Which Open Source LLM is Best for RAG? Top Models Compared

Retrieval Augmented Generation (RAG) combines a knowledge base with an LLM to produce accurate, sourced answers. Here, the AI does not rely on the data it learned during training but extracts traceable info from internal documents. Closed models like GPT perform well, but they require sending data to external servers. This is a non-starter for companies handling sensitive information.

In 2026, enterprises are moving toward open source LLMs for RAG to keep data private. They offer better security, retrieval accuracy, and large context windows. This guide walks you through the seven best open source LLM models for RAG.

Why Open Source Models Are Popular for RAG

Most enterprise RAG systems run behind firewalls. They are built around sensitive data like internal wikis, databases, proprietary research, and support logs. When a team member uses a closed LLM, every query and retrieved document is processed on a third-party server. It creates data residency and compliance risks for regulated industries. This is unacceptable for many businesses.

On the other hand, open-source LLMs keep AI fully in-house. Companies can run the weight themselves without having to send the data to external APIs. Besides privacy, you have full control over fine-tuning, updates, latency, and cost. More importantly, these models often surpass closed LLMs on RAG-specific performance benchmarks.

DeepSeek V3.2

DeepSeek V3.2 (from DeepSeek AI) is a reasoning-first model built for code, math, and agentic workflows. Released in December last year, it uses DeepSeek Sparse Attention (DSA) to reduce compute and memory use in long-context scenarios.

Why it's good for RAG: Its multi-hop reasoning connects information from multiple document chunks with minimal hallucinations. The V3.2 offers GPT-level reasoning and good instruction following. In RAG, you often need the model to strictly adhere to the retrieved text and ignore its internal knowledge. DeepSeek handles these types of instructions well.

Key specifications

• Parameter size: 685B total parameters, 37B active (MoE)
• Context window: 128K tokens
• Architecture: Multi-head Latent Attention with DeepSeek Sparse Attention
• License: MIT

Strengths for RAG Pipelines

• Excellent at reasoning over long documents
• Fast inference on long retrieval sets
• Hybrid thinking and non-thinking modes
• Supports tool use and function calling
• MIT license allows unrestricted commercial deployment
• Very low inference cost per token

Typical deployment environments: Due to its massive size, DeepSeek V3.2 will need multiple high-end GPUs (e.g., H100/A100/H200) to run locally. Alternatively, you can use Azure, AWS, and Google Cloud for cloud hosting with inference engines vLLM and SGLang. Enterprise teams with high-volume tasks opt for on-premise deployment.

Limitations: The huge total parameter count requires beefy infrastructure for better performance. Plus, the 128K context window is smaller for synthesizing info from multiple documents.

Try DeepSeek V3.2 for Free!

Qwen 3

Alibaba’s Qwen 3 family (especially 235B-A22B-Instruct-2507 variant) is well-suited for RAG. It works well for multilingual RAG workflows as the model supports 100+ languages. The model consistently ranks high on benchmarks for math, code, tool usage, and multi-step logic.

Why it's good for RAG: Qwen 3 is optimized for retrieval-augmented scenarios. The native 256K context window makes it suitable for multi-document synthesis. It has good prompt adherence; therefore, it correctly interprets and uses retrieved information. More importantly, the hybrid thinking mode allows it to reason through retrieved evidence before responding.

Key specifications

• Parameter size: 235B total parameters, 22B active
• Context window: 256K (expandable to 1M tokens)
• Architecture: MoE
• License: Apache 2.0

Strengths for RAG pipelines:

• Very long context window to process hundreds of documents and lengthy videos
• Multi-step logical reasoning to produce source-grounded answers
• Native support for MCP and function-calling
• Thinking mode allows deeper analysis of retrieved evidence for complex queries
• Dual Chunk Attention to process long sequences into manageable chunks

Typical deployment environments: Qwen 3 is available on ModelScope, Kaggle, and Hugging Face. You can deploy it locally via Ollama, MLX, or LM Studio. Alibaba Cloud offers hosted versions of Qwen 3 models. Enterprise teams can opt for on-premise deployment using inference frameworks like vLLM, SGLang, and TensorRT-LLM.

Limitations

It has fewer community fine-tunes than Llama models, though rapidly growing. The non-thinking mode lacks the chain-of-thought output needed for complex RAG tasks.

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GPT-OSS 120B

GPT-OSS 120B is the first major open-weight release from OpenAI (makers of ChatGPT). It is built to replicate the behavior of popular proprietary GPT-5 models. The 120B variant offers o4-mini-level reasoning on coding, reasoning, and math benchmarks.

Why it’s good for RAG: The full Chain-of-Thought and tool use capabilities are valuable for RAG. The CoT helps it synthesize accurate answers from retrieved snippets. You can adjust the reasoning effort (high, medium, low) for specific RAG tasks and latency needs. The tool-use support is perfect for agentic RAG workflows involving querying, verifying, and synthesizing information.

