GLM-4.7: Math Reasoning Breakthrough from Zhipu AI
95.7% on AIME 2025, surpassing GPT-5.1 High and Gemini 3.0 Pro.
Zhipu AI's 358B parameter Mixture-of-Experts model introduces enhanced "Interleaved Thinking" for complex reasoning tasks. With MIT licensing and Claude Code integration at 1/7th the cost of proprietary alternatives, GLM-4.7 represents a significant advancement in accessible mathematical AI.
Key Finding: Open-Source Math Reasoning Leadership
GLM-4.7 achieves 95.7% on AIME 2025, edging out GPT-5.1 High (94.0%) and matching or exceeding Gemini 3.0 Pro (95.0%) on mathematical reasoning benchmarks. This marks the first time an MIT-licensed model has led competitive math benchmarks.
Technical Specifications
GLM-4.7 employs a depth-over-width architecture strategy, opting for fewer experts with more layers compared to other MoE models. This design choice prioritizes reasoning depth over parallel specialization.
Context Window Advantage
The 200K input / 128K output context window enables processing of extensive mathematical proofs and multi-step problem chains without truncation. This is particularly valuable for competition mathematics where problems build on previous solutions.
Benchmark Results
GLM-4.7 demonstrates consistent leadership across mathematical reasoning benchmarks, with particularly strong performance on competition-level problems.
| Benchmark | GLM-4.7 | GPT-5.1 High | Gemini 3.0 Pro |
|---|---|---|---|
| AIME 2025 | 95.7% | 94.0% | 95.0% |
| HMMT Feb 2025 | 97.1% | - | - |
| HLE (with Tools) | 42.8% | 42.7% | - |
| LiveCodeBench-v6 | 84.9% | - | - |
Generation-over-Generation Gains
Compared to GLM-4.6, the improvements represent substantial advances in both reasoning quality and tool-augmented problem solving.
How Interleaved Thinking Works
GLM-4.7's core innovation is its "Interleaved Thinking" mechanism, which allows the model to dynamically balance between fast intuitive responses and slower deliberative reasoning during inference.
Turn-Level Thinking Control
Unlike models that commit to either fast or slow thinking for an entire conversation, GLM-4.7 can adjust its reasoning depth at each turn. This enables:
Quick responses for straightforward queries. Lower latency, suitable for simple arithmetic or factual recall.
Extended reasoning chains for complex proofs. Higher accuracy on multi-step problems at the cost of increased latency.
Speed/Accuracy Tradeoff Control
Developers can explicitly control the thinking depth through API parameters, allowing optimization for different use cases:
# Example: Adjusting thinking depth via API
response = client.chat.completions.create(
model="glm-4.7",
messages=[{"role": "user", "content": problem}],
extra_body={
"thinking_depth": "deep", # or "fast", "balanced"
"max_thinking_tokens": 8192
}
)Note: Actual API parameters may vary. Consult Zhipu AI documentation for current implementation.
Depth-Over-Width Architecture
GLM-4.7 departs from the trend of increasing expert count in MoE models. Instead, it uses fewer experts with more layers, prioritizing sequential reasoning depth:
- -Loss-free balance routing ensures efficient expert utilization without gradient collapse
- -Deeper layer stack enables more sophisticated intermediate representations
- -Reduced expert switching overhead improves inference efficiency for sequential reasoning
Competitive Landscape: GLM-4.7 vs GPT-5 vs Claude
The December 2025 reasoning model landscape has become increasingly competitive, with multiple frontier-class models achieving similar performance on mathematical benchmarks.
vs GPT-5.1 High
GLM-4.7 Advantages
- - 1.7 percentage points higher on AIME 2025
- - MIT license vs proprietary
- - Approximately 1/7th API cost
- - Self-hostable for sensitive workloads
GPT-5.1 High Advantages
- - Broader benchmark coverage
- - Established API stability
- - Multimodal capabilities
- - Larger ecosystem of integrations
vs Gemini 3.0 Pro
GLM-4.7 Advantages
- - 0.7 percentage points higher on AIME 2025
- - Open weights for research
- - Turn-level thinking control
- - No vendor lock-in
Gemini 3.0 Pro Advantages
- - Native multimodal understanding
- - Google ecosystem integration
- - Longer context in some configurations
- - Stronger on mixed-modality math
vs Claude 4 Opus
GLM-4.7 Advantages
- - Stronger on pure mathematical reasoning
- - Open source with MIT license
- - Competition math specialization
- - Lower per-token cost
Claude 4 Opus Advantages
- - Superior on agentic coding tasks
- - Better instruction following
- - Stronger on long-form analysis
- - More consistent output formatting
Recommendations for Math-Heavy Workloads
Based on benchmark performance and architectural characteristics, here are practical guidelines for deploying GLM-4.7 in production math applications.
Recommended Use Cases
Competition Mathematics
AIME, AMC, HMMT, Putnam-style problems. GLM-4.7's 95%+ accuracy on competition benchmarks makes it the current leader for this domain.
Educational Platforms
Step-by-step solution generation for tutoring systems. The interleaved thinking provides detailed reasoning traces.
Research Assistants
Mathematical proof verification and exploration. The 200K context enables processing of lengthy proofs.
Code with Heavy Algorithms
LiveCodeBench-v6 score of 84.9% indicates strong performance on algorithmic coding challenges.
Consider Alternatives For
Multimodal Math
Problems involving diagrams, charts, or images. Consider Gemini 3.0 Pro or GPT-5 Vision for visual mathematical reasoning.
General-Purpose Coding
SWE-bench style tasks. MiniMax-M2.1 or Claude 4 Sonnet demonstrate stronger performance on repository-level code tasks.
Deployment Considerations
- -Self-hosting requirements: 358B parameters requires significant GPU memory. Expect 8x A100-80GB or equivalent for full precision inference.
- -API availability: Zhipu AI provides hosted API access with Claude Code integration at approximately 1/7th the cost of GPT-5.
- -Latency considerations: Deep thinking mode increases response time. Use fast mode for simple queries and reserve deep mode for complex proofs.
- -Context management: The 128K output limit means very long derivations may require chunking strategies.
Summary
GLM-4.7 represents a significant milestone: the first MIT-licensed model to lead mathematical reasoning benchmarks. Its 95.7% AIME 2025 score, combined with accessible licensing and competitive pricing, makes it a compelling choice for math-intensive applications.
The interleaved thinking architecture offers practical advantages for production systems, allowing developers to optimize the speed/accuracy tradeoff at runtime rather than model selection time.
For teams building educational technology, research tools, or algorithm-heavy applications, GLM-4.7 warrants serious evaluation alongside proprietary alternatives.