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Claw-Eval: The Real-World AI Agent Benchmark Challenging Traditional Leaderboards

Claw-Eval benchmark released on HuggingFace evaluates AI models on real-world agent tasks across PinchBench, OfficeQA, OneMillion-Bench, Finance Agent, and Terminal-Bench 2.0. Xiaomi MiMo-V2.5-Pro (1T params) ranked #1, Zai GLM5.1 (754B) #2, Xiaomi MiMo-V2.5 (310B) #3. Key finding: DeepSeek v4 flash at 210B parameters achieves performance comparable to models 4x its size, suggesting significant inference efficiency gains. The benchmark provides task-specific model selection guidance for agent frameworks.

NathanMonday, May 11, 2026Original source
#Benchmark#Agent#DeepSeek#Efficiency#OpenSource

Claw-Eval: The Real-World AI Agent Benchmark Challenging Traditional Leaderboards

Summary

Claw-Eval is a new multi-task benchmark evaluating AI agents on real-world scenarios across PinchBench, OfficeQA, OneMillion-Bench, Finance Agent, and Terminal-Bench 2.0. Xiaomi MiMo-V2.5-Pro (1T params) tops the rankings, but the headline finding is DeepSeek v4 flash—a 210B parameter model—achieving performance comparable to models 4× its size, suggesting breakthrough inference efficiency for agent deployments.

Integration Strategy

When to Use This?

Claw-Eval should inform your model selection process if you're building:

  1. Autonomous agent systems requiring multi-tool orchestration
  2. Terminal automation for DevOps, script generation, or system administration
  3. Financial analysis pipelines with document and data processing requirements
  4. Office productivity tools handling documents, emails, and structured data
  5. Long-horizon planning tasks where cumulative errors compound

The benchmark is less relevant for:

  • Single-turn Q&A without tool use
  • Purely creative writing tasks
  • Tasks with well-defined deterministic outputs

How to Integrate?

Direct Evaluation Path:

  1. Access the dataset at huggingface.co/datasets/claw-eval/Claw-Eval
  2. Run evaluation against your fine-tuned or deployed model
  3. Compare against published leaderboard results

Model Selection Guidance:

  • For maximum capability: Xiaomi MiMo-V2.5-Pro (1T params) — highest raw performance
  • For efficiency-critical deployments: DeepSeek v4 flash — best performance-per-parameter ratio
  • For balanced tradeoffs: GLM5.1 or MiMo-V2.5 at 310B-754B range

Framework Compatibility:

  • Compatible with standard HuggingFace evaluation pipelines
  • Supports models exportable to HuggingFace format
  • Integration with agent frameworks (LangChain, LlamaIndex, custom) requires standard API wrapping

Compatibility Considerations

Note: Specific framework compatibility details are not confirmed in available documentation. The following represents reasonable inferences based on HuggingFace dataset format.

  • PyTorch: Primary framework expected
  • Transformers: Standard AutoModel compatibility
  • vLLM/SGLang: Flash attention support likely enables efficient serving of DeepSeek v4 flash
  • CUDA requirements: Not specified; assume compute requirements scale with parameter count

Source: @huggingface Reference: Claw-Eval Dataset Published: 2026-05-11 DevRadar Analysis Date: 2026-05-11