LILT Launches Multilingual AI Leaderboard for Enterprise Agents

SAN FRANCISCO, Sept. 30, 2026 /CourierPR/ -- LILT, the leading agentic AI solution for enterprise, public sector, and Frontier labs, today unveiled AURORA, the industry's first Multilingual AI Leaderboard designed to measure frontier models on non-English agentic, multimodal, and socio-cultural tasks grounded in language and culture. Enterprises are increasingly deploying agents worldwide, but nearly every published measure of frontier AI progress relies on English-centric or translated benchmarks. This gap is now costing companies real-world consequences, as agents perform real actions in the real world, and performance in every language now carries commercial weight.

The launch of AURORA addresses this multilingual gap by testing models against LILT's multilingual benchmark suite, which features tasks designed and verified by native-language domain experts. These tasks represent real-world enterprise applications, such as software development, customer support, and complex workflows, each grounded in language, region, and culture. AURORA provides visibility across LILT's multilingual benchmarks, including:

- Multilingual Terminal-bench: Coding tasks representing challenges in software written for native-language users. - Multilingual τ³-bench: Multi-turn customer support in airline, telecom, retail, and banking sectors. - Multilingual MultiChallenge: Long-context instruction-following, memory, and self-coherence tasks. - Multilingual GAIA-v2-LILT: Agentic reasoning and tool use.

AURORA is developed and managed by LILT's Applied AI practice, a PhD-led research team with 10+ years' experience in multilingual AI. Their latest analysis shows model quality can differ substantially between languages. For example, in coding, GPT 5.5 performs best in Spanish, Claude Opus 5.5 wins in Japanese, while Muse Spark 1.3 leads in Serbian. The AURORA leaderboard is now live at aurora.lilt.com, where readers can compare models by language and task.

The industry is choosing models on a scoreboard that stops at English. This gap used to cost you an awkward translation. Now that agents are taking real actions in the real world, it costs you a wrong decision, and you find out from your users.

LILT's AURORA is a significant step forward in the field of AI, providing enterprises with a comprehensive and accurate evaluation of AI models' performance across different languages and tasks. As enterprises continue to deploy agents worldwide, AURORA will help ensure that these agents are performing optimally, leading to better decision-making and improved customer experiences.