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Z.ai: GLM 5 Turbo
Z.AI
•
GLM-5-Turbo
•
Released 
March 2026

Z.AI
GLM-5-Turbo

Z.ai's execution-tuned GLM-5 variant for long-chain agent tasks, stable tool calling and high-throughput OpenClaw-style automation.

Modality:
Text
PDF
model ID
z-ai/glm-5-turbo
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Output Speed *

N/A
tok/s

Intelligence Index *

26.6
/ 100

Context Window *

202752
tokens

Input price

7.2
Anytoken

Output price

24
Anytoken
GLM-5-Turbo: An Execution Engine for Long-Chain Agents GLM-5-Turbo is a proprietary, text-only derivative of Z.ai's GLM-5, tuned for agent execution rather than open-ended chat. Z.ai positions it as a purpose-built model for OpenClaw-style workflows: tool invocation, complex instruction decomposition, scheduled and persistent tasks, and long-chain execution. It sits alongside the open-weight GLM-5 base as an execution-optimized variant, not a successor. With a 200K-token context window and up to 128K output, it targets multi-step agent pipelines where tool-calling stability and sustained throughput matter more than peak reasoning breadth. Access GLM-5-Turbo via the AnyAPI.ai API

Performance

Tool-Call Stability Over Raw Reasoning Breadth

GLM-5-Turbo is built for execution reliability across long, multi-step agent chains. Its training was adapted from the GLM-5 base specifically around OpenClaw task patterns, which Z.ai reports yields more stable tool invocation and better instruction decomposition than the base model. On independent agentic benchmarks it scores strongly, including 98.5% on τ²-Bench Telecom and 33.3% on Terminal-Bench Hard (Artificial Analysis). For production, this means sequential tool-call pipelines complete more consistently, with fewer compounding failures across extended runs—the dimension where GLM-5-Turbo was explicitly optimized rather than general knowledge breadth.

Benchmarks

GLM-5-Turbo on Independent Agentic and Reasoning Benchmarks

Artificial Analysis estimates GLM-5-Turbo at roughly 27 on its Intelligence Index—above the median for comparably priced models, though below the full GLM-5.2 (51.1). It posts 84.7% on GPQA Diamond and 73.2% on IFBench for instruction following. Its strongest independent results are agentic: 98.5% on τ²-Bench Telecom and 33.3% on Terminal-Bench Hard. Note that Z.ai's headline ZClawBench results—where it reportedly outperforms GLM-5—are company-supplied and lack third-party validation. Artificial Analysis has since marked the model deprecated for ongoing benchmarking.

Output Speed

*
N/A
tok/s

Intelligence Index

*
26.6
/ 100

MMLU *

Broad world knowledge and problem-solving
0
%

GPQA *

PhD-level scientific reasoning across physics, biology, chemistry.
85
%

HLE *

Adherence to multi-step structured instructions.
28
%

LiveCodeBench *

Tool-calling reliability in long agentic loops.
0
%

Technical Specifications

What the model supports

GLM-5-Turbo is text-in, text-out with a 200K-token context window and up to 128K output tokens—unusually high output headroom for long agent transcripts and multi-file generation. It supports tool calling, structured outputs, streaming and a thinking mode, with MCP integration for external tools. The large output ceiling is the most production-relevant spec: long-chain agent runs can emit extended tool sequences and results in a single response. Note the context window (200K) is smaller than GLM-5.2's 1M, which matters for very large codebases or document corpora.
Verified Specifications — 
GLM-5-Turbo
*
Input modalities
Text
PDF
output modalities
Text
Context window
202752
 tokens
Maximum output tokens
131072
Reasoning
Yes
Knowledge cutoff
March 2026
Pricing (standard)
7.2
 AnyTokens in
 / 
24
 AnyTokens out

