Braintrust vs OpenCompass

Braintrust is an observability platform for teams running AI agents in production. It lets teams inspect agent traces and tool calls while tracking latency, cost, and quality in real time. Users can evaluate datasets, compare prompts and models, and score outputs with LLMs, code, or human reviewers. Discovery tools surface patterns in production traces to help investigate agent behavior, and the Loop agent can run evaluations, generate test cases, and iterate on prompts autonomously. Braintrust lists native SDKs for Python, TypeScript, Go, Ruby, C#, and more, and its MCP server lets coding agents query logs, run evaluations, and update prompts from an IDE. Security options listed include SSO, RBAC, and hybrid deployment; Braintrust states that it is SOC 2 Type II certified and GDPR and HIPAA compliant. Starter is free, with 1 GB processed data, 10,000 scores, and 14-day retention. Pro costs $249.00 per month; Enterprise pricing is custom.

OpenCompass is a free, open-source Python platform for evaluating large language models across knowledge, reasoning, coding, science, language, long-context and safety datasets. It describes support for models from OpenAI, Anthropic, Gemini, Qwen, GLM, DeepSeek and other providers, along with more than 100 datasets. Evaluations can use zero-shot, few-shot or chain-of-thought methods with standard or dialogue prompt templates. The system supports task division, distributed evaluation and data-parallel work across multiple GPUs. Configuration files record experiments, and results can be reported in real time. Its modular design can be extended with models, datasets, task-division strategies and cluster-management systems. OpenCompass evaluates API models as well as open-source models; installation documentation lists LMDeploy and vLLM inference backends and an API option. A VLMEvalKit integration supports multimodal dataset loading, OpenAI-compatible inference and VLMEvalKit metrics. It is intended for self-hosted use under the Apache-2.0 license. The README recommends Python 3.12, while pyext-backed code-evaluation datasets require Python 3.10. Some datasets and agent evaluations need extra dependencies that may conflict.