Assistant Benchmark

CommerceAgentBench is a benchmark developed and maintained by the Accio team at Alibaba International for evaluating whether AI agents can complete long-horizon business workflows in high-fidelity, stateful, reproducible replicas of online services. Its tasks cover browser operations, native-style command-line tools, file and spreadsheet production, API and MCP workflows, public-web research, supplier analysis, product publishing, logistics, storefront configuration, and other commerce operations. Each task runs in a fresh container and is graded by a deterministic or LLM-assisted verifier. The benchmark uses local mock services representing commerce, SaaS, messaging, document, and operational systems; runs preserve the resolved configuration, agent trajectory, verifier result, artifacts, logs, and container metadata for auditing and reproducibility. The repository describes 107 tasks spanning CLI, browser, file, and API/MCP interfaces, with text-only, browser-text-capable, and vision-required capability slices. The project was previously called RealReplicaBench.

ai-evaluation-framework is an open-source framework by Dreamers Inc. for benchmarking model-based solutions by accuracy, p95 latency, and estimated cost against user-defined ground-truth cases. Each task specifies fields and a scoring rule per field, including exact identifiers, normalized dates, numeric tolerances, containment, token overlap, and expected-missing values; cases can also provide alternate accepted renderings. It runs models from OpenAI, Anthropic, or an OpenAI-compatible gateway, compares results in a table, emits detailed JSON reports, and caches responses by model, task, and case so scoring rules can be changed without another model call. The repository uses document-field extraction as its worked example but accepts arbitrary text inputs such as OCR output, transcripts, and PDF text. A separate feedback server collects thumbs-up or thumbs-down responses with required comments, stores append-only JSONL data, and exposes statistics, exports, and candidate cases for human review; it does not train models. The repository includes tests using scripted mock models and is licensed under Apache-2.0.

Terminal Bench 4 is a benchmark for comparing coding-model performance across tasks with different difficulty levels.

An independent platform for evaluating and comparing generative AI models. It publishes third-party assessments and comparative data on model capabilities and performance.

VI codebench is a coding benchmark that measures how well AI models turn natural-language prompts into full-stack web applications. It is associated with VALS, an AI evaluation company that develops continuously evolving assessments for measuring model performance before release and in practical coding workflows.