Large codebase work
Map architecture, maintain constraints across many files, and execute staged changes with more project context available.
Independent tool overview
GLM 5.2 at a glance
GLM-5.2 is Z.ai's open-weight text model for long-horizon coding and agent workflows, with a one-million-token context window, up to 128K output, configurable reasoning effort, tool calling, caching, and structured output. It remains available through Z.ai and under an MIT license, although GLM-5.3 is now the newer flagship and should be the first comparison for new deployments.
Visit the official GLM 5.2 site ↗
- Developer
- Z.ai
- Context window
- 1M tokens
- Maximum output
- 128K tokens
- Input and output
- Text
- Weights license
- MIT
- Current successor
- GLM-5.3
Overview
What GLM 5.2 is
Z.ai released GLM-5.2 in June 2026 to make very long coding-agent trajectories more usable, not merely to raise the advertised context limit. Its training and serving work focused on large codebases, extended refactors, research reproduction, mobile debugging, and other tasks that can run for hours.
The hosted model accepts text input, supports a one-million-token context window and up to 128K output tokens, and exposes thinking modes, streaming, function calling, context caching, JSON-style structured output, and MCP integrations through Z.ai's API ecosystem.
The weights are published under the MIT license. Z.ai's Hugging Face release lists roughly 753 billion parameters, so local deployment is technically open but operationally far beyond an ordinary laptop or single consumer GPU.
GLM-5.2 is still active and priced in Z.ai's current API table, but GLM-5.3 has succeeded it as the latest flagship. Existing teams can keep using 5.2 for stability or reproducibility; new teams should benchmark 5.2 against 5.3 before locking a model version.
Use cases
Who GLM 5.2 is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Long-horizon engineering agents
Run extended implementations, migrations, debugging loops, research reproduction, and performance work through agent-compatible tools.
Open-weight deployments
Use MIT-licensed weights when an organization can support the substantial infrastructure and operational work of self-hosting.
Capabilities
Core GLM 5.2 features
One-million-token context
Designed and trained for long coding trajectories, with a larger working window than GLM-5.1's 200K context.
Flexible reasoning effort
Choose higher reasoning effort for difficult work or lower effort when latency and token use matter more.
Agent tool support
Function calling, MCP integrations, streaming, and structured output support integration with coding agents and custom systems.
Context caching
Cached input pricing and context reuse can reduce the cost of repeatedly sending a large stable codebase prefix.
Broad coding-tool compatibility
Z.ai documents use with ZCode, Claude Code, OpenCode, Cline, and other supported tools through its Coding Plan endpoint.
Open weights
Z.ai publishes BF16 and optimized variants with an MIT license and support for frameworks including Transformers, vLLM, and SGLang.
Process
How the GLM 5.2 workflow works
Step 1
Choose 5.2 deliberately
Compare GLM-5.2 with GLM-5.3 on representative repository tasks, latency, cost, and regression risk before fixing the model version.
Step 2
Load instructions before the codebase
Give the model repository rules, prohibited actions, test commands, architecture boundaries, and a precise definition of done.
Step 3
Stage long work
Ask for a plan and checkpoints, then require implementation, tests, and evidence instead of assuming that a large context window guarantees completion.
Step 4
Control tools and cost
Use narrow permissions, isolated environments, approval gates, caching, token limits, and spending alerts for agentic production use.
Cost
GLM 5.2 pricing and free plan
Z.ai currently lists GLM-5.2 API usage at $1.40 per million input tokens, $0.26 per million cached-input tokens, and $4.40 per million output tokens, with cached-input storage temporarily free. The separate GLM Coding Plan currently lists monthly Lite, Pro, and Max plans at $18, $72, and $160; plan quota rules and model multipliers apply. Self-hosting has no model license fee but requires substantial compute, storage, engineering, and operations.
Z.ai API
$1.40 input / $4.40 output per 1M tokens
Metered hosted access to GLM-5.2 through the general API.
