What are the Best Open Source LLMs for Indonesian?
The best open source LLMs for Indonesian are large language models specifically designed or trained to understand, process, and generate Indonesian text with high accuracy. These models leverage deep learning architectures and multilingual training data to handle Indonesian language nuances, grammar, and context. They enable developers and creators to build chatbots, translation systems, content generation tools, and more with unprecedented linguistic accuracy. Open source Indonesian LLMs foster collaboration, accelerate innovation in Southeast Asian markets, and democratize access to powerful language AI, enabling applications from digital content creation to enterprise-scale language processing solutions.
Qwen/Qwen3-235B-A22B
Qwen3-235B-A22B is the latest large language model in the Qwen series, featuring a Mixture-of-Experts (MoE) architecture with 235B total parameters and 22B activated parameters. This model uniquely supports seamless switching between thinking mode and non-thinking mode, with superior multilingual capabilities covering over 100 languages and dialects including strong Indonesian language support.
Qwen/Qwen3-235B-A22B: Premier Multilingual Reasoning Model
Qwen3-235B-A22B is the latest large language model in the Qwen series, featuring a Mixture-of-Experts (MoE) architecture with 235B total parameters and 22B activated parameters. This model uniquely supports seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue). It demonstrates significantly enhanced reasoning capabilities, superior human preference alignment in creative writing, role-playing, and multi-turn dialogues. The model excels in agent capabilities for precise integration with external tools and supports over 100 languages and dialects with strong multilingual instruction following and translation capabilities, making it ideal for Indonesian language tasks.
Pros
- Supports over 100 languages including Indonesian with excellent translation capabilities.
- MoE architecture with 235B parameters for powerful performance.
- Dual-mode operation for both reasoning and general dialogue.
Cons
- Higher pricing at SiliconFlow ($1.42/M output tokens).
- Requires significant computational resources for deployment.
Why We Love It
- It delivers state-of-the-art multilingual performance with exceptional Indonesian language understanding, combining powerful reasoning with efficient dialogue capabilities in a single model.
meta-llama/Meta-Llama-3.1-8B-Instruct
Meta Llama 3.1-8B-Instruct is a multilingual large language model developed by Meta, optimized for multilingual dialogue use cases. Trained on over 15 trillion tokens, this 8B instruction-tuned model outperforms many open-source chat models and provides excellent Indonesian language support with cost-effective performance.
meta-llama/Meta-Llama-3.1-8B-Instruct: Efficient Multilingual Model
Meta Llama 3.1 is a family of multilingual large language models developed by Meta, featuring pretrained and instruction-tuned variants in 8B, 70B, and 405B parameter sizes. This 8B instruction-tuned model is optimized for multilingual dialogue use cases and outperforms many available open-source and closed chat models on common industry benchmarks. The model was trained on over 15 trillion tokens of publicly available data, using techniques like supervised fine-tuning and reinforcement learning with human feedback to enhance helpfulness and safety. Llama 3.1 supports text and code generation, with a knowledge cutoff of December 2023, and provides strong Indonesian language capabilities at an accessible price point on SiliconFlow.
Pros
- Excellent multilingual support including Indonesian language.
- Cost-effective with SiliconFlow pricing at $0.06/M tokens.
- Trained on 15 trillion tokens for robust language understanding.
Cons
- Smaller parameter size may limit complex reasoning tasks.
- Knowledge cutoff at December 2023 may miss recent Indonesian content.
Why We Love It
- It offers the perfect balance of Indonesian language performance and cost-efficiency, making advanced multilingual AI accessible to developers and businesses of all sizes.
Qwen/Qwen3-8B
Qwen3-8B is the latest 8.2B parameter model in the Qwen series with unique dual-mode capabilities. It supports seamless switching between thinking and non-thinking modes, demonstrates enhanced reasoning capabilities, and excels in over 100 languages including Indonesian with strong instruction following and translation capabilities.
Qwen/Qwen3-8B: Versatile Reasoning Model for Indonesian
Qwen3-8B is the latest large language model in the Qwen series with 8.2B parameters. This model uniquely supports seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue). It demonstrates significantly enhanced reasoning capabilities, surpassing previous QwQ and Qwen2.5 instruct models in mathematics, code generation, and commonsense logical reasoning. The model excels in human preference alignment for creative writing, role-playing, and multi-turn dialogues. Additionally, it supports over 100 languages and dialects with strong multilingual instruction following and translation capabilities, making it ideal for Indonesian language applications at an affordable SiliconFlow price.
Pros
- Dual-mode operation for reasoning and general dialogue in Indonesian.
- Supports over 100 languages with strong Indonesian capabilities.
- Cost-effective at $0.06/M tokens on SiliconFlow.
Cons
- Smaller 8B parameter size compared to flagship models.
- May require mode switching for optimal task performance.
Why We Love It
- It combines advanced reasoning capabilities with excellent Indonesian language support in a compact, affordable package perfect for diverse applications from chatbots to content generation.
Indonesian LLM Model Comparison
In this table, we compare 2025's leading open source LLMs for Indonesian language tasks, each with unique strengths. For enterprise-scale multilingual applications, Qwen3-235B-A22B provides the most comprehensive capabilities. For cost-effective deployment, Meta-Llama-3.1-8B-Instruct offers excellent value, while Qwen3-8B delivers versatile reasoning with strong Indonesian support. This side-by-side view helps you choose the right model for your Indonesian language AI goals based on performance, pricing from SiliconFlow, and specific capabilities.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Strength |
|---|---|---|---|---|---|
| 1 | Qwen/Qwen3-235B-A22B | Qwen3 | Multilingual Chat | $1.42/M (out) $0.35/M (in) | 100+ languages with reasoning |
| 2 | meta-llama/Meta-Llama-3.1-8B-Instruct | meta-llama | Multilingual Chat | $0.06/M tokens | Cost-effective multilingual |
| 3 | Qwen/Qwen3-8B | Qwen3 | Reasoning & Multilingual | $0.06/M tokens | Dual-mode reasoning |
Frequently Asked Questions
Our top three picks for Indonesian language LLMs in 2025 are Qwen/Qwen3-235B-A22B, meta-llama/Meta-Llama-3.1-8B-Instruct, and Qwen/Qwen3-8B. Each of these models stood out for their multilingual capabilities, strong Indonesian language support, and unique approaches to solving challenges in language understanding, generation, and reasoning tasks specific to Indonesian contexts.
Our analysis shows different leaders for specific needs. For enterprise applications requiring the highest quality Indonesian language understanding and generation, Qwen3-235B-A22B is the top choice with its 100+ language support and advanced reasoning. For developers seeking the most cost-effective solution, meta-llama/Meta-Llama-3.1-8B-Instruct offers excellent Indonesian capabilities at just $0.06/M tokens on SiliconFlow. For applications requiring both reasoning and dialogue in Indonesian, Qwen3-8B provides the best balance with its unique dual-mode operation.