EvalScope is a one-stop large language model evaluation framework built by the ModelScope community, positioned as a streamlined and customizable efficient large model (LLM, VLM, AIGC) evaluation and performance benchmarking tool. Its core goal is to allow users to start model capability evaluation, inference performance stress testing, and result visualization with a single command.
In terms of use cases, EvalScope is suitable for evaluating multiple model types, including large language models (LLM), vision-language models (VLM), Embedding, Reranker, AIGC, etc., covering text, multimodal, and multi-domain scenarios. It comes with multiple industry-recognized evaluation benchmarks, such as MMLU, C-Eval, GSM8K, etc., and continuously adds a large number of benchmarks and features.
Main capabilities include:
- Comprehensive evaluation benchmarks: built-in MMLU, C-Eval, GSM8K, etc., and continuously expanding, covering multiple types such as text, multimodal, code, Agent, long context, OCR, and image quality.
- Multimodal and multi-domain support: supports model types such as LLM, VLM, Embedding, Reranker, AIGC.
- System One evaluation: evaluates text selection decision models through --eval-type systemone_api, supporting task descriptions, few-shot examples, and structured probability/confidence.
- Multi-backend integration: seamlessly integrates evaluation backends such as OpenCompass, VLMEvalKit, RAGEval.
- Agent evaluation mode: drives benchmarks (such as GSM8K, AIME, SWE-bench Agentic) in a controlled multi-turn AgentLoop, supports pluggable strategies, tools, and Docker sandboxes, and records the complete Agent Trace for each sample and visualizes it.
- Inference performance testing: provides a model service stress testing tool, supporting multiple performance metrics such as TTFT and TPOT.
- Interactive reports: provides a Web Dashboard for multi-dimensional model comparison, report overview, and detailed inspection.
- Arena mode: supports multi-model battles (Pairwise Battle), intuitively ranking and evaluating models.
- Highly extensible: developers can easily add custom datasets, models, and evaluation metrics.
In terms of usage, EvalScope provides Chinese documentation and English documentation, as well as a product website. It is officially described as "start with just one command." It supports switching evaluation methods through command-line parameters (such as --eval-type systemone_api, --eval-type anthropic_api), and can use Agent Skill to drive EvalScope with natural language for model evaluation, performance benchmarking, and result visualization. In addition, EvalScope supports multi-turn dialogue performance stress testing, Embedding and Rerank model service stress testing, and automatic tuning of model service SLA, etc.
Applicable scenarios include: large model capability evaluation, multimodal model evaluation, Agent capability evaluation, code evaluation, RAG evaluation, inference performance stress testing, model service comparison and ranking, model API compatibility verification, etc. EvalScope is used as the official benchmarking tool for TokenSpeed performance evaluation and is used in the SWE-smith benchmark testing pipeline.
In terms of limitations, the materials do not provide specific information such as pricing models, prices, platform restrictions, or performance guarantees. Some features (such as Docker sandboxes in Agent evaluation, external Agent Bridge, etc.) may depend on specific environments or third-party tools. Specific installation commands, supported operating systems, hardware requirements, and other details are not yet provided.
