GLM-5 proved that frontier AI could be built on non-NVIDIA hardware. GLM-5.1 shifts focus to a different frontier: sustained agentic execution over long horizons. Here's a detailed comparison of what changed, what improved, and whether you should upgrade.
📋 Table of Contents
- 1.Benchmark Improvements
- 2.The Long-Horizon Shift
- 3.Licensing: Open-Weight → MIT
- 4.API & Platform Changes
- 5.Coding Agent Compatibility
- 6.Should You Upgrade?
- 7.Lushbinary Migration Support
1Benchmark Improvements
| Benchmark | GLM-5 | GLM-5.1 | Δ |
|---|---|---|---|
| SWE-Bench Pro | 55.1% | 58.4% | +3.3 |
| NL2Repo | 35.9% | 42.7% | +6.8 |
| Terminal-Bench 2.0 | 56.2% | 63.5% | +7.3 |
| CyberGym | 48.3% | 68.7% | +20.4 |
| BrowseComp | 62.0% | 68.0% | +6.0 |
| AIME 2026 | 95.4% | 95.3% | -0.1 |
| GPQA-Diamond | 86.0% | 86.2% | +0.2 |
The biggest jump is CyberGym (+20.4 points), followed by Terminal-Bench 2.0 (+7.3) and NL2Repo (+6.8). Reasoning benchmarks are essentially flat — the improvements are concentrated in coding and agentic tasks.
2The Long-Horizon Shift
This is the fundamental difference. GLM-5 tends to exhaust its repertoire early — it applies familiar techniques for quick initial gains, then plateaus. Giving it more time doesn't help. GLM-5.1 is specifically designed to stay productive over much longer sessions, breaking complex problems down, running experiments, and revising strategy through repeated iteration.
The VectorDBBench demonstration makes this concrete: GLM-5.1 sustained meaningful optimization over 600+ iterations and 6,000+ tool calls, reaching 21.5K QPS. GLM-5 would have plateaued much earlier in the same setup.
3Licensing: Open-Weight → MIT
GLM-5 was released under a permissive open-weight license. GLM-5.1 upgrades to the MIT License — the most permissive standard open-source license. This removes any ambiguity about commercial use, modification, and redistribution rights.
4API & Platform Changes
Both models are available on api.z.ai and BigModel.cn. GLM-5.1 adds explicit compatibility with Claude Code and OpenClaw. The GLM Coding Plan now supports GLM-5.1 with a 3×/2× peak/off-peak quota multiplier (promotional 1× off-peak through April 2026).
5Coding Agent Compatibility
GLM-5.1 expands coding agent support to include Claude Code, OpenCode, Kilo Code, Roo Code, Cline, and Droid. The Z Code GUI adds multi-agent development with SSH remote machine support — a new capability not available with GLM-5.
6Should You Upgrade?
- Yes, upgrade if: You use GLM for coding tasks, agentic workflows, or long-running optimization. The improvements are substantial across all coding benchmarks.
- No rush if: You primarily use GLM for reasoning or math tasks. Performance is essentially unchanged in those areas.
7Lushbinary Migration Support
Upgrading from GLM-5 to GLM-5.1? At Lushbinary, we help teams migrate between model versions, update inference configurations, and validate performance on your specific workloads.
🚀 Free Consultation
Planning a GLM-5 to GLM-5.1 migration? We help teams upgrade model versions, validate performance, and optimize inference configurations.
❓ Frequently Asked Questions
What changed from GLM-5 to GLM-5.1?
GLM-5.1 focuses on long-horizon agentic engineering. Key improvements: SWE-Bench Pro 55.1% → 58.4%, NL2Repo 35.9% → 42.7%, Terminal-Bench 2.0 56.2% → 63.5%, CyberGym 48.3% → 68.7%. The biggest change is sustained productivity over hundreds of optimization rounds.
Is GLM-5.1 still trained on Huawei Ascend chips?
Zhipu AI has not disclosed specific training hardware details for GLM-5.1. GLM-5 was notably trained entirely on Huawei Ascend chips. GLM-5.1's announcement focuses on capability improvements rather than hardware provenance.
📚 Sources
- Z.ai — GLM-5.1: Towards Long-Horizon Tasks (April 7, 2026)
- HuggingFace — GLM-5.1 Model Weights
- GitHub — GLM-5.1 Repository
Content was rephrased for compliance with licensing restrictions. Benchmark data sourced from official Zhipu AI publications as of April 8, 2026. Pricing and availability may change — always verify on the vendor's website.
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