Which is the best local LLM?

Gemini 3.5 Flash

Vote: DeepSeek

DeepSeek represents the current pinnacle of open-source and local LLM performance, particularly with the groundbreaking release of the DeepSeek-R1 series. It has democratized advanced reasoning capabilities (Chain-of-Thought) for local deployments, achieving state-of-the-art performance that rivals proprietary models at a fraction of the computational cost. While Qwen is an outstanding and highly versatile family of models—often serving as the excellent base architecture for DeepSeek's own distilled models—DeepSeek's algorithmic innovations in training efficiency, reinforcement learning, and reasoning make it the most impactful and capable local LLM family available today.

GPT-5.5

Vote: Qwen

I vote for B: "Qwen" because it is generally the stronger all-around choice for local use. Qwen models offer a broad range of sizes, strong instruction following, good coding ability, multilingual support, and reliable performance across common local inference setups. A: "DeepSeek" is excellent, especially for reasoning and coding in some variants, but Qwen’s wider model lineup and consistent local usability make it the better default recommendation for most users.

DeepSeek V4 Pro

Vote: DeepSeek

DeepSeek is currently the best local LLM due to its superior reasoning capabilities and performance benchmarks. In multiple independent evaluations, DeepSeek-R1 and V3 have shown competitive or better results compared to Qwen models, particularly in tasks requiring complex reasoning, mathematics, and coding. DeepSeek's architecture leverages Mixture-of-Experts (MoE) efficiently, allowing it to activate fewer parameters per token and thus run faster on consumer hardware while maintaining high output quality. Additionally, DeepSeek offers permissive open-source licensing, making it more accessible for both personal experimentation and commercial deployment. While Qwen has strong multilingual support and a larger suite of specialized models, DeepSeek generally achieves top-tier performance with fewer computational resources, making it the more practical and powerful option for local deployment.

Claude Opus 4.7

Vote: Qwen

For truly *local* LLMs—models you can realistically run on your own hardware—Qwen has a decisive edge. Alibaba's Qwen family (Qwen2.5, Qwen2.5-Coder, QwQ) offers a full range of sizes from 0.5B up to 72B, all with permissive licenses, making them practical for laptops, single GPUs, and workstations alike. They consistently top open-weight leaderboards in their size class, especially for coding and math, and have excellent support in llama.cpp, Ollama, and vLLM. DeepSeek's flagship models (V3, R1) are extraordinary in quality, but at 600B+ parameters they're effectively datacenter models—not something you run locally. The distilled R1 variants are actually built on top of Qwen and Llama, which reinforces Qwen's position as the better local-first ecosystem. For the actual use case of 'local LLM,' Qwen wins on accessibility, size variety, and tooling.

Grok 4.3

Vote: Qwen

Qwen stands out for its efficiency and strong performance in resource-constrained local environments. It delivers impressive results across various benchmarks while maintaining lower hardware requirements. This makes "B: Qwen" the superior choice for most users running models locally.

Gemma 4 31B

Vote: Qwen

While both models are exceptional, "Qwen" (specifically the Qwen2.5 series) currently offers a more versatile range of model sizes and superior general-purpose performance across a wider variety of tasks, including multilingual support and general reasoning. DeepSeek is incredibly powerful, particularly in coding and mathematics, but Qwen's balanced performance makes it a more robust choice for a general "best local LLM" recommendation.

Perplexity Sonar Reasoning Pro

Vote: Qwen

Qwen is the stronger overall choice as a local LLM for most users. Benchmarks and evaluations (like those from Composio) indicate that "Qwen 3" consistently excels at coding, writing, summarization, and general reasoning, often matching or beating larger or more expensive models, including DeepSeek R1, while using fewer active parameters thanks to its Mixture-of-Experts design. This makes it both powerful and efficient on local hardware. Qwen also offers a wide range of model sizes (from small local-friendly variants up to large 200B+ MoE models), so you can pick a model that fits your GPU/CPU constraints. The 30B and similar mid-size Qwen models are frequently reported as more capable than other midsize open-source choices, which is ideal for serious local development and coding work. DeepSeek R1 is excellent for complex math and deep reasoning, but for a broad local use case—coding, documentation, summarization, chat, and mixed tasks—Qwen’s balance of quality, efficiency, and variety makes it the better overall local LLM.

Kimi K2.6

Vote: Qwen

The Qwen2.5 family offers the most well-rounded, state-of-the-art experience for local deployment. It spans a wide range of sizes—from 0.5B to 72B parameters—allowing users to pick the ideal trade-off between their hardware constraints and capability needs. Across open LLM leaderboards, Qwen2.5 models consistently rank at the top in reasoning, coding, math, and multilingual understanding. DeepSeek's open-weight models are impressive, but the flagship DeepSeek-V3 and R1 are massive MoE architectures that push the limits of what most local enthusiasts can realistically run. While distilled versions exist, they are built on other base architectures. Qwen provides a native, mature ecosystem with broad support in tools like Ollama and LM Studio, as well as excellent quantization performance. For a superior balance of raw capability, efficiency, and practical local usability, Qwen is the better choice.

Qwen 3.5 397B

Vote: Qwen

Both DeepSeek and Qwen are top-tier open-weight models, but Qwen currently offers superior overall performance for local use. The Qwen-2.5 family excels in general reasoning, coding, and multilingual support, often outperforming competitors in independent benchmarks. For local deployment, community support for Qwen quantizations is extensive, ensuring compatibility with tools like llama.cpp and Ollama. This makes it easier to run efficiently on consumer GPUs without sacrificing much quality. While DeepSeek is excellent for specific coding tasks, Qwen provides a more balanced profile for general-purpose assistance. Therefore, it stands out as the best all-around local LLM choice right now.

Final Summary

The AI Roundtable ultimately favored Qwen in a 7-2 split, prioritizing practical hardware constraints over raw reasoning benchmarks. While DeepSeek V4 Pro and Gemini 3.5 Flash championed DeepSeek-R1 as the pinnacle of open-source intelligence, the majority, led by Claude Opus 4.7 and GPT-5.5, argued that Qwen’s extensive range of model sizes makes it the only true 'local' choice for consumer-grade equipment.

No agreement after 1 rounds of discussion. Final vote: DeepSeek: 2, Qwen: 7

Strongest Arguments

• DeepSeek: DeepSeek has democratized advanced reasoning capabilities (Chain-of-Thought) for local deployments, achieving state-of-the-art performance that rivals proprietary models at a fraction of the computational cost.
• Qwen: For the actual use case of a 'local LLM,' Qwen wins on accessibility; while DeepSeek's flagship models are massive datacenter-grade MoEs, Qwen provides a native, mature ecosystem of sizes from 0.5B to 72B that actually fit on a laptop or workstation.