Repository-scale coding agents
Engineering teams evaluating an agent for multi-file implementation, debugging, refactoring, tests, command-line work, and long tasks with explicit verification.
Independent tool overview
GLM-5.3 at a glance
GLM-5.3 is Z.ai's 753-billion-parameter open-weight reasoning model for long-horizon coding, tool-using agents, and authorized vulnerability research. It uses the same base model as GLM-5.2 but receives stronger post-training, mandatory thinking, and low, high, or max reasoning effort. It is available through Z.ai's API, GLM Coding Plan and ZCode, and self-hosted weights; pay-as-you-go API pricing is $1.40 per million input tokens, $0.26 per million cached input tokens, and $4.40 per million output tokens.
Visit the official GLM-5.3 site ↗
- Model code
- glm-5.3
- Model size
- 753B parameters; roughly 756 GB weight repository
- Context
- Up to 1M tokens in published evaluation configurations
- Reasoning
- Mandatory thinking with low, high, or max effort
- Primary strengths
- Long-horizon coding, tool use, and authorized security analysis
- API price
- $1.40 input / $0.26 cached input / $4.40 output per 1M tokens
- Weights
- Available under the custom GLM-5.3 License
- Languages
- English and Chinese tags on the official model card
- Reviewed
- August 31, 2026
Overview
What GLM-5.3 is
GLM-5.3 focuses on substantial engineering work rather than short code completion. Z.ai trained it on executable environments that resemble production debugging, infrastructure optimization, repository-scale implementation, research, tool use, and multi-stage verification. The company reports large gains over GLM-5.2 on Terminal-Bench 3.0, DeepSWE v1.1, Agents' Last Exam, cyber benchmarks, and its private Z.ai Code Bench.
The release is a post-training upgrade: Z.ai says GLM-5.3 shares GLM-5.2's base model and receives its improvements from further reinforcement-learning environments and scaling. It supports long contexts used at up to one million tokens in the published evaluations and can emit very long reasoning and coding trajectories, which makes cost, compaction, timeouts, state, and verification important parts of the application design.
Thinking cannot be disabled. The API supports low, high, and max reasoning effort and defaults to max. An existing integration that sends thinking.type as disabled will fail after a direct model swap; Z.ai instructs teams to enable thinking and use low effort first during migration if they previously relied on a non-thinking path.
The model is available as a Z.ai API endpoint, through a points-based GLM Coding Plan in tools such as ZCode, Claude Code and OpenCode, and as downloadable weights on Hugging Face. The weight repository is roughly 756 GB and lists 753 billion parameters, so self-hosting the full model is an accelerator-cluster project rather than a normal laptop workflow.
Z.ai openly emphasizes GLM-5.3's vulnerability-discovery and exploitation capability. That can help authorized defensive teams find and reproduce flaws, but it also raises the consequence of excessive network, credential, shell, repository, or deployment access. Use isolated environments, explicit written scope, allowlisted targets, non-production credentials, command review, output screening, and coordinated disclosure.
Use cases
Who GLM-5.3 is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Cost-sensitive API workloads
Developers seeking a capable agentic model at lower token prices than many closed frontier APIs and able to benefit from cached repeated repository context.
Controlled self-hosting
Organizations with accelerator infrastructure and model-serving expertise that need open weights, an OpenAI-compatible serving layer, and greater control over code and runtime data.
Authorized defensive security
Internal security teams and maintainers performing scoped code review, vulnerability reproduction, patch development, and disclosure in isolated, permissioned environments.
Long-running technical research
Workflows that combine code, tools, experiments, logs, and iteration over hours, provided humans define success checks and review evidence.
Capabilities
Core GLM-5.3 features
Long-horizon agent training
Post-trained on larger executable environments representing complex engineering and research workflows with multi-step dependencies and verifiers.
Mandatory adjustable reasoning
Supports low, high, and max reasoning effort. Thinking remains enabled and max is the default and Z.ai's recommended level for difficult coding tasks.
Large context
Z.ai evaluates the model with contexts up to one million tokens on repository, agent, and software tasks, although applications still need context selection and compaction.