Key specifications:

• Parameter size: 117B total parameters, 5.1B active
• Context window: 128K
• Architecture: MoE with MXFP4 quantization
• License: Apache 2.0

Strengths for RAG pipelines

• Reliably synthesizes information and cites sources
• Easy adoption for teams already familiar with OpenAI’s API interface
• Tool-use and function-calling streamline RAG workflows
• Strong performance on reasoning and coding benchmarks (90% on MMLU and 80.1 on GPQA)

Typical deployment environments: GPT-OSS 120B can be deployed on a single, 80GB+ GPU (H100 or MI300X). In addition, you can run it via cloud inference endpoints or hybrid on-premise/cloud setup.

Limitations: The 128K context window is smaller than specialized long-context models like Qwen 3 and Llama 4 Maverick.

Try GPT-OSS 120B for Free!

Kimi K2

Developed by Moonshot AI, Kimi K2 (particularly K2-Instruct-0905) is optimized for agentic workflows and long context tasks. For an agentic RAG pipeline, it can autonomously plan, retrieve, call tools, and execute multi-step processes.

Why it’s good for RAG: Kimi K2 0905 excels in RAG due to its 256K context window, up from 128K in earlier variants. This allows the model to ingest entire libraries and codebases in one go without loss of information. The strong instruction following minimizes hallucinations when synthesizing retrieved information.

Key specifications

• Parameter size: 1T total parameters, 32B active
• Context window: 256K
• Architecture: MoE with 384 experts (8 activated per token), MLA, SwiGLU
• License: Modified MIT

Strengths for RAG pipelines

• Agentic tool use for multi-hop retrieval
• Handles very long retrieved contexts with improved accuracy
• Strong reasoning and coding capabilities to synthesize complex info
• Clearly follows instructions and provides answers from source documents
• Efficient MoE reduces per-token cost and latency

Typical deployment environments:

Hosting a full 1T model requires substantial hardware. However, a model with heavy quantization can be deployed on high-end GPUs (H100/H200). Cloud API access is available through Moonshot's platform and multiple third-party providers. K2 is also available in block-FP8 format from Hugging Face.

Limitations: It only accepts text input; image/video inputs require a separate model, Kimi K2.5.

Try Kimi K2 for Free!

GLM-5

GLM-5 is Zhipu AI’s new foundation model, released in February 2026. It builds on GLM 4.x architecture and adds DeepSeek Sparse Attention to improve long-horizon agentic tasks. It is reliable for multi-step reasoning and complex coding.

Why it’s good for RAG: For RAG specifically, GLM-5 stands out for its dramatically reduced hallucination rate. Ziphu reduced the hallucination rate to 34% on the Omniscience index using the reinforcement learning technique, Slime. Its predecessor, GLM 4.7, has a 90% hallucination rate. In a RAG pipeline, models that rely on retrieved context perform better. GLM-5’s native agentic strengths make it ideal for multi-hop RAG pipelines. It can autonomously retrieve more data, verify facts, and chain reasoning steps.

Key Specifications

• Parameter size: 744B, 44B active per inference
• Context window: 200K
• Architecture: Transformer-based MoE with DSA, pertaining on 28.5T tokens
• License: MIT

Strengths for RAG pipelines

• 200K context window comfortably handles document retrieval
• DSA makes large-context inference fast and cost-effective
• Record-low hallucination rate for better factual accuracy
• Advanced tool-calling for multi-hop retrieval and verification

Typical deployment environments: Open weights are available on ModelScope, GitHub, and Hugging Face (zai-org/GLM-5) for self-hosting. It also supports local deployment via inference frameworks (xLLM, vLLM, and SGLang). You can also deploy it on non-NVIDIA chips, such as Huawei Ascend, Hygon, MetaX, and more.

Limitations: GLM-5 is not fit for multimodal RAG as it processes text inputs/outputs only. The context window is not large, but not compared to rivals like Kimi K2.

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Llama 4 Maverick

Llama 4 Maverick from Meta is an open-weight, natively multimodal model released in April 2025. It is optimized for reasoning, coding, creative writing, image analysis, and multilingual tasks.

Why it’s good for RAG: The multimodal capability makes it a perfect fit for RAG workflows involving documents with charts, product images, and technical diagrams. This native multimodality is not found in most LLMs on this list. Not to mention, the 1M token window processes entire knowledge bases in one pass without chunking.

Key specifications

• Parameter size: 400B total, 17B activated
• Context window: 1M tokens
• Architecture: Transformer-based MoE with 128 experts, native multimodality
• License: Llama 4 community license

Strengths for RAG pipelines

• A 1M context window allows the retrieval of book-length documents
• Multilingual capabilities for global or enterprise knowledge bases
• Extensive community resources, fine-tunes, and LoRA adapters
• Only 17B active parameters reduce memory bandwidth and compute cost

Typical deployment environments: A BF16 and FP8 quantized versions can fit on a single H100 GPU and H100 DGX host, respectively. A multi-GPU server is required for full precision. It is also available on major cloud providers like AWS, Cloudflare, Workers AI, GCP, Azure, and more.

