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Rank Model Price
1
Open Weights / API
2
Open Weights
3
Open Weights
4
Open Weights
5
Open Weights
6
Open Weights
7
Open Weights

Just the Highlights

DeepSeek-V4

Rank #1
Open Weights / API

The Sparse Colossus. Released late November 2025, this 1-trillion parameter MoE model redefines the open-source ceiling. It matches GPT-5 performance while offering an ultra-low-cost API for enterprise-level STEM reasoning and code translation.

Llama 4 Maverick (405B)

Rank #2
Open Weights

The Community Standard. Meta's flagship released in April 2025 offers the most robust ecosystem support. Its 'Scout' (lightweight) and 'Behemoth' (dense) variants allow for massive flexibility, with Maverick being the sweet spot for creative reasoning.

Grok-3

Rank #3
Open Weights

The Unfiltered Reasoner. Known for exceptional performance in mathematics, physics simulation, and real-time data analysis. It has become the standard for academic 'red teaming' due to its low refusal rates.

Qwen 3 Max (235B MoE)

Rank #4
Open Weights

The Polyglot. Outperforming Llama 4 in multilingual tasks and math, this model supports 119 languages natively. Its unique architecture allows for dynamic context expansion up to 1M tokens, making it a favorite for global enterprise applications.

Mistral Medium 3.1

Rank #5
Open Weights

The Efficient Frontier. Released August 2025, this multimodal model balances performance with strict privacy controls. It is highly optimized for RAG (Retrieval Augmented Generation) and fits comfortably on smaller enterprise clusters than Llama 4.

Falcon 3 (180B)

Rank #6
Open Weights

The Sovereign Choice. Developed by TII, Falcon 3 focuses on data sovereignty and 'light infrastructure' deployment. It is the preferred model for government and defense sectors requiring air-gapped performance on standard hardware.

Arctic Embed 2.0 (Snowflake)

Rank #7
Open Weights

The Retrieval Engine. While not a chat model, it is the world's best open-weight embedding model. It powers the 'memory' of most open-source agentic systems, excelling at multilingual retrieval without sacrificing English performance.