I. The Tripartite Standoff: A New Landscape in Large Model Competition
The global AI large model competition in 2026 has entered a white-hot stage. Among the numerous contenders, Zhipu AI's GLM-5.1, OpenAI's GPT-5.4, and Anthropic's Claude Opus 4.6 are regarded as the pinnacle representatives of current technological prowess.
GLM-5.1 was officially released on April 7, 2026, open-sourced under the MIT license, and boasts 754 billion parameters, making it Zhipu's most powerful flagship model to date. The core breakthrough of this model lies in its long-range Agent engineering capability—it can sustain autonomous work on a single task for up to 8 hours, effectively addressing the pain point of traditional large models "getting dumber the longer they run" during extended tasks.
GPT-5.4 is OpenAI's latest version launched in 2026, integrating Codex capabilities and demonstrating solid performance in standard benchmark tests. According to evaluation data released by the LLM Council on March 21, 2026, GPT-5.4 achieved a score of 87.3% on the MMLU (70B) benchmark, slightly leading its competitors.
Claude Opus 4.6, as the flagship version of Anthropic's Claude 4 series, excels in code generation and complex reasoning tasks. Its HumanEval benchmark score is as high as 92.5%, with an MATH test score of 88.7%, maintaining a leading position in the field of mathematical reasoning.
These three models each have their strengths: GLM-5.1 performs best in real-world industrial code repair scenarios, GPT-5.4 holds a slight edge in general language understanding, while Claude Opus 4.6 is more outstanding in mathematical reasoning and complex analysis tasks.
II. In-Depth Comparison of Coding Capabilities: GLM-5.1 Achieves Supremacy
2.1 SWE-Bench Pro: The Touchstone for Real Industrial Code Repair
SWE-Bench Pro is widely recognized as the authoritative benchmark for measuring a model's real-world code repair ability. Its test cases are derived from real GitHub repository issues, not artificially constructed simplified scenarios. This characteristic makes it better reflect a model's performance in actual engineering environments.
On the SWE-Bench Pro leaderboard, the scores of the three models are as follows:
| Model | SWE-Bench Pro Score |
|---|---|
| GLM-5.1 | 58.4% ⭐ |
| GPT-5.4 | 57.7% |
| Claude Opus 4.6 | 57.3% |
GLM-5.1 tops the list with a score of 58.4%, comprehensively surpassing GPT-5.4 and Claude Opus 4.6. This result has garnered widespread attention in the AI community. It is particularly noteworthy that GLM-5.1's programming capability score reached 45.3 points, only 2.6 points behind the globally strongest Claude Opus 4.6.
2.2 SWE-Bench Standard: Comprehensive Code Capability Assessment
In the standard SWE-Bench test, the competitive landscape presents a different picture:
| Model | SWE-Bench Score |
|---|---|
| Gemini 3.1 Pro | 78.80% |
| GPT 5.4 | 78.20% |
| Claude Opus 4.6 (Thinking) | 78.20% |
| GPT 5.3 Codex | 78.00% |
This leaderboard shows that while GLM-5.1 leads in Agentic Coding scenarios, Gemini 3.1 Pro still maintains an advantage in standard code evaluation, with GPT-5.4 and Claude Opus 4.6 tied in the second tier.
2.3 NL2Repo: Natural Language to Code Comprehension Ability
NL2Repo (Natural Language to Repository) tests a model's ability to understand and process the relationship between natural language descriptions and code repositories. On this metric, GLM-5.1 scored 42.7%, significantly leading Claude Opus 4.6's 33.4%. This means GLM-5.1 has a significant advantage in understanding programming requirements described by users in natural language.
III. General Capability Comparison: Each Has Its Merits
3.1 Overview of Benchmark Test Data
According to the comprehensive evaluation report released by the LLM Council on March 21, 2026:
| Metric | GPT-5.4 Pro | Claude Opus 4.6 | Gemini 3.1 Pro |
|---|---|---|---|
| MMLU (70B) | 87.3% | 86.9% | 87.1% |
| HumanEval | - | 92.5% | - |
| MATH | - | 88.7% | - |
MMLU (Massive Multitask Language Understanding) is an important metric for measuring a model's cross-domain knowledge mastery. GPT-5.4 Pro leads slightly with a score of 87.3%, demonstrating its comprehensive strength in general language understanding.
HumanEval and MATH are specialized benchmarks for evaluating code generation and mathematical reasoning abilities. Claude Opus 4.6 achieved scores of 92.5% and 88.7% respectively in these two tests, showcasing its advantage in complex reasoning tasks.
3.2 The Balance of Intelligence and Efficiency
According to detailed evaluations by Zhihu users, Zhipu's previous generation GLM-5 was already "the most balanced model in all aspects during the Spring Festival period," and GLM-5.1 further strengthens AI coding and Agent capabilities on this foundation. Compared to GPT-5.4 and Claude Opus 4.6, GLM-5.1 places more emphasis on the product philosophy of "intelligence and action can be combined," significantly improving practical task execution capabilities while maintaining high reasoning intelligence.
