MODEL EVALUATION

Turn model output into defensible evidence.

Evaluate LLMs, VLMs, AIGC and RAG workloads with benchmark metadata, sample reviews, scoring contracts and reports that retain context.

LLMVLMAIGCRAGOpenAI-compatible API
Abstract evaluation evidence path

BROAD MODEL COVERAGE

One interface for every evaluation layer.

Models · tasks · datasets · metrics

LLMLanguage models and custom benchmarks
VLMVision-language and multimodal tasks
AIGCText-to-image and image editing
RAGRetrieval and generation quality

From a dataset row to the report you share.

01Dataset

The selected dataset and version define what the run is allowed to measure.

Evaluation that survives scrutiny.

Transparent · reproducible · extensible

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Versioned semantics

Benchmark metadata publishes evaluation versions, so prompt, mapping, data or scoring changes can be visible.

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Judge contracts

Structured judge outputs fail closed when invalid; malformed replies do not quietly become scores.

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Portable artifacts

Predictions, reviews, reports and configs remain together for local inspection and dashboard drill-down.