bottlenecks across ML training stacks, implementing optimizations, running experiments, and delivering measurable end-to-end speedups while preserving correctness).

Key technical stack elements carried over and scaled from GLM-5.2 include:

• IndexShare for efficient long-context processing
• SAO (with compaction) for RL on long-horizon tasks
• slime (open-source asynchronous RL framework) for large-scale training

The model is text-only (input/output modalities: text), supports a solid 1-million-token context window, and allows up to 128K output tokens. Thinking is always enabled, with three effort levels: low, high, and max (default and recommended for coding is max). Disabling thinking is no longer supported.

Core Features of GLM-5.3

• Frontier coding & agentic capabilities: Designed for complex software engineering, terminal operations, multi-step agent tasks, and long-horizon workflows that can span hours or days.
• 1M lossless context: Strong retention for project-level codebases, documentation, and multi-file reasoning.
• Multiple thinking modes: Always-on reasoning with controllable effort (low / high / max) to balance depth vs. latency/cost.
• Streaming, function calling, structured output, and context caching: Production-ready agent tooling support.
• Emergent cybersecurity strengths: Strong white-box vulnerability discovery and growing multi-stage exploitation planning (intended primarily for defensive use).
• Token efficiency gains: Better results with fewer output tokens than GLM-5.2 at equivalent or higher performance levels.
• Open-weights roadmap: Weights scheduled for release ~2 weeks after launch under a permissive license (following the MIT pattern of prior GLM-5.x models), after safety evaluation and hardening focused on the new cyber capabilities.

These features position GLM-5.3 as a strong contender for developers building coding agents, autonomous engineering systems, and security research tools.

GLM-5.3 Benchmark Improvements: GLM-5.3 vs GLM-5.2

The most useful way to evaluate GLM-5.3 is to look at benchmarks where Z.ai provides a direct before-and-after comparison.

From x

What the Benchmark Data Actually Says

First, the upgrade over GLM-5.2 is broad. GLM-5.3 improves on every row in Z.ai's comparison table. The largest relative changes appear on hard, low-baseline tasks such as Terminal-Bench 3.0, ExploitGym, and ExploitBench. That pattern is consistent with Z.ai's claim that more sophisticated long-horizon environments pushed capability further up the difficulty curve.

Second, GLM-5.3 is competitive but not a universal frontier leader. It leads the full comparison on CyberGym (84.5), AutomationBench (48.2), and GDPval-AA v2 (1769 Elo). It is nearly tied with GPT-5.6 Sol on Agents' Last Exam CLI, 28.5 versus 28.6. However, GPT-5.6 Sol scores 34.6 on Terminal-Bench 3.0 and 72.7 on DeepSWE, while Fable 5 reaches 33.7 and 69.7. On ExploitBench, both are more than 20 points ahead of GLM-5.3.

Third, harness and budget details matter. Z.ai evaluated different benchmarks with contexts ranging from 300K to 1M tokens, maximum outputs from 64K to 163,840 tokens, and timeouts as long as ten hours. Several tests used Claude Code 2.1.207 as the agent harness. ExploitGym time budgets were normalized using per-model throughput. These choices are documented, but they mean the score measures a model-plus-harness system rather than abstract intelligence alone.

How to Access GLM-5.3

At launch, access is still being rolled out.

Z.ai's official documentation currently says:

“The GLM-5.3 API is coming soon.”

At the same time, GLM-5.3 is available to GLM Coding Plan users.

Z.ai's Coding Plan documentation says the service supports GLM-5.3 alongside other GLM models and can be used with coding environments such as Claude Code, Cline, OpenCode and related tools.

The official ZCode documentation also describes GLM-5.3 integration through Z.ai and BigModel accounts.

GLM-5.3 Pricing

Pricing is one area where developers should be careful.

At the time of publication, the general GLM-5.3 API pricing has not yet been published by Z.ai in the official GLM-5.3 API documentation, because the API is still described as “coming soon.”

Instead, developers can currently access the model through the GLM Coding Plan.

Z.ai's current plan page advertises the following starting prices:

*Pricing and promotions can change; verify the live Z.ai pricing page before purchase.

The important point is that these are coding subscription prices, not equivalent per-token API pricing.

Once the general GLM-5.3 API becomes available, developers should compare:

• Input-token cost
• Output-token cost
• Reasoning-token cost
• Cached-input pricing
• Context-window pricing
• Rate limits
• Tool-call costs
• Batch pricing
• Enterprise terms

rather than comparing subscription prices alone.

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GLM-5.3 Comparison Table: Which Model Fits Which Workload?

