Core Conclusion
Zhipu released GLM-5.1 in early April, positioned as the next-generation flagship model for AI Agents. Its core selling point is not absolute scores on static benchmarks, but rather sustained optimization capability during long-horizon tasks — the model demonstrates continuous improvement across 600 iterations of long-range reasoning. This forms a sharp contrast with GLM-5’s “unlimited weekly quota” plan adjustment: GLM-5 is converging on commercialization, while GLM-5.1 is exploring new Agent scenarios.
GLM-5.1 Technical Highlights
Long-Horizon Task Capability
GLM-5.1’s core innovation lies in its continuous learning ability across multiple iterations. Traditional models tend to experience “capability degradation” in multi-round Agent loops — output quality declines as conversation rounds increase. GLM-5.1, through architectural optimization, maintains a trend of continuous improvement across 600 iterations.
| Capability Dimension | GLM-5 | GLM-5.1 | Improvement Direction |
|---|---|---|---|
| Long-range reasoning | Baseline | Significantly enhanced | Multi-step task decomposition and backtracking |
| Iterative optimization | Limited | 600 iterations of continuous improvement | Agent self-correction loops |
| SWE-Bench Pro | Industry-leading | Further ahead | Code repair tasks |
| Agent tool calling | Supported | Enhanced | Tool selection accuracy |
Leading in SWE-Bench Pro
In SWE-Bench Pro (the professional version of the software engineering benchmark), GLM-5.1’s performance ranks in the industry’s first tier. This benchmark simulates real code repair scenarios — given a GitHub issue and a codebase, the model needs to understand the problem, locate the code, and propose a fix.
For Agent scenarios, SWE-Bench Pro is a more meaningful metric than traditional Q&A benchmarks because it measures:
- Understanding complex codebases
- Multi-step reasoning (locate → analyze → fix → verify)
- Tool usage (search, read, edit, test)
Why It Matters
Differentiation of Domestic Models in the Agent Race
In the domestic large model competition, each vendor is finding its differentiated positioning:
| Vendor | Core Positioning | Advantage Scenarios |
|---|---|---|
| DeepSeek | Extreme cost efficiency | Large-scale API calls, long text |
| Kimi/Moonshot | Long context + search enhancement | Information retrieval, knowledge organization |
| MiniMax | Multimodal + safety | Content creation, safety-sensitive scenarios |
| Zhipu GLM | Agent + code | Programming assistance, automated workflows |
GLM-5.1’s release further strengthens Zhipu’s positioning in the Agent + code track. The sustained optimization capability for long-horizon tasks is a core requirement for Agent scenarios — a model that can continuously work for hundreds of rounds without degradation is more practically valuable than one that performs excellently in single-turn conversations.
GLM-5 Commercialization vs GLM-5.1 Innovation
Notably, Zhipu is simultaneously doing two things:
- GLM-5 commercialization convergence: Stopping the “unlimited weekly quota” old plan, moving to more refined pricing strategies
- GLM-5.1 technical breakthrough: Building technical barriers in Agent long-horizon capabilities
This “tightening old products while launching new ones” strategy is increasingly common among domestic model vendors — maintaining profit margins through product iteration during price wars.
Comparison with Competitors
Long-Horizon Agent Capability
| Model | Iteration Stability | Degradation at 600+ Rounds | Agent Scenario Fit |
|---|---|---|---|
| GLM-5.1 | Continuous improvement | Minimal | High |
| Claude Sonnet 4.6 | Stable | Low | High |
| GPT-5.5 | Medium | Medium | Medium |
| Qwen 3.5 | Good | Low | Medium-High |
| Kimi K2.5 | Good | Low | Medium-High |
Pricing Reference
Zhipu’s pricing strategy has shifted from “unlimited weekly quota” to more structured plans:
| Plan | Monthly Fee | Use Case |
|---|---|---|
| New plan (former unlimited users) | Pay-per-use | High-frequency Agent usage |
| Standard plan | Monthly subscription | Daily development assistance |
| Free trial | Limited quota | Evaluation and testing |
Note: Zhipu stopped automatic renewal of the GLM Coding Plan unlimited weekly quota old plan on April 30; affected users received 2 months of new plan benefits.
Action Recommendations
Scenarios Suitable for GLM-5.1
- Agent-driven code repair: Scenarios requiring continuous work in large codebases with multi-step reasoning
- Long-horizon automated workflows: Tasks requiring the model to maintain consistency and improvement trends across many rounds of interaction
- SWE-Bench-type evaluation tasks: Scenarios requiring high-accuracy code understanding and repair capabilities
Testing Strategy
- Run a 600-round stress test first: GLM-5.1’s core selling point is long-horizon stability; this capability should be verified with extensive iterations
- Compare SWE-Bench Pro performance: If your team cares about code quality, use actual code repair tasks to compare GLM-5.1 against other models
- Evaluate tool call accuracy: In Agent scenarios, tool call accuracy directly impacts task completion rate
Migration Recommendations
- GLM-5 users: If you previously used the unlimited weekly quota plan, note that automatic renewal stopped on April 30. You’ve received 2 months of new plan benefits. Use this time to test GLM-5.1
- New developers: GLM-5.1 represents Zhipu’s current technical frontier in the Agent track and is worth considering as one of the domestic Agent model options
- Budget-conscious users: Watch Zhipu’s pricing adjustments — the new plan may be more expensive than the old unlimited plan; ROI needs to be evaluated