Limitations: The Llama 4 Community license includes commercial-use restrictions for large-scale deployments.

Try Llama 4 Maverick for Free!

GLM 4.7

Last but not least, GLM 4.7 is one of the most capable open-source LLMs for RAG use cases. Released in December 2025, it is a lighter-weight version of the GLM family. The model is designed for agentic coding, multi-step reasoning, and tool-augmented workflows.

Why it’s good for RAG: The model’s triple-tier thinking system makes it effective for RAG. Interleaved Thinking reasons before every response or tool use to answer accurately based on retrieved information. Preserved Thinking remembers reasoning from earlier turns, so you do not have to build context each time. This mode is useful for conversational RAG. In addition, Turn-level Thinking gives developers control over how much reasoning a model uses.

Key specifications

• Parameter size: 358B, 32B active
• Context window: 200K
• Architecture: MoE with Multi-Head Attention
• License: MIT

Strengths for RAG pipelines

• The 200K input context handles a large document collection
• Controllable thinking modes for step-by-step synthesis
• Turn-level Thinking for fast, low-cost responses for iterative RAG workflows
• Strong coding and multilingual capabilities
• Can be fine-tuned on your proprietary data

Typical deployment environments: BF16 and FP8 quantized versions require 16x H100s and 8x H100s, respectively. They run well via inference engines like vLLM, SGLang, and Transformers 4.57.3+. Enterprises can opt for secure, fully private deployments on their own infrastructure or Huawei Ascend clusters.

Limitations: Thinking modes generate additional tokens and increase latency and cost. Moreover, it is a text-only model and does not support audio, image, or video inputs.

Try GLM 4.7 for Free!

How to Choose the Right Model for Your RAG Use Case

Selecting the best open-source LLM for RAG depends on the following factors:

• Context Window: A large context window is crucial for RAG workflows involving lengthy documents. Prioritize a model that can handle that volume without losing coherence. Llama 4 Maverick (1M tokens), Kimi K2 (256K), and Qwen 3 (256K) are clear winners here. The aforementioned GLM variants with 200K context length also handle extensive retrieval comfortably.
• Reasoning Complexity: Strong CoT models such as DeepSeek V3.2, Kimi K2, and GPT-OSS 120B are well-suited for heavy reasoning and multi-hop retrieval. These models are optimized for multi-step logic, e.g., “Compare Q3 sales across regions using these five reports.”
• Hallucination Sensitivity: Factual accuracy is important for RAG applications. Of all the listed models, GLM-5 (34%) and Llama 4 Maverick (12.7%) have record-low hallucination rates.
• Deployment Constraints: MoE models with low active parameters (Qwen 3, GPT-OSS 120B, Llama 4 Maverick) keep GPU costs low. In particular, GPT-OSS 120B is an affordable option for on-premise deployment as it can fit on a single 80GB GPU.
• Licensing: DeepSeek V3.2, Qwen3, GPT-OSS 120B, and GLM-5 are all MIT or Apache 2.0 licensed. This allows commercial use with little to no restrictions. Llama 4 Maverick uses a community license that permits commercial use but has specific terms.
• Multimodal Retrieval: If you are dealing with mixed-media documents, native multimodal Llama 4 Maverick has an edge over other models.

Running These Models Privately Without Managing Infrastructure

Self-hosting a 70B model is expensive and technically demanding. You need the right GPUs, networking, storage, and orchestration. Infrastructure costs can quickly balloon into tens of thousands of dollars per month. Okara.ai helps by eliminating all the infrastructure challenges.

The platform provides private, isolated deployments of all the models mentioned above. You can use a RAG-ready model without any setup. The privacy-focused keeps your documents in your controlled environment.

Try Okara.ai for free for your private RAG workloads.

Frequently Asked Questions

What is the best open-source LLM for RAG?

Qwen 3 and DeepSeek V3.2 are the overall winners for RAG use cases due to the large context window and faithfulness. Llama 4 Maverick is suitable for multi-document synthesis. Kimi K2 is the best choice for agentic RAG with multi-step tool use.

Which open source LLM has the longest context window for RAG?

Llama 4 Maverick supports up to 1M tokens, ample for most RAG systems. Kimi K2 and Qwen handle a 256K context window. GLM 5 and 4.7 each have 200K context length.

Do I need to fine-tune an LLM for RAG?

Not always. Most open-source LLMs are instruction-tuned to follow RAG prompts effectively. Fine-tuning can help if your use case requires specific output formats (like JSON), custom terminology, or a unique brand voice.

What is the best embedding model to pair with an open-source LLM for RAG?

For most use cases, Qwen3-Embedding-8B and BGE-M3 offer good multilingual retrieval performance. They also integrate well with open-source LLMs like Qwen and Llama.