Compatibility testing
Teams maintaining an existing glm-5.1 API integration that need to measure behavior before moving to GLM-5.3.
Independent tool overview
GLM-5.1 at a glance
GLM-5.1 is Z.ai's 754-billion-parameter open-weight text model for long-horizon coding and tool-using agents. It remains available through Z.ai's pay-as-you-go API and as MIT-licensed weights, but it has been superseded by GLM-5.3: current GLM Coding Plan requests naming GLM-5.1 are automatically routed to GLM-5.3. The direct API is priced at $1.40 per million input tokens, $0.26 per million cached input tokens, and $4.40 per million output tokens.
Visit the official GLM-5.1 site ↗
- Released
- April 7, 2026
- Current position
- Active but superseded by GLM-5.3
- Model size
- 754B parameters; about 1.51 TB BF16 repository
- Context
- 200K tokens
- Maximum output
- 128K tokens
- Modalities
- Text input and text output
- License
- MIT for the official weights
- API price
- $1.40 input / $0.26 cached input / $4.40 output per 1M tokens
- Reviewed
- August 31, 2026
Overview
What GLM-5.1 is
Released on April 7, 2026, GLM-5.1 was positioned as a flagship foundation model for agentic engineering. It is a text-in, text-out model with a 200,000-token context window and up to 128,000 output tokens, plus thinking modes, streaming, function calling, context caching, structured output, and MCP integrations.
Z.ai designed the model to keep working through extended engineering loops: planning, editing, running tools, examining results, fixing failures, and iterating. The company's evaluations report a 58.4 score on SWE-Bench Pro and improvements over GLM-5 on repository generation and terminal tasks. Those are useful comparison points, not a substitute for testing the model on a team's own repositories and agent harness.
GLM-5.1 is still a usable model, but it is no longer Z.ai's current flagship. The direct API documentation continues to show the glm-5.1 model code and its own token price. By contrast, the current GLM Coding Plan automatically maps requests for GLM-5.1 and GLM-5.2 to GLM-5.3, so a subscription user is not actually selecting the original 5.1 model there.
The official Hugging Face repository provides 754B-parameter BF16 weights under the MIT License and documents serving with vLLM, SGLang, Transformers, Docker Model Runner, and community quantizations. The full repository is about 1.51 TB, making self-hosting an accelerator-cluster undertaking rather than a typical local installation.
Treat any long-running coding agent as an untrusted operator. Use an isolated branch or container, least-privilege credentials, bounded network and shell access, protected secrets, explicit approvals for destructive or production actions, automated tests, and human review of the final diff and evidence.
Use cases
Who GLM-5.1 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 coding research
Developers comparing how models plan, use tools, repair failures, and sustain work across repository-scale tasks.
Controlled open-weight deployment
Organizations with substantial accelerator infrastructure that value MIT-licensed weights and control over runtime data.
Cost-aware hosted experiments
API teams that can benefit from cached repository context and want a relatively low published token rate for a large agentic model.
Capabilities
Core GLM-5.1 features
Long-horizon agentic coding
Built for multi-stage engineering work that combines planning, implementation, tool use, testing, diagnosis, and repeated optimization.
200K context and 128K output
Supports large prompts and unusually long completions, although effective applications still curate context and cap outputs.
Thinking modes
Supports deep reasoning controls for difficult work. The documented examples enable thinking, and migration testing should confirm the exact behavior expected by the integration.
Tool streaming and function calls
Can stream tool-call parameters and integrate with agent harnesses, command-line tools, external APIs, and MCP servers.
Context caching
Z.ai offers a lower cached-input token rate for repeated context such as stable repository instructions or reused source files.
Structured output
Supports JSON-style structured responses for applications that need machine-readable plans, classifications, or tool arguments.
Hosted API
The direct Z.ai API still documents the glm-5.1 model identifier and OpenAI-compatible calling patterns.
MIT-licensed open weights
Official BF16 weights are downloadable and may be used, modified, distributed, and sold under the MIT License's notice and warranty terms.
Multiple serving frameworks
The model card lists vLLM, SGLang, Transformers, KTransformers, xLLM, and Docker-based options, plus community quantizations.
Process
How the GLM-5.1 workflow works
Step 1
Decide whether 5.1 is necessary
For a new hosted integration, evaluate GLM-5.3 first. Choose 5.1 when preserving a known behavior, reproducing research, or using its MIT-licensed weights matters.
