What are Open Source LLMs for Information Retrieval & Semantic Search?
Open source LLMs for information retrieval and semantic search are specialized large language models designed to understand, process, and retrieve relevant information from vast text corpora based on semantic meaning rather than just keyword matching. Using advanced deep learning architectures and long-context capabilities, these models can comprehend complex queries, understand document relationships, and deliver highly accurate search results. They enable developers and organizations to build intelligent search systems, knowledge bases, and retrieval-augmented generation (RAG) applications that understand user intent and context. These models foster innovation, democratize access to powerful semantic search technology, and enable a wide range of applications from enterprise document search to customer support systems.
Qwen3-30B-A3B-Instruct-2507
Qwen3-30B-A3B-Instruct-2507 is the updated version of the Qwen3-30B-A3B non-thinking mode. It is a Mixture-of-Experts (MoE) model with 30.5 billion total parameters and 3.3 billion activated parameters. This version features key enhancements, including significant improvements in general capabilities such as instruction following, logical reasoning, text comprehension, mathematics, science, coding, and tool usage. Its capabilities in long-context understanding have been enhanced to 256K, making it ideal for information retrieval and semantic search applications.
Qwen3-30B-A3B-Instruct-2507: Enhanced Long-Context Retrieval
Qwen3-30B-A3B-Instruct-2507 is the updated version of the Qwen3-30B-A3B non-thinking mode. It is a Mixture-of-Experts (MoE) model with 30.5 billion total parameters and 3.3 billion activated parameters. This version features key enhancements, including significant improvements in general capabilities such as instruction following, logical reasoning, text comprehension, mathematics, science, coding, and tool usage. It also shows substantial gains in long-tail knowledge coverage across multiple languages and offers markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation. Furthermore, its capabilities in long-context understanding have been enhanced to 256K, making it exceptionally well-suited for information retrieval and semantic search tasks that require processing large documents and maintaining contextual coherence across extensive text.
Pros
- Enhanced long-context understanding up to 256K tokens.
- Efficient MoE architecture with only 3.3B active parameters.
- Superior text comprehension and instruction following.
Cons
- Non-thinking mode only, no reasoning chain output.
- May require fine-tuning for domain-specific retrieval tasks.
Why We Love It
- It delivers exceptional long-context understanding with an efficient MoE architecture, making it perfect for processing large document collections and complex semantic search queries at scale.
GLM-4-32B-0414
GLM-4-32B-0414 is a new generation model in the GLM family with 32 billion parameters. Its performance is comparable to OpenAI's GPT series and DeepSeek's V3/R1 series, and it supports very user-friendly local deployment features. The model achieves exceptional results in search-based Q&A and report generation, making it ideal for information retrieval applications. It has been enhanced for instruction following and function calling using advanced reinforcement learning techniques.
GLM-4-32B-0414: Search-Optimized Performance
GLM-4-32B-0414 is a new generation model in the GLM family with 32 billion parameters. Its performance is comparable to OpenAI's GPT series and DeepSeek's V3/R1 series, and it supports very user-friendly local deployment features. GLM-4-32B-Base-0414 was pre-trained on 15T of high-quality data, including a large amount of reasoning-type synthetic data, laying the foundation for subsequent reinforcement learning extensions. In the post-training stage, in addition to human preference alignment for dialogue scenarios, the team enhanced the model's performance in instruction following, engineering code, and function calling using techniques such as rejection sampling and reinforcement learning, strengthening the atomic capabilities required for agent tasks. GLM-4-32B-0414 achieves exceptional results in areas such as search-based Q&A and report generation, making it a powerful choice for information retrieval and semantic search systems. On several benchmarks, its performance approaches or even exceeds that of larger models.
Pros
- Exceptional performance in search-based Q&A tasks.
- Strong instruction following and function calling capabilities.
- User-friendly local deployment options.
Cons
- Context length limited to 33K tokens.
- Requires significant computational resources for optimal performance.
Why We Love It
- It combines GPT-level performance with enhanced search-based Q&A capabilities, delivering accurate, context-aware retrieval results while maintaining cost-effective deployment options.
Meta-Llama-3.1-8B-Instruct
Meta Llama 3.1-8B-Instruct is a multilingual large language model optimized for dialogue use cases, trained on over 15 trillion tokens of publicly available data. Despite its compact 8B parameter size, it outperforms many available open-source and closed chat models on common industry benchmarks. Its efficient architecture and strong text comprehension capabilities make it an excellent choice for lightweight information retrieval and semantic search applications.
Meta-Llama-3.1-8B-Instruct: Efficient Semantic Understanding
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. Its compact size combined with strong performance makes it ideal for resource-constrained environments requiring efficient information retrieval and semantic search capabilities.
Pros
- Compact 8B parameter size for efficient deployment.
- Strong multilingual capabilities across diverse languages.
- Trained on over 15 trillion tokens of high-quality data.
Cons
- Smaller context window of 33K tokens.
- Knowledge cutoff limited to December 2023.
Why We Love It
- It delivers enterprise-grade semantic understanding and retrieval performance in a lightweight 8B parameter package, making it perfect for cost-effective, high-throughput search applications.
LLM Comparison for Information Retrieval & Semantic Search
In this table, we compare 2025's leading open source LLMs for information retrieval and semantic search, each with unique strengths. Qwen3-30B-A3B-Instruct-2507 excels in long-context understanding with 256K token capacity, GLM-4-32B-0414 delivers exceptional search-based Q&A performance, while Meta-Llama-3.1-8B-Instruct offers efficient lightweight retrieval. This side-by-side view helps you choose the right tool for your specific information retrieval and semantic search needs. Pricing shown is from SiliconFlow.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | Qwen3-30B-A3B-Instruct-2507 | Qwen | Text Understanding & Retrieval | $0.4/$0.1 per M Tokens | 256K long-context understanding |
| 2 | GLM-4-32B-0414 | THUDM | Search & Question Answering | $0.27/$0.27 per M Tokens | Search-optimized performance |
| 3 | Meta-Llama-3.1-8B-Instruct | meta-llama | Lightweight Retrieval | $0.06/$0.06 per M Tokens | Efficient semantic understanding |
Frequently Asked Questions
Our top three picks for 2025 are Qwen3-30B-A3B-Instruct-2507, GLM-4-32B-0414, and Meta-Llama-3.1-8B-Instruct. Each of these models stood out for their innovation, performance, and unique approach to solving challenges in information retrieval, semantic search, and long-context document understanding.
Our in-depth analysis shows several leaders for different needs. Qwen3-30B-A3B-Instruct-2507 is the top choice for applications requiring extensive long-context understanding up to 256K tokens, ideal for large document collections. For search-based Q&A and report generation with balanced performance, GLM-4-32B-0414 excels. For resource-constrained environments needing efficient retrieval, Meta-Llama-3.1-8B-Instruct delivers exceptional performance-to-resource ratio with its compact 8B parameters.