Tool and terminal workflows
Designed for agent harnesses that inspect repositories, edit files, run commands, execute tests, collect evidence, and iterate toward a verified result.
Security reasoning
Shows strong vendor-reported performance in vulnerability discovery and multi-stage exploitation benchmarks, making defensive governance a core deployment requirement.
Z.ai API
Available as a hosted endpoint with token-based input, cached-input, and output pricing plus context caching and optional paid built-in web search.
GLM Coding Plan and ZCode
Runs in a points-based coding subscription with integrations for ZCode and multiple agent tools, five-hour and weekly quotas, cache-aware consumption, and off-peak discounts.
Open-weight deployment
Official weights can be served through frameworks listed by Z.ai, including SGLang, vLLM, Transformers, KTransformers, and TokenSpeed.
Custom commercial license
The GLM-5.3 License broadly permits use, modification, distribution, fine-tuning, and sale while imposing attribution, legal-compliance, warranty, and large Model-as-a-Service conditions.
Process
How the GLM-5.3 workflow works
Step 1
Select the access path
Use the pay-as-you-go API for product integration, a Coding Plan for supported interactive agents, or self-host only when the organization can operate a very large model and has reviewed the custom license.
Step 2
Migrate parameters safely
Change any thinking.type disabled configuration to enabled, begin with reasoning_effort low, update the model code to glm-5.3, and then test high or max where the quality gain justifies latency and output cost.
Step 3
Create a representative evaluation
Use private, versioned tasks from the real codebase covering implementation, regression repair, tests, build systems, tool calls, security, documentation, and refusal boundaries. Score completed artifacts, not persuasive explanations.
Step 4
Limit environment authority
Run in an isolated branch or ephemeral container with least-privilege credentials, a domain and command allowlist, protected secrets, no default production access, bounded CPU and time, and explicit approval for network, package, deployment, or destructive actions.
Step 5
Structure the task
Provide the objective, relevant repository context, constraints, tests, prohibited actions, acceptance criteria, budget, and required evidence. Do not send an undifferentiated million-token repository when a smaller verified context is enough.
Step 6
Review the plan and diffs
Require the agent to inspect before editing, keep changes scoped, run tests and security checks, explain failures, and present the final diff and evidence for human approval. Treat generated terminal commands as untrusted input.
Step 7
Govern security use
Obtain written authorization and target scope; isolate the lab; prohibit unrelated scanning, persistence, credential harvesting, lateral movement, or data access; document findings; and follow the maintainer's coordinated-disclosure process.
Step 8
Measure cost and reliability
Log model version, effort, token categories, cache hit rate, tool calls, wall time, retries, test outcome, revert rate, and reviewer acceptance. Use output caps, timeouts, and circuit breakers for long trajectories.
Step 9
Roll out gradually
Start with suggestions or draft pull requests, then allow low-risk autonomous steps only after evidence shows they are reliable. Keep protected branches, code review, CI, audit logs, kill switches, and rollback in place.
Cost
GLM-5.3 pricing and free plan
Z.ai offers GLM-5.3 through pay-as-you-go API tokens, a separate GLM Coding Plan, and self-hosted weights. The hosted API is $1.40 per million input tokens, $0.26 per million cached input tokens, and $4.40 per million output tokens; cache storage is listed as limited-time free and built-in web search is $0.01 per use. The current ZCode page displays promotional Coding Plan rates, while the docs describe five-hour and weekly credit caps and 50% off-peak consumption. Self-hosted weights have no per-token license fee but require substantial hardware and operations.
Pay-as-you-go API
$1.40 input / $0.26 cached input / $4.40 output per 1M tokens
Hosted model access for applications and custom agent integrations.
- Prices in USD
- Cached-input storage listed as limited-time free
- Built-in web search costs $0.01 per use
- Long max-effort outputs can dominate total cost
- Confirm data handling, regional availability, rate limits, and support for the actual account
GLM Coding Lite
$12.60/month promotional; $18 shown as list
Entry subscription for lighter interactive agent work.
