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Z.ai: GLM 5.1
•
GLM-5.1
•
Released 
April 2026


GLM-5.1

Open-weight reasoning model built for long-horizon agentic coding, sustaining tool-call loops across hours of autonomous engineering work.

Modality:
Text
PDF
model ID
z-ai/glm-5.1
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Output Speed *

N/A
tok/s

Intelligence Index *

26.1
/ 100

Context Window *

202752
tokens

Input price

5.8
Anytoken

Output price

18.22
Anytoken
GLM-5.1: Open-Weight Reasoning for Long-Horizon Agentic Engineering GLM-5.1 is Z.ai's open-weight reasoning model and a post-training refinement of GLM-5, built around a 744B-parameter Mixture-of-Experts architecture with roughly 40B active parameters per token. It sits as the agentic-engineering flagship of the GLM-5 series, released under the MIT license. Its distinguishing trait is sustained performance across extended tool-calling loops rather than single-turn answers: Z.ai positions it for autonomous sessions lasting hours. The workloads that benefit most are repository-scale coding, bug fixing, and multi-step agent pipelines that require stable iteration over hundreds of reasoning rounds. Start building with the GLM-5.1 API on AnyAPI.ai

Performance

Where GLM-5.1 Earns Its Place: Sustained Agentic Coding

GLM-5.1 is engineered to keep improving across long task horizons rather than plateauing early, which is its core design thesis. Z.ai reports 58.4% on SWE-bench Pro, a vendor figure that edges GPT-5.4 and Claude Opus 4.6 on that specific benchmark, and it is positioned for autonomous sessions measured in hours with thousands of tool calls. For production, this means GLM-5.1 suits coding agents that run repository-scale refactors and bug-fixing loops end to end, where stability over many iterations matters more than raw single-turn latency. It is an open-weight, deployment-flexible alternative to closed frontier coders.

Benchmarks

GLM-5.1 Coding Benchmarks and Independent Speed Measurements

On SWE-bench Pro, GLM-5.1's self-reported 58.4% was, at launch, the top open-weight score and narrowly ahead of GPT-5.4 and Claude Opus 4.6 on that test. On Terminal-Bench 2.0 it scores 63.5%, strong but trailing Claude Opus 4.6. Vals AI's independent LiveCodeBench run places it around 81.4%. These numbers favor agentic and repository-level coding over abstract reasoning. On independent Artificial Analysis measurements, the Reasoning configuration runs at roughly 50.6 output tokens/sec with ~1.81s time to first token — below median throughput for open-weight models of similar size, reflecting its verbose reasoning output.

Output Speed

*
N/A
tok/s

Intelligence Index

*
26.1
/ 100

MMLU *

Broad world knowledge and problem-solving
0
%

GPQA *

PhD-level scientific reasoning across physics, biology, chemistry.
87
%

HLE *

Adherence to multi-step structured instructions.
30
%

LiveCodeBench *

Tool-calling reliability in long agentic loops.
0
%

Technical Specifications

What the model supports

GLM-5.1 is a text-in, text-out reasoning model with a context window of roughly 200K tokens and a maximum output of about 128K tokens. Reasoning is always on; unlike GLM-5.2, it exposes a single thinking mode rather than selectable effort levels, so you cannot disable deliberation for latency-sensitive calls. The large output ceiling supports long generated artifacts, but independent reports note output quality can degrade well past 100K tokens in a single run, so the practical usable output is lower than the nominal cap. It ships open-weight under the MIT license with OpenAI-compatible and Anthropic-compatible API surfaces.
Verified Specifications — 
GLM-5.1
*
Input modalities
Text
PDF
output modalities
Text
Context window
202752
 tokens
Maximum output tokens
65535
Reasoning
Yes
Knowledge cutoff
April 2026
Pricing (standard)
5.8
 AnyTokens in
 / 
18.22
 AnyTokens out

Limitations & Trade-offs

Where GLM-5.1 falls short

1
Output collapse past ~100K tokens. Although GLM-5.1 advertises a ~128K output ceiling, independent hands-on reports describe output fracturing suddenly after roughly 100,000 tokens in a single run. This matters for the long-horizon agentic sessions the model is marketed for: a multi-hour task that generates very long outputs can degrade without warning. Plan for checkpointing, task segmentation, and intermediate persistence rather than relying on one uninterrupted mega-generation. For workloads that genuinely need stable ultra-long output, GLM-5.2's larger window and revised handling may be safer.
2
No selectable reasoning effort. Reasoning is always on and GLM-5.1 exposes a single thinking mode, so you cannot dial deliberation down for latency- or cost-sensitive calls the way GLM-5.2's effort levels allow. Combined with its verbose reasoning output — independent evaluation generated unusually high token volumes — this inflates cost and time to final answer on simple requests. For high-frequency, low-complexity traffic, a model with a non-thinking mode or a lighter GLM-Flash variant is a better fit.
3
Below-median throughput. On independent Artificial Analysis measurements, the Reasoning configuration runs at roughly 50.6 output tokens/sec, below the median for comparable open-weight models, and end-to-end response time is extended by always-on thinking. Time to first token (~1.81s) is reasonable, but total latency for completed answers is not. This makes GLM-5.1 a poor fit for interactive, real-time chat UIs and better suited to background or batch agentic jobs where throughput matters less than task completion quality.
4
Text-only, smaller context than frontier peers. GLM-5.1 accepts and produces text only — no image, audio, or video input — and its ~200K context is smaller than the 1M windows offered by GLM-5.2 and several proprietary alternatives. Teams doing multimodal document understanding, screenshot-driven agents, or whole-monorepo single-pass analysis will hit these limits. If your pipeline needs vision input or genuinely huge context, pair GLM-5.1 with a multimodal model or move up to a larger-context option.

