What are Open Source LLMs for Deep Research?
Open source LLMs for deep research are specialized large language models designed to handle complex analytical, reasoning, and investigative tasks that require extensive context understanding and multi-step logical processing. Using advanced architectures like Mixture-of-Experts (MoE) and reinforcement learning techniques, they excel at mathematical reasoning, code analysis, scientific inquiry, and long-document comprehension. These models enable researchers and analysts to process vast amounts of information, synthesize insights, and generate well-reasoned conclusions. They foster collaboration, accelerate scientific discovery, and democratize access to powerful analytical tools, enabling applications from academic research to enterprise intelligence gathering.
DeepSeek-R1
DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) that addresses the issues of repetition and readability. With 671B total parameters in its MoE architecture and 164K context length, it achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. Through carefully designed training methods incorporating cold-start data, it has enhanced overall effectiveness for deep analytical research.
DeepSeek-R1: State-of-the-Art Reasoning for Complex Research
DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) that addresses the issues of repetition and readability. Prior to RL, DeepSeek-R1 incorporated cold-start data to further optimize its reasoning performance. It achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks, and through carefully designed training methods, it has enhanced overall effectiveness. With its massive 671B MoE architecture and 164K context window, DeepSeek-R1 excels at handling complex research tasks that require deep analytical thinking, multi-step reasoning, and extensive context understanding. The model's reinforcement learning foundation ensures it delivers robust, practical solutions aligned with rigorous research standards.
Pros
- Comparable performance to OpenAI-o1 in reasoning tasks.
- Massive 671B MoE architecture with 164K context length.
- Optimized through reinforcement learning for enhanced effectiveness.
Cons
- Higher computational requirements due to large parameter count.
- Premium pricing at $2.18/M output tokens on SiliconFlow.
Why We Love It
- It delivers OpenAI-o1-level reasoning performance with open-source accessibility, making it ideal for researchers tackling the most complex analytical challenges.
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 for complex logical reasoning and non-thinking mode for efficient dialogue, with 128K context support and exceptional multilingual capabilities across over 100 languages.
Qwen3-235B-A22B: Flexible Reasoning with Massive Multilingual Support
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. With its 128K context window and flexible reasoning modes, Qwen3-235B-A22B is perfectly suited for international research teams working on complex, multilingual analytical projects.
Pros
- Seamless switching between thinking and non-thinking modes.
- 235B total parameters with efficient 22B activation.
- Supports over 100 languages and dialects.
Cons
- Context window smaller than some competitors.
- May require mode selection expertise for optimal use.
Why We Love It
- It offers unparalleled flexibility with dual reasoning modes and exceptional multilingual support, making it ideal for global research collaboration on complex analytical tasks.
MiniMax-M1-80k
MiniMax-M1 is an open-weight, large-scale hybrid-attention reasoning model with 456B parameters and 45.9B activated per token. It natively supports 1M-token context, with lightning attention enabling 75% FLOPs savings vs DeepSeek R1 at 100K tokens. Efficient RL training with CISPO and hybrid design yields state-of-the-art performance on long-input reasoning and real-world software engineering tasks.
MiniMax-M1-80k: Extreme Context for Comprehensive Research
MiniMax-M1 is an open-weight, large-scale hybrid-attention reasoning model with 456B parameters and 45.9B activated per token. It natively supports 1M-token context, lightning attention enabling 75% FLOPs savings vs DeepSeek R1 at 100K tokens, and leverages a MoE architecture. Efficient RL training with CISPO and hybrid design yields state-of-the-art performance on long-input reasoning and real-world software engineering tasks. The model's unprecedented 1M-token context window makes it exceptional for researchers who need to analyze entire research papers, large codebases, or comprehensive document collections in a single pass. Its hybrid-attention architecture ensures computational efficiency while maintaining superior reasoning capabilities for the most demanding deep research applications.
Pros
- Unprecedented 1M-token native context support.
- 75% FLOPs savings compared to DeepSeek R1 at 100K tokens.
- 456B parameters with efficient 45.9B activation.
Cons
- Higher pricing at $2.20/M output tokens on SiliconFlow.
- May be overkill for shorter research tasks.
Why We Love It
- It shatters context limitations with native 1M-token support and exceptional efficiency, enabling researchers to analyze entire document collections and massive codebases without compromising reasoning quality.
Deep Research LLM Comparison
In this table, we compare 2025's leading open source LLMs for deep research, each with unique strengths. DeepSeek-R1 provides OpenAI-o1-level reasoning with 164K context, Qwen3-235B-A22B offers flexible dual-mode reasoning with exceptional multilingual support, and MiniMax-M1-80k delivers unprecedented 1M-token context for comprehensive analysis. This side-by-side view helps you choose the right model for your specific research requirements, with pricing from SiliconFlow.
| Number | Model | Developer | Architecture | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | DeepSeek-R1 | deepseek-ai | MoE (671B/164K) | $0.50 input / $2.18 output per M tokens | OpenAI-o1-level reasoning |
| 2 | Qwen3-235B-A22B | Qwen3 | MoE (235B/128K) | $0.35 input / $1.42 output per M tokens | Dual-mode + multilingual (100+ languages) |
| 3 | MiniMax-M1-80k | MiniMaxAI | MoE (456B/1M) | $0.55 input / $2.20 output per M tokens | 1M-token context with 75% efficiency gain |
Frequently Asked Questions
Our top three picks for deep research in 2025 are DeepSeek-R1, Qwen3-235B-A22B, and MiniMax-M1-80k. Each of these models stood out for their exceptional reasoning capabilities, extensive context handling, and unique approaches to solving complex analytical challenges in research environments.
For maximum reasoning power on complex analytical tasks, DeepSeek-R1 with its 671B MoE architecture is ideal. For international research collaboration requiring multilingual capabilities, Qwen3-235B-A22B's support for 100+ languages with dual reasoning modes is perfect. For researchers analyzing massive documents, codebases, or entire paper collections, MiniMax-M1-80k's native 1M-token context window is unmatched. All models available through SiliconFlow offer competitive pricing for research budgets.