Practical overview
When it fits
Use Evalscope when model assessment needs to cover both capability and serving performance, or when results need comparison and visual inspection rather than a single raw score.
Who it is for
- AI developers comparing model capabilities
- Teams monitoring changes between model releases
- Engineers testing inference performance
- Researchers or practitioners evaluating multimodal and RAG systems
Documented uses
- Run standardized evaluations on language and multimodal models.
- Benchmark API-accessible models against supported datasets.
- Stress-test model services using inference metrics such as TTFT and TPOT.
- Compare models and inspect evaluation results through interactive reports.
- Extend evaluations with custom datasets, models and metrics.
Requirements & limits
The project is publicly available and open source under the Apache-2.0 license. The documented setup uses pip and requires Python 3.10 or later. API-based evaluation is demonstrated with API URL and API key options; endpoint protocols and authentication details are not specified.
- Python 3.10 or later is required by the documented package badge.
- The framework supports both capability evaluation and inference-performance testing.
- Built-in benchmarks include MMLU, C-Eval and GSM8K.
- Supported model categories include LLM, VLM, embedding, reranker and AIGC systems.
- The project integrates OpenCompass, VLMEvalKit and RAGEval.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Evalscope for command-line evaluation
Set up Evalscope locally with pip so it can be used as a command-line evaluation tool.
Before you start
- Python 3.10 or later
- pip
Steps
Install the package with pip.
Use the Evalscope command-line interface for an evaluation workflow.
Expected outcome
Evalscope is installed for local command-line use.
Guide limitations
- The supplied documentation covers installation but does not provide operating-system-specific packaging details.
- Installation alone does not document a completed evaluation run.
Evaluate an API-accessible model on a benchmark
Run the documented API-based evaluation example against a model endpoint using the GSM8K dataset.
Before you start
- Evalscope installed with pip
- An API URL for the model
- An API key
- The GSM8K dataset
Steps
Install Evalscope with pip.
Provide the model name, API URL and API key to the evaluation command.
Select the openai_api evaluation type and GSM8K dataset.
Set the example limit to 5 and start the evaluation.
Expected outcome
The documented command starts an evaluation of an API-accessible model on GSM8K with a five-example limit.
Guide limitations
- The example does not specify endpoint protocols or authentication details beyond the API URL and API key options.
Compare options for Model inference and serving
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Evalscope | Run standardized evaluations on language and multimodal models. | CLI, Cloud, Self-hosted, Server | The project is publicly available and open source under the Apache-2.0 license. The documented setup uses pip and requires Python 3.10 or later. API-based evaluation is demonstrated with API URL and API key options; endpoint protocols and authentication details are not specified. |
| FMBench | Compare inference latency and transactions per minute across model-serving configurations. | CLI, Cloud | FMBench is distributed as a Python package and used from the command line. The project is published under the MIT-0 license. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 1 October 2026
- Original website title
- modelscope/evalscope · Official homepage title · retained in its source language