Best-Fit Workloads

Where this model earns its place

01

Autonomous coding agents

‍
GLM-5.1 is explicitly built to sustain performance across hundreds of reasoning rounds and thousands of tool calls, with Z.ai positioning it for autonomous sessions lasting hours. This fits agent frameworks that plan, execute, test, and refine code with minimal human intervention. Its top open-weight SWE-bench Pro score at launch supports this use. Budget for checkpointing given the reported output-collapse limitation on very long single runs.

02

Repository-scale bug fixing and refactors

‍
With strong SWE-bench Pro results and a ~200K context, GLM-5.1 can hold substantial multi-file context to diagnose and fix issues or migrate code across a codebase. Independent hands-on tests describe it completing large migration tasks across dozens of components in a single run. For truly whole-repository single-pass work that exceeds ~200K tokens, GLM-5.2's larger window is preferable.

03

Self-hosted and MIT-licensed deployments

‍
Because GLM-5.1 ships open-weight under the MIT license with vLLM and SGLang support, teams needing on-premise control, data residency, or freedom to modify and commercialize can run it without closed-vendor restrictions. This is a decisive advantage over proprietary frontier coders for regulated or infrastructure-sensitive environments, where it delivers competitive coding performance without external API dependency.

04

Terminal and DevOps automation agents

‍
GLM-5.1 scores 63.5% on Terminal-Bench 2.0, indicating solid capability at multi-step shell and terminal agent tasks such as environment setup, script execution, and iterative debugging in a real shell. It is a reasonable choice for CLI-driven automation, though Claude Opus 4.6 leads on this benchmark and GLM-5.2 improves substantially, so validate against your specific terminal workflows.

Pricing in anytokens via AnyAPI
Input
5.8
₳
Output
18.22
₳
Cache write
—
₳
Cache read
2.85
₳
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PerformanceBenchmarksTechnical SpecificationsLimitations & Trade-offsBest-Fit WorkloadsIntegrationFAQ

Integration

Access GLM-5.1 via AnyAPI.ai

Access GLM-5.1 through AnyAPI.ai using a unified API built for multi-model AI applications. Integrate GLM-5.1 without maintaining a separate provider-specific connection, and keep the flexibility to test, switch, or combine models as your application requirements evolve.

01

One API integration

Access GLM-5.1 and other AI models through the same API workflow instead of maintaining separate integrations for every provider.

02

Easy model switching

Test GLM-5.1 against alternative models or switch models as your performance, capability, or cost requirements change without rebuilding your application around another provider API.

03

Flexible for production

Use GLM-5.1 from experimentation through production while keeping your AI stack flexible as workloads, traffic, and model requirements evolve.

04

Multi-model applications

Use GLM-5.1 for the workloads where it performs best and combine it with other models for tasks that require different capabilities, performance, or efficiency.

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Frequently Asked Questions

Answers to common questions about integrating and using this AI model via AnyAPI.ai

GLM-5.1 is Z.ai's open-weight reasoning model built for long-horizon agentic engineering: autonomous coding agents, repository-scale bug fixing and refactors, and multi-step tool-calling workflows. It is designed to sustain performance across hundreds of reasoning rounds, making it strongest where tasks run over many iterations rather than single-turn chat.

GLM-5.1 has a context window of roughly 200,000 tokens (reported between 202,752 and 204,800 across providers) and a maximum output of around 128,000 tokens, up to ~131,072 on some endpoints. Note that independent reports describe output quality degrading past roughly 100,000 tokens in a single run.

GLM-5.1's reasoning is always on and uses a single thinking mode — it does not expose selectable effort levels. This differs from its successor GLM-5.2, which added two configurable thinking-effort levels. As a result, you cannot disable deliberation on GLM-5.1 for latency- or cost-sensitive requests.

Both share the 744B MoE backbone and target agentic coding. GLM-5.2 extends context to roughly 1M tokens (versus ~200K), adds selectable reasoning effort, and posts higher vendor coding scores (SWE-bench Pro 62.1 vs 58.4; Terminal-Bench 2.1 81.0 vs 63.5). GLM-5.1 remains a capable MIT-licensed baseline when ~200K context is sufficient.

Yes. GLM-5.1 is released as open-weight under the MIT license, one of the most permissive licenses available, allowing download, modification, and commercial use. It can be served locally via vLLM or SGLang, or accessed through OpenAI-compatible and Anthropic-compatible APIs.

* Benchmark data source: Artificial Analysis artificialanalysis.ai