Innodata AI Benchmarks
Explore expert-built benchmarks that reveal how LLM and AI models perform across cultural contexts, long and multimodal interactions, high-risk safety domains, and other emerging capabilities. Compare results, inspect methodology, and turn model failures into better training, safety, and product decisions.
Expert-authored scenarios • Structured rubrics • Multimodal and multilingual evaluation • Actionable failure analysis
Beyond Standard Scores
Real AI systems rarely fail under one clean, isolated condition. Failures emerge when context grows, modalities interact, cultural expectations vary, safeguards are tested, and user intent changes over time.
Innodata benchmarks are built around realistic scenarios and case-specific evaluation criteria. They help model builders and enterprise AI teams understand not only whether a model failed, but where, why, and what to improve next.
The Portfolio
Public Results
Cultural and global alignment
Evaluate whether AI models can apply cultural knowledge—not merely recall facts—across languages, locales, and multimodal scenarios.
For global product, localization, responsible AI, research, and evaluation teams.
Public preview
Capability and interaction
Test model performance when long context, multimodal inputs, and multi-turn conversations combine—surfacing grounding drift, instruction loss, modality neglect, and structural failures.
For model research, product, agent, post-training, and evaluation teams.
Private Evaluation
Safety and security
Evaluate where model safeguards hold—and where they break—across realistic chemical, biological, radiological, nuclear, and explosives scenarios.
For trust and safety, model safety, governance, security, and risk teams.
What Makes Our Benchmarks Different
We evaluate models under the conditions that define actual use—not only isolated prompts or academic question sets.
Domain, language, and cultural specialists design and review scenarios grounded in real tasks, risks, and user contexts.
Evaluation criteria are tailored to each scenario, making nuanced capabilities and failures measurable and traceable.
Results identify specific failure modes and provide signals for model selection, fine-tuning, safety improvement, and product decisions.
Evaluation to Improvement
A score is useful only when it leads to a better model. Innodata connects benchmark evaluation with failure analysis, targeted mitigation data, fine-tuning, and re-evaluation—helping teams move from identifying weaknesses to improving performance.
You do not need a fully defined evaluation plan. Start with the question, capability, or risk you need to investigate, and our team can help shape the right approach.
Get Started
Innodata provides expert AI model evaluation and benchmarking services to help model builders and enterprise AI teams identify performance gaps, diagnose failure modes, and improve model performance.
