llm

GLM4.7-Flash the new Local LLM king at 30B A3B ?

Luigi

• 3 min read

GLM4.7-Flash represents Z.ai's latest breakthrough in the 30B parameter class, delivering a Mixture-of-Experts (MoE) model that balances high performance with efficiency. Released in January 2026, this model has quickly established itself as a formidable contender in agentic coding and general reasoning tasks.

Technical Architecture

Model Specifications:

  • Parameters: 30B total, 3B activated (A3B MoE)
  • Architecture: Mixture-of-Experts with efficient routing
  • Context Window: 200K tokens
  • License: MIT (Open Source)
  • Developer: Z.ai (Zhipu AI)

The MoE design allows GLM4.7-Flash to maintain the computational efficiency of much smaller models while leveraging the knowledge capacity of a 30B parameter model. With only 3B parameters activated per inference, it offers significant memory and computational advantages over dense 30B models.

Performance Benchmarks

GLM4.7-Flash has demonstrated exceptional performance across multiple benchmarks, particularly excelling in agentic coding and reasoning tasks:

Key Benchmark Results:

  • SWE-bench Verified: 59.2% (Leading the 30B class)
  • τ²-Bench: 79.5% (Top position for agentic coding use)
  • AIME 25: 91.6%
  • GPQA: 75.2%
  • Humanity's Last Exam: 14.4%
  • BrowseComp: 42.8%

The model's performance on τ²-Bench is particularly noteworthy, as it demonstrates superior capabilities in dual-control conversational AI scenarios that simulate real-world technical support interactions.

Deployment and Accessibility

Local Deployment Options

GLM4.7-Flash supports multiple inference frameworks for local deployment:

vLLM Configuration:

vllm serve zai-org/GLM-4.7-Flash \
     --tensor-parallel-size 4 \
     --speculative-config.method mtp \
     --speculative-config.num_speculative_tokens 1 \
     --tool-call-parser glm47 \
     --reasoning-parser glm45 \
     --enable-auto-tool-choice \
     --served-model-name glm-4.7-flash

SGLang Configuration:

python3 -m sglang.launch_server \
  --model-path zai-org/GLM-4.7-Flash \
  --tp-size 4 \
  --tool-call-parser glm47  \
  --reasoning-parser glm45 \
  --speculative-algorithm EAGLE \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --mem-fraction-static 0.8 \
  --served-model-name glm-4.7-flash \
  --host 0.0.0.0 \
  --port 8000

Quantization Challenges

The quantized versions available through Unsloth's GGUF releases face significant limitations:

Current Issues:

  • Reasoning Effort Configuration: Not supported in quantized versions
  • Output Quality: Recent llama.cpp updates (January 21, 2026) addressed looping and poor output issues
  • Parameter Tuning: Requires specific settings for optimal performance:
    • General use: --temp 1.0 --top-p 0.95
    • Tool-calling: --temp 0.7 --top-p 1.0
    • Repeat penalty: Disabled or set to 1.0
    • Min-p: 0.01 (llama.cpp default is 0.05)

Competitive Analysis

Strengths

  • Agentic Coding Excellence: Top performer in τ²-Bench evaluations
  • Efficient MoE Architecture: 3B activated parameters reduce computational overhead
  • Open Source: MIT license allows unrestricted commercial use
  • Comprehensive Tool Support: Native function calling and reasoning capabilities
  • Strong Benchmark Performance: Consistently high scores across coding and reasoning tasks

Limitations

  • Quantization Issues: Current GGUF versions lack reasoning effort configuration
  • Hardware Requirements: Despite MoE efficiency, still requires substantial VRAM for optimal performance
  • Ecosystem Maturity: Newer model with less community tooling compared to established alternatives

Comparison with Alternatives

vs Alibaba-NLP_Tongyi-DeepResearch-30B-A3B-IQ4_NL:

  • Performance: Tongyi-DeepResearch performs better in general benchmarks
  • Speed: GLM4.7-Flash is faster despite similar MoE architecture
  • Specialization: GLM4.7-Flash excels in agentic coding, Tongyi in general research

vs Qwen3-4B-Instruct-2507-Q4_K_M:

  • Hardware Efficiency: Qwen3-4B is superior for low-VRAM and CPU-only configurations
  • Speed: Qwen3-4B offers faster inference on constrained hardware
  • Capability: GLM4.7-Flash provides significantly higher reasoning and coding capabilities

vs Qwen3-Coder-30B-A3B-Instruct-Q4_K_M:

  • Hardware Requirements: Qwen3-Coder is more efficient for low-VRAM scenarios
  • Specialization: Both excel at coding, but GLM4.7-Flash has broader agentic capabilities
  • Performance: GLM4.7-Flash leads in agentic benchmarks, Qwen3-Coder in pure coding tasks

Use Case Recommendations

Ideal For:

  • Agentic Workflows: Complex multi-step reasoning and tool usage
  • Coding Assistants: Advanced code generation and debugging
  • Research Applications: Tasks requiring deep reasoning and analysis
  • Production Deployment: Scenarios where performance justifies hardware requirements

Consider Alternatives For:

  • Resource-Constrained Environments: Low-VRAM or CPU-only deployments
  • Real-Time Applications: Scenarios requiring maximum inference speed
  • General Chatbots: Applications not requiring advanced reasoning capabilities

Future Outlook

GLM4.7-Flash's strong performance in agentic benchmarks suggests significant potential for continued development. The open-source nature and MIT license position it well for community-driven improvements and enterprise adoption.

Key areas to watch:

  • Quantization Improvements: Future GGUF releases may address reasoning configuration limitations
  • Hardware Optimization: Continued work on efficient inference for diverse hardware configurations
  • Ecosystem Development: Growing community support and tooling integration

Conclusion

GLM4.7-Flash represents a significant advancement in the 30B parameter class, particularly for agentic coding and reasoning tasks. While current quantization versions present challenges for resource-constrained deployments, the model's benchmark performance and open-source availability make it a compelling choice for applications where advanced reasoning capabilities are paramount.

The model's success in Artificial Analysis's τ²-Bench evaluations underscores its strength in real-world agentic scenarios, positioning it as a leading option for developers and organizations seeking powerful, open-source AI capabilities.

As the ecosystem matures and quantization improvements address current limitations, GLM4.7-Flash is well-positioned to become a cornerstone model for advanced agentic applications and AI-powered development workflows.