Step 2
Select direct API or self-hosting
Use the pay-as-you-go API when operational simplicity matters. Self-host only after sizing the 754B model, 1.51 TB repository, accelerator memory, networking, storage, serving, monitoring, and security burden.
Step 3
Build a private evaluation set
Use versioned tasks from the real codebase covering implementation, debugging, refactoring, tests, tool calls, long context, security boundaries, and failure recovery. Score artifacts and test results rather than confident prose.
Step 4
Isolate the agent
Run in an ephemeral environment or protected branch with read-first instructions, least-privilege credentials, secret filtering, restricted network destinations, command controls, time and token budgets, and no default production authority.
Step 5
Provide clear acceptance criteria
State the objective, relevant files, constraints, prohibited actions, required tests, output format, budget, and evidence expected at completion.
Step 6
Review plans, commands, and diffs
Require inspection before edits, keep changes scoped, execute tests and security checks, and have a human review the final diff and evidence before merging or deploying.
Step 7
Measure reliability and cost
Log model code, context size, cached and uncached tokens, output tokens, tool calls, wall time, retries, test outcome, human corrections, and revert rate.
Step 8
Migrate deliberately
Compare the same evaluation set on GLM-5.3, update model and reasoning parameters, regression-test tools and output parsing, then shift traffic gradually with rollback available.
Cost
GLM-5.1 pricing and free plan
As of August 31, 2026, Z.ai lists GLM-5.1 pay-as-you-go API rates of $1.40 per million input tokens, $0.26 per million cached input tokens, and $4.40 per million output tokens, with cached-input storage described as limited-time free. GLM Coding Plan subscriptions no longer provide a distinct 5.1 model: requests naming GLM-5.1 are automatically routed to GLM-5.3. The open weights have no per-token license fee, but their roughly 1.51 TB BF16 footprint makes infrastructure the dominant cost.
Pay-as-you-go API
$1.40 input / $0.26 cached input / $4.40 output per 1M tokens
Direct hosted access using the glm-5.1 model identifier.
- Prices are in USD
- Cached-input storage is listed as limited-time free
- Built-in web search, if used, is separately priced
- Long reasoning and output can dominate cost
- Confirm account availability, retention, rate limits, and terms before production use
GLM Coding Plan
Subscription pricing; GLM-5.1 requests route to GLM-5.3
A points-based coding subscription for supported agent tools, not distinct access to the original GLM-5.1 model.
- Current documentation supports GLM-5.3 and GLM-5.3-Flash
- Requests for GLM-5.1 or GLM-5.2 are automatically routed to GLM-5.3
- Five-hour and weekly credit caps apply
- Off-peak usage consumes fewer credits
- Use direct API or self-hosting when exact 5.1 behavior is required
Self-hosted weights
No per-token license fee; infrastructure extra
Operate the MIT-licensed model on organization-controlled hardware or cloud accelerators.
- 754B parameters
- About 1.51 TB for the official BF16 repository
- Requires substantial accelerator memory, storage, networking, serving, monitoring, patching, and security expertise
- Community quantizations can reduce the footprint with quality and compatibility tradeoffs
- Retain the MIT copyright and permission notice
Pricing checked . Check current pricing at the source ↗
Assessment
GLM-5.1 strengths and limitations
Where it stands out
- Designed for sustained coding loops rather than only single-turn code completion.
- Large 200K context window and 128K output ceiling support repository and long-form engineering workflows.
- Function calling, tool streaming, structured output, caching, and MCP support make it usable inside custom agent harnesses.
- Z.ai publishes comparative benchmark results across coding, terminal, reasoning, and tool-use tasks.
- Direct API pricing is relatively low for a very large model and cached input is substantially cheaper than uncached input.
- MIT-licensed weights provide unusually permissive rights for self-hosting, modification, fine-tuning, and commercial use.
- Official documentation covers several serving frameworks and OpenAI-compatible integration patterns.
- The direct API remains documented for teams that need exact 5.1 compatibility.
What to consider
- GLM-5.1 has already been superseded by GLM-5.3, so it is generally not the best starting point for a new hosted coding workflow.
- The GLM Coding Plan silently routes 5.1 requests to 5.3; a team can believe it is testing 5.1 when the service is actually running a newer model.
- Vendor-reported benchmark scores depend on specific prompts, harnesses, timeouts, tool access, and scoring. They do not establish reliability on a private repository or production system.
