AI Singapore Releases SEA-HELM: First Systematic LLM Benchmark for Southeast Asian Languages

## SEA-HELM: Evidence-Based LLM Selection for Southeast Asia

AI Singapore has released SEA-HELM (Southeast Asian Holistic Evaluation of Language Models), the first systematic benchmark evaluation of large language models specifically designed for Southeast Asian language capabilities. The benchmark fills a critical gap in the evidence available to APAC enterprises making LLM selection decisions: until SEA-HELM, there was no publicly available, rigorous comparison of commercially relevant LLMs on Southeast Asian language tasks.

### What SEA-HELM Measures

SEA-HELM evaluates 20 LLMs across 6 Southeast Asian languages:

- **Thai** (12 evaluation tasks) - **Vietnamese** (12 evaluation tasks) - **Bahasa Indonesia** (12 evaluation tasks) - **Malay** (10 evaluation tasks) - **Filipino (Tagalog)** (10 evaluation tasks) - **Tamil** (8 evaluation tasks)

Evaluation categories include natural language understanding, reading comprehension, machine translation quality, instruction following, mathematical reasoning, and coding — with all tasks in the target Southeast Asian language.

### Key Findings

**Finding 1: English performance does not predict Southeast Asian performance.** Models that lead English-language benchmarks (GPT-4o, Claude 3.7 Sonnet) maintain their lead in Southeast Asian languages, but the performance gap narrows significantly. GPT-4o averages 15–20% lower scores on Southeast Asian benchmarks versus English-language equivalents.

**Finding 2: Open-source models are competitive for certain languages.** Qwen3-72B and SEA-LION (AI Singapore's own multilingual model) outperform GPT-4o on Bahasa Indonesia and Malay tasks — the two languages with the most Southeast Asian-origin training data. For Thai and Vietnamese, proprietary models maintain an edge.

**Finding 3: Instruction-following quality degrades significantly in low-resource languages.** For languages with fewer internet-scale training examples (Filipino, Tamil), all evaluated models show 25–35% lower instruction-following quality than English. This has practical implications: Southeast Asian-language chatbots and AI assistants require more careful prompt engineering and human quality review.

**Finding 4: Translation quality varies widely by language pair.** AI-assisted translation quality (measured on business document translation tasks) is high for English↔Bahasa Indonesia and English↔Vietnamese, but significantly lower for English↔Thai and English↔Filipino — relevant for APAC enterprises using AI for multilingual content production.

### Implications for APAC Enterprises

**For customer service AI:** APAC enterprises deploying AI chatbots for Thai, Filipino, or Tamil-speaking customers should plan for lower automation rates and higher human escalation than English-language deployments — SEA-HELM data quantifies the expected quality delta.

**For document processing:** AI document processing accuracy for Southeast Asian-language documents requires specific evaluation against local document types and language variants — generic model evaluations from US/EU benchmarks do not apply.

**For model selection:** SEA-HELM provides the first data-driven basis for selecting between GPT-4o, Claude 3.7, Gemini 2.0, Qwen3, and SEA-LION for Southeast Asian language use cases. The results are use-case-specific — no single model leads across all languages and tasks.

### Access

SEA-HELM benchmark results, evaluation code, and methodology are published at the AI Singapore GitHub repository and the AISG research portal. The benchmark is intended to be a living evaluation, updated as new models are released.

### AIMenta Assessment

SEA-HELM is the most practically useful AI research output for APAC enterprise model selection decisions to emerge in 2025–2026. Any APAC organisation building AI systems for Southeast Asian-language customers should review the SEA-HELM results for their target language and use case before finalising model selection — and factor the expected English-vs-regional performance gap into their deployment design.