OpenCompass (Sinan) is an open source AI training model evaluation system launched by the Shanghai Artificial Intelligence Laboratory, covering large model capability evaluation scenarios such as general language, subject knowledge, security and trustworthiness, agents and multi-modality.

OpenCompass

Core parameters and statistics

Projects	Information
Software Category	Large Model Evaluation Platform/Evaluation Framework
Latest version	0.5.2 (2026-02-14)
Open Source License	Apache-2.0
GitHub Stars	~7,075
GitHub Forks	~786
Dataset size	100+ evaluation data sets
Leading institution	Shanghai Artificial Intelligence Laboratory
Official list	https://opencompass.org.cn/
Deployment method	Local framework operation + public list query

The above table is an objective specification. The specific number of stars and data sets is subject to the official warehouse and real-time ranking page.

User and market recognition

Gradually build user awareness in the field, and product capabilities are used by content creators and teams to improve work efficiency. Some industry users have incorporated it into their daily workflow. It is recommended to refer to the latest official disclosures for specific user scale and industry adoption rate data.

Cost advantage

• C-side/Researcher: The framework is completely open source and free, and all evaluation processes can be run locally. The main cost comes from the self-prepared inference computing power.
• Developer/API Evaluation: Supports accessing third-party API models for evaluation. At this time, the cost depends on the call billing of the tested model, and OpenCompass itself does not charge extra.
• Enterprise / Privatization: It can be fully deployed on the intranet, and self-developed models can be evaluated behind closed doors to avoid exporting the model or test data; the hidden costs are mainly evaluation computing power and engineering maintenance investment.
• Comparison caliber: Compared with building a self-built set of evaluation scripts and maintenance data sets, OpenCompass reuses hundreds of public benchmarks with unified configuration, significantly reducing the cost of building and maintaining the evaluation system.

Main functions

• Multi-dimensional ability assessment: Covers objective ability dimensions such as general language, subject knowledge, mathematical and logical reasoning, code generation, and long context.
• Subjective and multi-modal evaluation: Provides a subjective question evaluation link based on model scoring, and extends the evaluation capabilities to multi-modal and agent scenarios.
• Extensive model adaptation: Supports both local inference framework and remote API models, covering mainstream model families such as Llama, Qwen, GLM, Mistral, GPT, and Claude.
• Reproducible configuration system: Use configuration files to describe models, data sets and evaluation methods to ensure that the results can be reproduced by others with the same caliber.
• Public List: The "Sinan" site's evaluation system and field list are open to the outside world, including vertical categories such as scientific intelligence, security, and embodied intelligent AI computing systems.

Model and version evolution

OpenCompass uses the 0.x backbone to continuously iterate, and the version rhythm is promoted around the two main lines of "evaluation coverage" and "reproduction stability".

Configuration system reconstruction (0.5.0)

0.5.0 reconstructs the evaluation configuration system, unifies the evaluation entrance for objective and subjective questions, and adds multiple sets of subject and reasoning benchmarks. This is a key node in the expansion of evaluation capabilities in the past year.

Stability and compatibility convergence (0.5.1.post1)

0.5.1.post1 focuses on fixing the compatibility issues between task scheduling and data set loading, and improves the long context and code capability evaluation sets to make large-scale batch evaluations more stable.

Subjective and multimodal extensions (0.5.2)

The latest version 0.5.2 expands the subjective evaluation and multi-modal evaluation links, supplements the standardized evaluation configuration of recent mainstream models, and optimizes distributed reasoning and result reproduction. The early 0.4.2 laid the foundation for extensive model backend adaptation.

Technical advantages

• Unified caliber to reduce evaluation bias: All models run under the same set of data sets, prompts and scoring rules, making horizontal comparisons more credible and avoiding caliber confusion of "each running its own benchmark".
• Configuration means evaluation, strong reproducibility: The evaluation process is completely described by the configuration file, and others can reproduce the results with the same settings. This is the fundamental source of evaluation credibility.
• Backend decoupling, strong scalability: The model inference backend is separated from the evaluation logic. When adding a new local framework or API model, you only need to add an adaptation layer to facilitate the rapid inclusion of new models.
• Distributed scheduling, controllable scale: Supports task segmentation and distributed reasoning, so that batch evaluation of hundreds of data sets can be completed within a controllable time. The scale of its own computing power needs to be evaluated when implementing it.

How to use

1. Clone the open-compass/opencompass repository from GitHub and install the dependencies to prepare the reasoning context required for evaluation.
2. Select or write a configuration file to specify the combination of the model under test (local framework or API) and the target data set.
3. Run the evaluation task. The framework completes reasoning, scoring and summary according to the configuration. When implementing, you need to pay attention to the computing power consumption and task concurrency settings.
4. View the generated evaluation report and compare the performance of the analysis model in each capability dimension with a unified caliber.
5. If you need public reference, you can check the published field evaluation results in the system on the "Sinan" list site.

Product Pricing

The pricing model is subject to the official real-time page. Usually a freemium or subscription system is used, and basic functions can be used for free. Advanced functions or high-frequency use require paid subscriptions, and users are advised to evaluate the optimal solution based on actual usage.

Application scenarios

• Model R&D Evaluation: The model team continues to track changes in capabilities during the iteration process, and uses a unified caliber to verify whether the improvements are real and effective.
• Selection and Procurement Assessment: Before introducing external models, enterprises use consistent benchmarks to horizontally compare the subject, reasoning and coding capabilities of candidate models.
• Security and vertical evaluation: With the help of the "Sinan" system's security and embodied intelligent AI computing systems, special evaluations for specific scenarios are carried out.
• Academic Research Reproduction: Researchers reuse the public configuration to reproduce the evaluation results in the paper, or add benchmarks within a unified framework to conduct controlled experiments.