Limitations & Trade-offs

Where GLM-5-Turbo falls short

1
Smaller context than GLM-5.2. GLM-5-Turbo's 200K-token window is a fraction of GLM-5.2's 1M-token context. For agents that must hold an entire large codebase, extended multi-document corpora, or very long cumulative tool histories in-context, this ceiling forces chunking or retrieval. Long-horizon tasks that benefit from lossless 1M context are better served by GLM-5.2.
2
Lower general intelligence than the broader GLM-5 line. On the Artificial Analysis Intelligence Index, GLM-5-Turbo scores around 27 versus roughly 51 for GLM-5.2—a large gap spanning reasoning, coding and knowledge breadth. The model was deliberately narrowed toward execution fidelity, not general capability. For open-ended reasoning, research assistance, or hard math, a higher-intelligence GLM model or frontier alternative is preferable.
3
Headline benchmarks are vendor-supplied. Z.ai's strongest claims—outperforming GLM-5 across OpenClaw categories—come from its own ZClawBench suite, which lacks broad independent validation. While the dataset and trajectories were published for reproduction, teams should validate on their own agent workloads rather than relying on the vendor benchmark for procurement decisions.
4
Text-only and proprietary. GLM-5-Turbo accepts and returns text only—no image, audio, video or document input. Agents needing visual perception (screenshots, GUI grounding, design-to-code) should use the multimodal GLM-5V-Turbo instead. It is also closed-source, so unlike open-weight GLM-5 it cannot be self-hosted or fine-tuned locally.

Best-Fit Workloads

Where this model earns its place

01

Long-chain tool-using agents


The model's core design target. Z.ai tuned it on OpenClaw-style patterns—sequential tool calls, instruction decomposition, multi-agent handoffs—and reports improved stability over GLM-5 across extended runs. Independent agentic scores (98.5% τ²-Bench Telecom) support its tool-use reliability. Best for production agent pipelines where compounding tool-call failures are the main risk.

02

Scheduled and persistent automation


GLM-5-Turbo is explicitly optimized for time-triggered and continuously running tasks. Combined with its large 128K output ceiling, it suits autonomous workflows that run over extended periods and emit long execution transcripts—monitoring agents, recurring data jobs, and persistent background automation where response stability across long runs matters.

03

High-throughput data and office automation


Z.ai highlights data analysis, information retrieval and office automation among the OpenClaw task categories the model handles well, with the largest reported gains in high-throughput data processing. Its throughput orientation makes it suitable for batch-style agent tasks where many steps execute in sequence against structured tools and data sources.

04

Multi-agent orchestration


The model supports instruction decomposition and collaboration across multiple agents in task distribution, making it a candidate for orchestration layers that coordinate sub-agents. Its tool-call stability reduces breakage at handoff points. Note that for very large shared context across many agents, the 200K window may require external state management.

Pricing in anytokens via AnyAPI
Input
7.2
₳
Output
24
₳
Cache write
—
₳
Cache read
1.44
₳
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PerformanceBenchmarksTechnical SpecificationsLimitations & Trade-offsBest-Fit WorkloadsIntegrationFAQ

Integration

Access GLM-5-Turbo via AnyAPI.ai

Access GLM-5-Turbo through AnyAPI.ai using a unified API built for multi-model AI applications. Integrate GLM-5-Turbo 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-Turbo and other AI models through the same API workflow instead of maintaining separate integrations for every provider.

02

Easy model switching

Test GLM-5-Turbo 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-Turbo from experimentation through production while keeping your AI stack flexible as workloads, traffic, and model requirements evolve.

04

Multi-model applications

Use GLM-5-Turbo 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-Turbo is a proprietary, text-only variant of Z.ai's GLM-5, optimized for AI agent execution rather than general chat. It was tuned during training on OpenClaw-style task patterns—tool calling, complex instruction decomposition, scheduled and persistent tasks, and long-chain execution—making it a fit for multi-step agent pipelines that depend on reliable sequential tool use.

GLM-5-Turbo supports a 200,000-token context window (listed as ~202,752 on OpenRouter) and can generate up to 128,000 output tokens in a single response (listed as 131,072 on OpenRouter). The large output ceiling suits long agent transcripts and extended tool-call sequences, though the context window is smaller than GLM-5.2's 1M tokens.

No. GLM-5-Turbo accepts text input and returns text output only—it has no image, audio, video or document input. For agent workflows that require visual perception such as screenshots, GUI grounding or design-to-code, Z.ai's GLM-5V-Turbo adds a native vision encoder; GLM-5-Turbo remains text-only.

GLM-5-Turbo is execution-tuned and narrower, scoring around 27 on the Artificial Analysis Intelligence Index with a 200K context. GLM-5.2 is broader and more capable, scoring roughly 51 with a 1M-token context. Choose GLM-5-Turbo for stable, tool-heavy long-chain agent execution; choose GLM-5.2 for higher general reasoning, coding and long-context work.

Yes. GLM-5-Turbo supports tool calling with strengthened multi-step stability, MCP integration for external tools and data sources, structured outputs, streaming responses, and multiple thinking modes. These capabilities are the model's primary focus, tuned specifically for OpenClaw-style agent workflows.

* Benchmark data source: Artificial Analysis artificialanalysis.ai