EvalScope

EvalScope is an evaluation framework that assesses large model performance and runs inference stress tests.

EvalScope is a one-stop evaluation framework built by the ModelScope Community for large language models, vision language models, embedding models, rerankers, and AIGC. It supports model capability evaluation, inference performance stress testing, and result visualization. Users can run industry-recognized benchmarks like MMLU, C-Eval, and GSM8K, or utilize the agent evaluation mode with a Docker sandbox. It integrates multiple backends such as OpenCompass and VLMEvalKit. The framework provides an interactive web dashboard for multi-dimensional model comparison and report overview.

EvalScope at a glance

Best for: Software engineer, Researcher
Runs on: CLI, open source
Pricing: Free
Released: December 2023
Category: AI research tool
Website: github.com/modelscope/evalscope

Key features

Built-in multiple evaluation benchmarks
Multi-modal and multi-domain model support
Multi-backend integration with OpenCompass and VLMEvalKit
Agent evaluation mode with Docker sandbox
Inference performance stress testing tools
Interactive Web Dashboard for result visualization
Arena Mode for multi-model pairwise battles

Use it for

Evaluate large language model and vision language model capabilities
Run inference performance stress testing for model services
Compare multiple models using interactive web dashboards
Test agent performance in controlled multi-turn environments

Alternatives to EvalScope

VLMEvalKit — Evaluate large vision-language models across numerous benchmarks with a single command.
Lighteval — Evaluates LLMs across multiple backends with thousands of built-in tasks.
LMMs-Eval — Provides open research and tooling for multimodal model evaluation and benchmarks.
Evals Skills — It helps avoid common mistakes and pitfalls when building AI evaluation suites.
DeepEval — Provides over fifty research-backed metrics and pytest-native execution.
Rsi Eval — It supplies the evaluation boundary so methods can use existing benchmarks without reimplementing execution.
vLLM — Maximizes throughput with PagedAttention and continuous batching.
Eval Skills — Guides coding agents from first execution to validated graders and experiments.
AgentScope Java — Provides engineering scaffolding for reliable, long-running agent execution.
OpenVLHarness — Lifts six multimodal model backbones by 9.7 to 12.6 points across 22 benchmarks.
AgentScope — Offers triple-layer security with sandboxing and tool reviews.
LightLLM — It delivers high-speed inference and serving performance for large language models.

All EvalScope alternatives