Use GLM-5.1 in Tabbit

Use in Tabbit GLM-5.1

GLM-5.1 · Model overview

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Official source

Task guides4

Review sources5

Sources reviewed0

Editor picks8

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Overview · English

GLM-5.1 Explained: Long-Horizon Agents, Access, and Cost

A sourced GLM-5.1 overview covering its 200K context, 8-hour execution claim, Z.AI pricing snapshot, deployment boundaries, and a cautious pilot path.

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All prompts and workflows

Agent workflow · CodingUnverified

GLM-5.1: Long-horizon Agent and Claude Code Configuration

GLM-5.1 should be configured as a “long-horizon engineering Agent”: provide ample context and output budget, clarify the role, tech stack, and acceptance criteria first, then let it loop through execution, compilation, testing, and iteration; in Claude Code, you can switch the model name directly to `GLM-5.1`..

Prepare: model ID, repository, acceptance checks, output budget
Runtime: Z.AI API or Claude Code-compatible harness

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Coding · reasoningUnverified

GLM-5.1: SGLang Heterogeneous Deployment and Interleaved Thinking Configuration

Local deployment of GLM-5.1 depends on the exact `transformers==5.3.0` version and SGLang parser configuration; coding Agent workflows must enable `Interleaved + Preserved Thinking` mode to prevent multi-turn forgetting..

Prepare: GPU/CPU layout, package versions, parser settings, test task
Runtime: KTransformers and SGLang local deployment

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Agent workflow · API configurationUnverified

GLM-5.1: Claude Code Tool Discovery and System Role Compatibility Workaround

When using GLM-5.1 in Claude Code or a multi-Agent framework, you must explicitly inject `tool_reference` parsing rules into the system prompt to prevent tool deadlocks, and intercept the `system` role in `messages[]` to avoid HTTP 422 errors..

Prepare: tool_reference schema, message roles, error trace, regression task
Runtime: Claude Code or multi-agent message adapter

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Agent workflow · CodingUnverified

GLM-5.1: OpenCode Multi-Model Orchestration and Anti-Overthinking Prompt

Embedding GLM-5.1 in a multi-model pipeline as a “high-value code executor,” together with a system prompt that enforces action, can effectively resolve overthinking deadlocks in Agents and YAML indentation defects..

Prepare: model roles, task slices, action budget, YAML checks
Runtime: OpenCode multi-model orchestration

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Read evidence and limits

Public results use different versions, tiers, and harnesses; unknown values stay unknown.

All reviews and sources

Z.aiVendor report

GLM-5.1: Z.ai's Official Long-Horizon Engineering Benchmarks and Reproduction Conditions

Z.AI’s 2026-04-07 material claims up to 8 hours of sustained execution, 58.4 on SWE-Bench Pro, and 3.6× geometric-mean speedup on KernelBench Level 3; results depend on OpenHands/Terminus/Claude Code harnesses.

Evidence: Vendor report
Boundary: Does not make the official results a bare-model ranking or your repository success rate.

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Serenities AIEditorial analysis

GLM-5.1: Serenities AI's Self-Reported Benchmarks and the Boundaries of Independent Validation

Serenities AI’s 2026-03-29 evaluation separates an early Claude Code self-reported 45.3 from a later SWE-Bench Pro 58.4 and warns they are not the same test; its setup must be read as reported.

Evidence: Editorial analysis
Boundary: Does not treat the two numbers as one rerun or independent validation.

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Artificial AnalysisEditorial analysis

GLM-5.1: Artificial Analysis Independent Intelligence Index and Inference Throughput Benchmark

The Artificial Analysis GLM-5.1 Reasoning page collected 2026-08-20 records Intelligence Index 41 and 82.7 tokens/s, while noting verbosity and relatively high cost; this is an aggregated platform index.

Evidence: Editorial analysis
Boundary: Does not make the aggregate index fixed for a provider or production cost.

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Z.ai

Use GLM-5.1 in Tabbit

Explore sourced prompt guides, evaluations, and community reports for GLM-5.1—then use the model directly in Tabbit.