- 10,000 weekly credits in current documentation
- 2,000 rolling five-hour credits
- Supports GLM-5.3 and GLM-5.3-Flash
- Off-peak model calls use 50% of standard credits
- Promotion and plan display can change
GLM Coding Pro
$56/month promotional; $80 shown as list
Higher-volume coding plan for regular development on mid-sized repositories.
- 60,000 weekly credits
- 12,000 rolling five-hour credits
- Six times Lite usage in the current ZCode comparison
- Priority model access and curated MCP tools
- Subscription credits are not the same as unrestricted API use
GLM Coding Max
$117.60/month promotional; $168 shown as list
Largest individual coding quota for advanced and long-running work.
- 140,000 weekly credits
- 28,000 rolling five-hour credits
- Fourteen times Lite usage in the current comparison
- First access to selected new models and features
- Still subject to rolling, weekly, tool, rate, and abuse limits
Self-hosted weights
No per-token license fee; infrastructure extra
Download and operate GLM-5.3 under its custom license.
- 753B parameters and roughly 756 GB of repository files
- Requires substantial accelerator memory, serving, networking, observability, patching, and security expertise
- Include copyright and license notices
- Organizations above the license's revenue threshold that operate Model as a Service may need Z.ai security review
- Obtain legal advice for the intended commercial distribution or service
Pricing checked . Check current pricing at the source ↗
Assessment
GLM-5.3 strengths and limitations
Where it stands out
- Z.ai reports major gains over GLM-5.2 on long-horizon coding, terminal, tool-use, automation, and security evaluations without changing the base model.
- Low, high, and max effort give applications a direct quality, latency, and cost control while keeping the same model code.
- Hosted token rates are comparatively low for a flagship agentic model, and repeated codebase context can receive a much cheaper cached-input rate.
- A one-million-token context option can support large repositories and extended trajectories when context is curated and compacted well.
- Open weights allow controlled deployment, auditing, fine-tuning, and provider choice for organizations with sufficient infrastructure.
- Official model files support several common inference frameworks and OpenAI-compatible local serving patterns.
- The launch post publishes detailed benchmark settings, timeouts, harnesses, context sizes, output caps, and anti-cheating measures rather than only topline scores.
- The custom license grants broad commercial and derivative-work rights for most users, subject to its stated conditions.
- Strong vulnerability reasoning can materially assist authorized maintainers and defensive security teams when it is paired with scope and disclosure controls.
What to consider
- Vendor and benchmark results do not guarantee performance on a private repository. Several comparisons use Z.ai's own model, harness choices, private benchmark, or selected effort and token budgets.
- GLM-5.3 still trails some closed models on the more advanced exploitation and coding benchmarks published by Z.ai, despite leading or matching on others.
- Thinking cannot be disabled and max is the default. Simple tasks can incur unnecessary latency and output tokens unless the application explicitly selects low or high effort and sets caps.
- A direct migration can fail when the old request sends thinking.type disabled. Tool schemas, output length, context management, and agent behavior also need regression testing.
- A million-token window does not make all context useful. Large prompts raise latency, cost, irrelevant-context risk, secret exposure, and prompt-injection surface.
- The 753B model is impractical for ordinary local hardware. Even compressed deployment requires serious accelerator capacity, engineering, and energy, and the full repository is hundreds of gigabytes.
- The weights use a custom GLM-5.3 License rather than MIT. Large Model-as-a-Service operators above the stated $10 billion revenue threshold may need a Z.ai security review before commercial use.
- Hosted API or Coding Plan use can expose proprietary source code, credentials, logs, customer data, and security findings to an external service. Review terms, privacy, retention, data location, access controls, and contractual requirements before use.
- The Coding Plan's credits, multipliers, five-hour cap, weekly cap, off-peak rate, cache assumptions, and promotions make effective capacity harder to compare with simple token billing.
- Agentic coding can delete files, run malicious package scripts, expose secrets, alter infrastructure, introduce dependencies, or pass a narrow test while breaking another workflow. Least privilege and human review remain necessary.
- Its cyber capability can support unauthorized exploitation as well as defense. Do not connect it to systems, accounts, networks, repositories, or data outside explicit written authorization and scope.