This is not a universal ranking. The correct production metric is cost per accepted result on your own tasks, including retries, latency, tool failures, and human correction.

Should You Use GLM-5.3?

GLM-5.3 deserves a serious evaluation if your workload includes repository-scale coding, automation, long-running tool use, or defensive security. The upgrade over GLM-5.2 is too broad to dismiss as benchmark noise, and the token-efficiency result on Z.ai's private coding test could matter as much as the raw score.

It is not an automatic replacement for every model. Fable 5 and GPT-5.6 Sol remain ahead on several difficult coding and exploit benchmarks in Z.ai's own table. GLM-5.2 remains easier to self-host today. Interactive, latency-sensitive features may also prefer a faster model with optional or disabled reasoning.

For most businesses, the pragmatic path is to test GLM-5.3 through a hosted API, compare it on real tasks, and route selectively. CometAPI is particularly relevant when you want usage-based billing, a 20% displayed discount, and alternative models available through the same operating layer. Check the live catalog first, because this article captures a launch-day product state that may change quickly.

The Bottom Line

GLM-5.3 is one of the most significant open-weight AI releases of August 2026 because it represents a shift in what “model improvement” means. Z.ai did not simply announce a larger model. Instead, it kept the GLM-5.2 base and pushed much harder on post-training with real-world engineering workflows.

The results are impressive:

• 50% coding improvement on Z.ai's Code Bench
• 46.2 → 66.9 on DeepSWE v1.1
• 23.8 → 28.5 on Agents' Last Exam
• 24.4 → 54.4 on ExploitBench
• 84.5% on CyberGym
• 1M-token context
• 128K maximum output
• Stronger agent and tool-use capabilities

The model is currently accessible through Z.ai's Coding Plan, while its general API is still coming. Open weights are expected after additional security evaluation. For developers, the biggest opportunity is not simply asking whether GLM-5.3 beats another model on a leaderboard.

The better question is:

Can GLM-5.3 complete your real engineering workflow with fewer human interventions, lower total cost and acceptable latency?

That is the benchmark that matters.

For teams already experimenting with GLM models, CometAPI provides a practical starting point through its existing GLM-5.2 API integration. As GLM-5.3 becomes available across third-party platforms, a unified API approach can make it easier to compare GLM-5.3 against other reasoning and coding models without rebuilding your entire application stack.

Explore GLM models on CometAPI

FAQs About GLM-5.3

What is GLM-5.3?

GLM-5.3 is Z.ai's August 2026 reasoning model for complex coding, long-horizon agents, automation, and cybersecurity. It uses the same base model as GLM-5.2, with its gains attributed to scaled post-training on more and harder executable environments.

Is GLM-5.3 better than GLM-5.2?

Yes on every benchmark in Z.ai's GLM-5.3 launch table. Examples include Terminal-Bench 3.0 rising from 4.6 to 28.3, DeepSWE from 46.2 to 66.9, AutomationBench from 26.2 to 48.2, and ExploitBench from 24.4 to 54.4.

Is GLM-5.3 open source?

Z.ai calls it an open-weights model but did not publish the weights on launch day. The company says weights will be released two weeks after the August 14 launch, once safety evaluation and hardening are complete.

Why is GLM-5.3 notable for cybersecurity?

Z.ai reports 84.5 on CyberGym, 54.4 on ExploitBench, and 105/130 completed ExploitGym tasks under normalized two-hour/six-hour budgets. The model improves sharply over GLM-5.2, but security outputs still require authorization, sandboxing, expert review, and coordinated disclosure.

How can I access the GLM-5.3 API?

Z.ai provides GLM-5.3 through its Coding Plan and ZCode. CometAPI lists the model ID glm-5.3 with an OpenAI-compatible production endpoint.

Does GLM-5.3 support a 1M-token context window?

Z.ai evaluated some GLM-5.3 benchmarks with a 1M-token context and GLM-5.3 shares GLM-5.2's base model, whose headline feature is a 1M context.

Does GLM-5.3 support vision or image input?

No vision capability was confirmed in the official launch post. CometAPI currently lists text input and text output, so developers should treat GLM-5.3 as text-only unless Z.ai or the API provider publishes a multimodal specification.

Is GLM-5.3 the best model for coding?

It is one of the strongest models in Z.ai's launch comparison and a large improvement over GLM-5.2, but it is not first on every coding benchmark. GPT-5.6 Sol and Fable 5 score higher on Terminal-Bench 3.0 and DeepSWE, so the best model depends on workload, latency, price, and accepted-result rate.