- A 200K context window can increase latency, cost, irrelevant-context errors, prompt-injection exposure, and secret leakage when applications send files indiscriminately.
- The 128K output ceiling is not a recommendation. Unbounded long trajectories can consume time and money while drifting from the objective.
- At 754B parameters and roughly 1.51 TB in BF16, full self-hosting is impractical on consumer hardware and expensive even for experienced infrastructure teams.
- Community quantizations and third-party providers can change quality, speed, privacy, licensing, and support characteristics compared with the official hosted model.
- Hosted use may send proprietary code, logs, secrets, customer data, or security findings to an external provider. Review privacy, retention, regional, contractual, and compliance terms.
- An agent with shell, network, package-manager, or cloud access can delete files, expose credentials, install malicious dependencies, alter infrastructure, or pass a narrow test while breaking another workflow.
- Generated code and security findings can be wrong or incomplete. Human review, tests, static analysis, dependency review, and production safeguards remain necessary.
- The MIT License provides the software without warranty and does not transfer operational, legal, privacy, security, or product-safety accountability to the model developer.
Compare
GLM-5.1 alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Consumer
GLM-5.3
Choose GLM-5.3 for Z.ai's current flagship behavior, longer context, adjustable reasoning effort, and the model now used when Coding Plan requests name 5.1.
Explore GLM-5.3 →Consulting
Kimi K3
Choose Kimi K3 to compare another current open-weight model aimed at agentic reasoning and coding.
Explore Kimi K3 →Consumer
Qwen3.7-Max
Choose Qwen3.7-Max when Alibaba's current agent model, ecosystem, or commercial deployment path fits better.
Explore Qwen3.7-Max →Coding
Qwen3-Coder-Next
Choose Qwen3-Coder-Next when a substantially smaller open-weight coding model is more practical to operate.
Explore Qwen3-Coder-Next →Coding
Claude Code
Choose Claude Code when a polished supported coding-agent product matters more than self-hosted weights or Z.ai's token pricing.
Explore Claude Code →Questions
GLM-5.1 FAQs
What is GLM-5.1?
GLM-5.1 is Z.ai's 754B open-weight text model released in April 2026 for long-horizon coding, tool use, and general reasoning. It supports a 200K context and up to 128K output tokens.
Is GLM-5.1 still available?
Yes. Z.ai still documents the direct glm-5.1 API and its official weights remain downloadable. However, it has been superseded by GLM-5.3.
Does the GLM Coding Plan still run GLM-5.1?
Not as a distinct model. Current Z.ai documentation says Coding Plan requests for GLM-5.1 or GLM-5.2 are automatically routed to GLM-5.3.
How much does the GLM-5.1 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. Cache storage is listed as limited-time free.
Is GLM-5.1 open source?
Its official model repository provides downloadable weights under the permissive MIT License. Keep the required copyright and license notice, and evaluate the operational and data risks of the intended use.
Can GLM-5.1 run locally?
Technically yes through frameworks such as vLLM, SGLang, Transformers, and Docker Model Runner. Practically, the 754B model and roughly 1.51 TB BF16 repository require substantial accelerator infrastructure; it is not a normal laptop model.
How does GLM-5.1 compare with GLM-5.3?
GLM-5.3 is the current Z.ai flagship and the destination of Coding Plan requests that name 5.1. Use a private evaluation to compare quality, latency, tools, output behavior, safety, and cost before moving a direct API workload.
Is GLM-5.1 safe to let run for hours?
Only with safeguards. Run it in an isolated environment with scoped credentials, network and command controls, token and time limits, protected branches, tests, logs, explicit approvals, and human review before merge or deployment.
Do the benchmark scores guarantee production coding quality?
No. Benchmarks are controlled evaluations and results depend on the harness and settings. Validate the model on representative private tasks and score working code, tests, security, maintainability, and reviewer acceptance.
Bottom line
Our GLM-5.1 verdict
GLM-5.1 remains useful for existing direct-API integrations, reproducible model comparisons, and organizations that specifically value its MIT-licensed weights. For a new hosted coding workflow, GLM-5.3 is the more sensible Z.ai starting point, especially because the current Coding Plan already maps 5.1 requests to it. If 5.1 is retained, benchmark it on private tasks, exploit caching carefully, cap long outputs, isolate the agent, and require human review. Self-hosting only makes sense for teams prepared to operate a 754B-class model and roughly 1.51 TB of BF16 files.
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