- Generated vulnerability reports can be false, incomplete, or unsafe to publish. Reproduce findings in an isolated lab, assess severity with experts, protect embargoed details, and coordinate disclosure with maintainers.
- Model output is provided without warranty under the license and is not a substitute for professional code review, threat modeling, security testing, legal review, safety engineering, or production accountability.
Compare
GLM-5.3 alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Consumer
GLM-5.1
Choose GLM-5.1 only for compatibility comparisons; GLM Coding Plan requests are now routed toward GLM-5.3 and the newer model is the current flagship.
Explore GLM-5.1 →Consulting
Kimi K3
Choose Kimi K3 to compare another current open-weight frontier model focused on agentic reasoning and coding.
Explore Kimi K3 →Consumer
Qwen3.7-Max
Choose Qwen3.7-Max when Alibaba's current long-horizon agent model, ecosystem, and commercial terms fit better.
Explore Qwen3.7-Max →Coding
Qwen3-Coder-Next
Choose Qwen3-Coder-Next when a much smaller open-weight model is easier to operate than GLM-5.3's 753B footprint.
Explore Qwen3-Coder-Next →Coding
Claude Code
Choose Claude Code when a polished supported coding-agent product and Anthropic's model ecosystem matter more than open weights or Z.ai pricing.
Explore Claude Code →Questions
GLM-5.3 FAQs
What is GLM-5.3?
GLM-5.3 is Z.ai's flagship 753B open-weight reasoning model for long-horizon coding, agent tools, and authorized vulnerability research. It is a post-training upgrade over the same base model used by GLM-5.2.
Is GLM-5.3 open source?
Z.ai calls it open source, but the weights use a custom GLM-5.3 License. The license broadly permits use, modification, distribution, fine-tuning, and commercial work, with attribution, compliance, warranty, and large Model-as-a-Service conditions. Review the actual license for your use.
How much does the GLM-5.3 API cost?
Z.ai lists $1.40 per million input tokens, $0.26 per million cached input tokens, and $4.40 per million output tokens. Built-in web search is $0.01 per use and cache storage is listed as limited-time free.
What context window does GLM-5.3 support?
Z.ai's published evaluations use contexts up to one million tokens on several repository and agent benchmarks. Verify the exact endpoint and harness limit, and still curate and compact context rather than filling the window by default.
Can GLM-5.3 run locally?
The weights are downloadable and Z.ai documents SGLang, vLLM, Transformers, KTransformers, and TokenSpeed. At 753B parameters with roughly 756 GB of files, full self-hosting requires substantial accelerator infrastructure and is not an ordinary laptop setup.
Can thinking be disabled?
No. GLM-5.3 requires thinking to be enabled. You can set reasoning_effort to low, high, or max; max is the default.
How do I migrate from GLM-5.2?
Remove any thinking.type disabled setting, enable thinking, begin with low effort, switch to glm-5.3, and regression-test tool calls, output length, cost, latency, compaction, safety, and task success before expanding traffic.
What is the GLM Coding Plan?
It is a separate subscription for using GLM models in supported coding tools. It uses model and tool credits with both rolling five-hour and weekly caps, different token multipliers, and a 50% off-peak consumption rate.
Is GLM-5.3 good for cybersecurity?
Z.ai reports strong vulnerability-discovery and exploitation benchmark results. Use it only for systems and code you are authorized to test, inside an isolated and logged environment with target allowlists, human review, and coordinated disclosure.
Does a high benchmark score mean I can let it deploy autonomously?
No. Benchmarks are controlled tasks and cannot establish reliability on your production environment. Begin with draft changes, protected branches, CI, security review, scoped credentials, approvals, and rollback.
Bottom line
Our GLM-5.3 verdict
GLM-5.3 is a serious option for teams that need long-horizon coding, low hosted token prices, or open weights and can provide a strong agent harness. Its most notable strengths—very long trajectories, mandatory reasoning, extensive tool use, and advanced security capability—also make unconstrained deployment risky. Benchmark it on private tasks, use low effort for routine work, exploit caching carefully, keep the agent isolated and least-privileged, and require human evidence review. Self-hosting is viable only for organizations prepared for a 753B-class operational footprint and the custom license.
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