GLM 5.1

Supported Functionality

Item	Specification
Input	Text
Output	Text
Context	200,000 tokens
Max Output	128,000 tokens
Vision	✗ Not Supported (vision handled by separate GLM-5V-Turbo)
Function Calling	✓ Supported

Description

GLM-5.1 is the flagship open-source LLM from Z.ai (formerly Zhipu AI), released on April 7, 2026 under the MIT license. It uses a 754B-parameter Mixture-of-Experts architecture (GlmMoeDSA) with 40B active parameters per token, combining Gated DeltaNet linear attention with standard attention layers. The model is a post-training upgrade to GLM-5, specifically tuned for agentic engineering and long-horizon software development.

The defining breakthrough is its shift from "one-shot generation" to a self-directed "experiment–analyze–optimize" loop: GLM-5.1 can run autonomously for up to 8 hours, proactively rewriting its strategy when progress stalls instead of looping on dead ends. It tops SWE-Bench Pro at 58.4%, ahead of GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%), making it the first open-source model to lead that leaderboard — and it was trained entirely on Huawei Ascend 910B chips with no NVIDIA hardware involved.

Key Capabilities

• Long-Horizon Execution: Sustains autonomous "plan–execute–test–fix–optimize" loops for up to 8 hours; in one demo it built a full Linux desktop environment over 655 iterations.
• Top-Tier Agentic Coding: State-of-the-art on SWE-Bench Pro (58.4%), NL2Repo (42.7%), and Terminal-Bench 2.0 (63.5%) — all real-world engineering benchmarks.
• Strategy Rethinking: Identifies bottlenecks mid-task and switches technical approaches rather than plateauing on repeated attempts.
• Strong Math & Reasoning: Scores 95.3% on AIME 2026 and 86.2% on GPQA-Diamond, placing it in the top tier for STEM reasoning.
• Very Long Context: 200K-token context with a generous 128K-token max output — enough to ingest entire repositories and emit large artifacts in one pass.
• Tool Use & MCP Integration: Native function calling, structured output, context caching, and MCP support; works out-of-the-box with Claude Code, OpenClaw, and Cline.
• Open & Commercially Usable: MIT-licensed weights on Hugging Face (zai-org/GLM-5.1) in BF16 and FP8, deployable via vLLM, SGLang, and KTransformers.

Technical Strengths

Feature	Benefit
GlmMoeDSA hybrid MoE	256 routed + 1 shared expert, 8+1 active per token — large-model capacity with efficient inference
Gated DeltaNet linear attention	Mixed with standard attention to slash long-context cost, making 200K windows practical
Post-training-driven gains	Same GLM-5 base, +28% coding lift purely from multi-task SFT → multi-stage RL → cross-stage distillation
8-hour autonomous loop	Rare-in-industry stability over long tasks: goal alignment holds, errors don't compound
Trained on Huawei Ascend	Proves frontier-grade training without NVIDIA, validating an alternative hardware stack
Full MIT open weights	No commercial licensing friction; substantially lower TCO than comparable closed APIs

Capability Ratings

Dimension	Rating	Notes
Reasoning	Excellent	95.3% on AIME 2026, 86.2% on GPQA-Diamond; top-tier STEM reasoning
Coding	Top-tier	#1 on SWE-Bench Pro, leads NL2Repo and CyberGym; built for long-horizon engineering
Creative Writing	Strong	Markedly improved on open Q&A and multi-turn dialogue, but trails GPT/Claude peers
Multimodal	Moderate	Text-only; no native vision or audio (use companion GLM-5V-Turbo for vision)
Response Speed	Moderate	~44–59 tokens/s — above average but not a latency-optimized model
Context Window	Huge	200K input + 128K output; output ceiling exceeds most peers

Use Cases

• Large Codebase Refactoring: Leverages 200K context and sustained execution to refactor architectures and modernize legacy code across files.
• Autonomous SWE Agents: Plugs into Claude Code or OpenClaw to deliver full requirement-to-PR loops without manual hand-holding.
• Research & Algorithm Experimentation: Runs benchmarks, finds bottlenecks, and self-tunes — demos include a 6.9× vector-DB throughput optimization.
• Long-Document & Complex Instruction Work: Contract review, technical-spec interpretation, and long-form research synthesis with high fidelity.
• On-Prem Enterprise Deployment: MIT-licensed open weights satisfy data-sovereignty needs for finance, government, and regulated industries.
• Math Competitions & STEM Education: High-accuracy problem solving with transparent reasoning steps for tutoring and content generation.
• Security & Pentest Research: Leading 68.7% on CyberGym makes it strong for automated vulnerability analysis and defensive engineering tasks.