modelscope/evalscope

What it solves

EvalScope provides a unified, one-stop framework for evaluating Large Language Models (LLMs) and other frontier AI models. It eliminates the need to manually run disparate benchmarks, stress tests, and visualization tools by consolidating capability evaluation, inference performance testing, and result analysis into a single command-line interface.

How it works

EvalScope acts as an orchestration layer that integrates multiple evaluation backends (such as OpenCompass, VLMEvalKit, and RAGEval) and built-in benchmarks (like MMLU, C-Eval, and GSM8K). It supports evaluating models via online APIs (OpenAI-compatible) or local model weights. For agentic evaluations, it uses a controlled multi-turn AgentLoop with pluggable strategies and Docker sandboxes to record and visualize the model's reasoning trajectory.

Who it’s for

It is designed for AI developers and researchers who need to benchmark the accuracy, reasoning capabilities, and inference efficiency of their models across text, vision, audio, and multimodal domains.

Highlights

• Comprehensive Benchmarking: Built-in support for a vast array of industry-standard benchmarks across multiple modalities (LLM, VLM, Embedding, Reranker).
• Agent Evaluation Mode: Supports multi-turn agentic loops with full trace recording and visualization for benchmarks like SWE-bench.
• Performance Stress Testing: Tools to measure inference metrics such as Time to First Token (TTFT) and Time Per Output Token (TPOT).
• Interactive Dashboard: A React-based web interface for multi-dimensional model comparison and detailed prediction inspection.
• Arena Mode: Enables pairwise battles to rank models intuitively.
• Extensible Architecture: Allows developers to easily add custom datasets, models, and evaluation metrics.