- $0.26 per 1M cached-input tokens
- Cached-input storage is temporarily free
- Built-in web search is separately listed at $0.01 per use
Coding Plan Lite
$18/month
Entry subscription for lightweight work on a small repository.
- Base usage allowance
- Supports 20+ coding tools
- Latest-model availability and quota multipliers can change
Coding Plan Pro
$72/month
Higher-usage plan for day-to-day development on mid-sized repositories.
- Five times Lite usage on the current public plan page
- Faster generation and selected MCP tools
- Intended for one to two simultaneous projects
Coding Plan Max
$160/month
Highest individual tier for advanced and larger-repository use.
- Twenty times Lite usage on the current public plan page
- Dedicated peak-time resources
- Intended for two or more simultaneous projects
Self-hosted weights
No model license fee
MIT-licensed weights deployed on infrastructure you operate.
- Roughly 753B parameters in the full release
- Hardware and inference costs can be substantial
- Security, scaling, updates, and monitoring are your responsibility
Pricing checked . Check current pricing at the source ↗
Assessment
GLM 5.2 strengths and limitations
Where it stands out
- One-million-token context and 128K maximum output are suited to unusually long engineering trajectories
- Strong integration primitives for coding agents: thinking controls, function calling, streaming, caching, structured output, and MCP
- Current API pricing is transparent and includes discounted cached input
- MIT-licensed weights provide deployment and research flexibility
- Multiple access paths cover chat, API, coding subscriptions, and self-hosting
What to consider
- GLM-5.3 is now the newer flagship, so GLM-5.2 should not automatically be chosen for a new deployment
- A one-million-token limit does not guarantee accurate retrieval, instruction retention, or successful completion across the entire window
- Long prompts and 128K outputs can create high latency and material cost even with cache discounts
- The full 753B-parameter model is impractical to self-host on typical consumer hardware
- Vendor-reported benchmark results depend on specific harnesses, token budgets, tool access, and evaluation settings
- Coding Plan quotas, model multipliers, peak-hour behavior, and supported-tool restrictions require ongoing review
Compare
GLM 5.2 alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Consumer
GLM-5.3
Choose GLM-5.3 first when you want Z.ai's current flagship and can validate migration behavior.
Explore GLM-5.3 →Consumer
Qwen3.7-Max
Consider Qwen 3.7 Max for another current model focused on large-context coding and agentic workloads.
Explore Qwen3.7-Max →Consumer
Claude Sonnet 5
Consider Claude Sonnet 5 when Anthropic's hosted coding ecosystem and model behavior are a better operational fit.
Explore Claude Sonnet 5 →Questions
GLM 5.2 FAQs
Is GLM-5.2 still available?
Yes. Z.ai still lists GLM-5.2 in its model documentation and API pricing. GLM-5.3 is the newer flagship, so new users should compare both.
How large is GLM-5.2's context window?
Z.ai documents a one-million-token context window and a maximum output of 128K tokens. Actual useful context depends on the task, harness, instructions, and retrieval quality.
How much does the GLM-5.2 API cost?
At the August 29, 2026 review, Z.ai listed $1.40 per million input tokens, $0.26 per million cached-input tokens, and $4.40 per million output tokens.
Can GLM-5.2 run locally?
Yes, the weights are MIT-licensed and supported by several inference frameworks. The full model is about 753B parameters, so practical deployment requires specialized multi-GPU or server infrastructure.
Should I use GLM-5.2 or GLM-5.3?
Start by testing GLM-5.3 because it is the newer flagship. Keep 5.2 when its behavior, cost, reproducibility, or an existing validated workflow makes it the better operational choice.
Bottom line
Our GLM 5.2 verdict
GLM-5.2 remains a compelling long-context coding model, especially for teams that value open weights, transparent API pricing, and agent-tool compatibility. Its one-million-token window is useful when paired with disciplined repository instructions, checkpoints, tests, and cost controls. Because GLM-5.3 is now available, 5.2 is best treated as a still-supported version to benchmark or preserve rather than the automatic default for every new build.
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