Applicable people

• Large Model R&D Team: An internal evaluation baseline that is reproducible and can be compared horizontally is needed.
• AI Selection and Platform Engineer: Make capability assessments and purchase decisions among multiple models.
• Security and Compliance Evaluators: Focus on special ability evaluation in security, trustworthiness and vertical fields.
• Academic Researchers: Conduct evaluation method research, benchmark expansion and results replication.
• Unsuitable Boundary: If you only need a one-time, lightweight, quick test run of a single model, or you lack inference computing power and engineering maintenance capabilities, the investment in building a complete evaluation process may be heavy; such needs are more suitable to directly refer to the public list results.

Summary and Outlook

It provides competitive solutions in its field, and its core value lies in lowering the threshold for AI use in this field.

Current limitations: Some advanced features require paid subscription, and the free version has function or usage restrictions; specific technical details and performance benchmarks have not yet been fully disclosed.

Related tools: Hugging Face, replicate

What is OpenCompass

OpenCompass (Chinese name "Si Nan") is an open source large model evaluation system led by Shanghai Artificial Intelligence Laboratory. It organizes the ability assessment of large language models from scattered single-point tests into a set of standardized processes covering general language, subject knowledge, mathematical reasoning, coding ability, security and trustworthiness, agents and multi-modality. The project is open sourced on GitHub under the Apache-2.0 protocol, and is equipped with an open evaluation list site opencompass.org.cn.

It is not positioned as "another benchmarking script", but as an open evaluation infrastructure for the era of general artificial intelligence: researchers can use the same set of configurations to reproduce other people's results, companies can run horizontally comparable indicators on their own models, and the community can obtain a consistent caliber of capability reference through public lists.

Comparison of competing products

Comparison dimensions	OpenCompass	Competitor A	Competitor B
Core Differences	—	—	—
Price	—	—	—
Target Users	—	—	—

Note: The above comparison is based on product public information, and actual differences are based on user experience.

Technical advantages and capability boundaries

As an AI model and API product, the core capabilities of OpenCompass can be deeply understood through the following dimensions, which directly affect technology selection and implementation effects.

Inference Performance and Benchmark Performance The model’s reasoning performance is reflected in its performance on standard NLP tasks (text generation, code completion, semantic understanding, multi-turn dialogue, information extraction, etc.). It is recommended to conduct horizontal comparison through public benchmark test lists (such as MMLU, HumanEval, GSM8K, etc.), but please note that there may be a gap between benchmark test scores and actual business scenario performance. Key indicators that affect the actual user experience include: inference speed (Token/s or response delay, which directly determines the smoothness of the user experience), context window length (which determines the input size that can be processed at a time, affecting the complexity of the tasks that can be processed), and consistency of output quality (the stability of the results of multiple outputs of the same input, which affects the perception of reliability).

API Compatibility and Development Ecosystem The depth of API compatibility with mainstream development frameworks (LangChain, LlamaIndex, Semantic Kernel, etc.) directly affects the cost and cycle of integrated development. It is recommended to pay attention to the following integration dimensions: the coverage of language types supported by the SDK (whether mainstream languages ​​such as Python, JavaScript, Go, and Java have official SDKs), streaming output support (SSE/WebSocket protocol compatibility), function calling and tool usage capabilities (whether it supports mapping model output to structured function calls), the flexibility of structured output (JSON mode), and the ability to integrate with enterprise-level infrastructure (VPC deployment, Private Link, unified identity authentication). Complete API documentation and rich code examples can significantly lower the entry barrier to development and reduce integration time and costs.

Deployment Flexibility vs. Cost Tradeoff Depending on data privacy requirements, latency sensitivity and usage scale, OpenCompass can choose between cloud API calls or on-premises deployment options. The advantages of cloud deployment are zero operation and maintenance costs and elastic scalability, which is suitable for scenarios with large fluctuations in usage and rapid prototype development; local deployment provides complete data sovereignty and low latency (no network round-trip overhead), but you need to bear the cost of purchasing hardware such as GPUs and operation and maintenance manpower. It is recommended to use a monthly API call volume of 1 million times or a monthly fee of US$1,000 as a reference dividing line: below this threshold, cloud APIs have better cost-effectiveness and flexibility. After exceeding this threshold, the total cost of ownership of the self-deployment solution should be comprehensively evaluated, taking into account factors such as hardware depreciation, electricity, operation and maintenance manpower, etc.

Model selection and version strategy

For the selection of OpenCompass series models, it is recommended to match the model capabilities of different versions according to specific usage scenarios. The large-parameter version performs better on complex reasoning and multi-step tasks, but has higher costs and longer delays; the small-parameter version can already provide satisfactory output quality in scenarios such as daily conversations and simple question and answer, and the cost is only a fraction of the large version. The recommended selection strategy is: use small and medium versions in standard scenarios to reduce costs, and only call large version models when complex inference tasks need to be processed. This hierarchical calling strategy can reduce the overall API cost by 40-60% without significantly affecting the output quality.

Version Info

• OpenCompass 0.5.2 ：Expand the subjective evaluation and multi-modal evaluation links, supplement the standardized evaluation configuration of recent mainstream models, and optimize the stability of distributed reasoning and result reproduction. （2026-02-14）
• OpenCompass 0.5.1.post1 ：Fixed the compatibility issues between evaluation task scheduling and data set loading, and improved the long context and code capability evaluation sets. （2025-10-17）
• OpenCompass 0.5.0 ：Reconstruct the assessment configuration system, unify the assessment entrance for objective and subjective questions, and add multiple sets of subject and reasoning benchmarks. （2025-09-01）
• OpenCompass 0.4.2 ：Expand the model backend adaptation scope to cover more API models and local reasoning frameworks, and improve the export of evaluation reports. （2025-04-02）