IV. Agent Capability: GLM-5.1's Unique Skill
4.1 8-Hour Long-Range Task Processing
The most revolutionary breakthrough of GLM-5.1 lies in its long-range Agent engineering capability. This model can sustain autonomous work on a single task for up to 8 hours, solving the problem of performance degradation in traditional large models during long-term projects. This capability is of significant value for complex software engineering tasks that require several hours or even a full day of continuous work.
4.2 600-Step Long-Range Task Processing
GLM-5.1 is specifically optimized for long-range tasks exceeding 600 steps, performing excellently when handling complex, multi-stage programming projects. In comparison, while GPT-5.4 and Claude Opus 4.6 also possess Agent capabilities, their performance in ultra-long task scenarios still lags behind.
4.3 Open Source Advantage
GLM-5.1 is fully open-sourced under the Apache 2.0 license, meaning developers can freely use, modify, and commercialize the model. Compared to the closed-source models of GPT-5.4 and Claude Opus 4.6, GLM-5.1 offers more possibilities for the open-source community and small-to-medium enterprises.
V. Application Scenario Analysis and Selection Recommendations
5.1 Optimal Scenarios for Each Model
Based on the comparative analysis above, the three models each have their best-fit scenarios:
GLM-5.1 is most suitable for the following scenarios:
- Software engineering scenarios requiring handling of long-range, complex programming tasks
- Enterprises and developers with requirements for open source and autonomous control
- Automated code repair tasks that need to run for several hours continuously
- Natural language description-driven code development workflows
GPT-5.4 is suitable for the following scenarios:
- Comprehensive tasks requiring broad general knowledge support
- Applications with high requirements for general benchmarks like MMLU
- Projects deeply integrated into the OpenAI ecosystem
Claude Opus 4.6 is suitable for the following scenarios:
- Tasks requiring high-level mathematical reasoning and complex analysis
- Projects with strict requirements for code quality and best practices
- Exploratory tasks requiring deep thinking and reasoning processes
5.2 Developer Selection Guide
For developer selection advice in 2026, the industry generally believes:
- Gemini 3.1 Pro remains the cost-effective first choice for most workloads
- Opus 4.6 is more suitable for complex coding and agent-type tasks
- GPT-5.4 is recommended for parallel A/B testing before deciding on large-scale deployment
- GLM-5.1 has unique advantages in open-source and long-range Agent scenarios
VI. Technical Specifications and Commercial Considerations
6.1 Parameter Scale Comparison
| Model | Parameter Count | License |
|---|---|---|
| GLM-5.1 | 754 Billion | Apache 2.0 (Open Source) |
| GPT-5.4 | Not Disclosed | Proprietary |
| Claude Opus 4.6 | Not Disclosed | Proprietary |
GLM-5.1 is currently the only top-tier model that discloses detailed parameters and is fully open-source. Its scale of 754 billion parameters is unparalleled in the open-source community.
6.2 Cost-Benefit Analysis
The open-source nature of GLM-5.1 gives it a significant cost advantage in commercial deployment. Enterprises do not need to pay high API call fees and can deploy and optimize the model autonomously within their internal network environments. Simultaneously, the Apache 2.0 license permits commercial use, clearing legal obstacles for enterprise-level applications.
VII. Summary and Outlook
7.1 Core Conclusions
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Coding Capability: GLM-5.1 Leads — On real industrial code repair benchmarks like SWE-Bench Pro, GLM-5.1 surpassed GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%) with a score of 58.4%, becoming the current best choice in the Agentic Coding field.
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General Capability: GPT-5.4 Slightly Ahead — On general language understanding benchmarks like MMLU, GPT-5.4 Pro maintains a slight lead with a score of 87.3%.
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Reasoning Capability: Claude Opus 4.6 Leads — On specialized benchmarks like HumanEval (92.5%) and MATH (88.7%), Claude Opus 4.6 demonstrates profound expertise in complex reasoning tasks.
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Agent Capability: GLM-5.1's Unique Advantage — The 8-hour long-range task processing capability and 600-step long-range optimization are GLM-5.1's unique skills.
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Open Source Value: GLM-5.1 Stands Alone — The Apache 2.0 open-source license makes GLM-5.1 the most attractive choice for cost-sensitive enterprises and those requiring autonomous control.
7.2 Future Outlook
With the success of GLM-5.1, the position of domestic large models in the global AI competition is undergoing a fundamental shift. From former followers to now running neck-and-neck and even achieving partial supremacy, Zhipu AI's breakthrough proves China's technological strength in the field of large language models.
It is foreseeable that the large model competition in the second half of 2026 will become even more intense. OpenAI has announced that GPT-6 will be released on April 14, with performance expected to surge by 40%. Anthropic is also actively preparing its next-generation Claude model. In this endless race, who will ultimately reach the summit? Let's wait and see.
