Open Source LLM Leaderboard: 12 Models That Just Dethroned GPT-4

The open source AI landscape just shifted. While everyone was paying OpenAI monthly fees, a dozen models quietly climbed the opensource llm leader board and started outperforming GPT-4 on key benchmarks. Some are from familiar names. Others came from nowhere.

Your next project doesn't need a $20-per-month ChatGPT subscription. These models run locally, cost nothing after download, and several match or beat the closed-source giants on reasoning, coding, and creative tasks.

DeepSeek V3.2: The Reasoning Champion

DeepSeek V3.2 sits at the top of most open source benchmarks right now. This 671B parameter model handles multi-step reasoning better than anything else in the open source space. The context window stretches to 128k tokens, and it runs surprisingly well on consumer hardware when quantized.

What makes V3.2 different is its training approach. DeepSeek focused heavily on mathematical reasoning and code generation during the fine-tuning process. The result shows in benchmarks like HumanEval where it scores above 85% on Python coding tasks.

Memory requirements are steep. You'll need at least 24GB VRAM for decent inference speeds. But for complex reasoning tasks, nothing else comes close in the open source world.

Llama 4 Maverick: Meta's Surprise Release

Meta dropped Llama 4 Maverick without much fanfare. This 405B parameter model focuses on instruction following and conversational AI. The training dataset included more recent web content, making it surprisingly current for an open source model.

Maverick excels at creative writing and long-form content generation. It maintains coherence across 32k token contexts and rarely hallucinates basic facts. The model works well for content creation, technical writing, and detailed explanations.

Running Maverick requires serious hardware. Plan for 32GB+ VRAM or distributed inference across multiple GPUs. The resource requirements are worth it if you need reliable long-form generation.

Kimi K2.5: The Efficiency Expert

Kimi K2.5 proves bigger isn't always better. This 14B parameter model punches way above its weight class. It runs smoothly on mid-range hardware while delivering surprisingly sophisticated responses.

The secret is aggressive optimization during training. Kimi's team used advanced distillation techniques to pack larger model knowledge into a smaller architecture. The result is snappy inference times with minimal quality loss.

K2.5 handles code completion, basic reasoning, and general chat very well. It's perfect for developers who need AI assistance without dedicating a gaming rig to inference. You can run it comfortably on 12GB VRAM.

DeepSeek R1 and V3: The One-Two Punch

DeepSeek released R1 and V3 as complementary models. R1 specializes in reasoning and mathematical problem-solving. V3 focuses on general conversation and creative tasks. Together, they cover most AI use cases.

R1 dominates math benchmarks. It can solve complex word problems, handle multi-variable equations, and even tackle some graduate-level physics. The model uses chain-of-thought reasoning by default, showing its work step by step.

V3 brings more personality to conversations. It understands context better, maintains engaging dialogue, and produces more natural-sounding text. Both models share similar architecture but different training focuses.

GLM-4.7 and GLM-5: China's Open Source Push

Zhipu AI released both GLM-4.7 and GLM-5 within months of each other. GLM-4.7 is the smaller, more practical option at 7 billion parameters. GLM-5 scales up to 130B parameters with enhanced multilingual capabilities.

Both models excel at Chinese language tasks, but their English performance surprises many users. GLM-4.7 runs on modest hardware while GLM-5 needs more resources but delivers better results on complex tasks.

The models include strong safety filters and content moderation built-in. They're particularly good at technical documentation, code comments, and structured data analysis.

GPT-oss 120B: The Community Wild Card

GPT-oss 120B came from a community effort to recreate GPT-4 level performance with full transparency. The project published training data, model weights, and detailed methodology.

Performance varies depending on the task. Creative writing and general conversation work well. Complex reasoning and specialized knowledge show gaps compared to the commercial models. But the full transparency makes it valuable for research and experimentation.

The community actively improves the model. Regular updates fix bugs, improve performance, and add new capabilities. It's not the strongest model on this list, but the open development process is fascinating to follow.

Qwen 3.5: Alibaba's Sleeper Hit

Qwen 3.5 flies under the radar in most top open source llm leaderboard discussions. This 72B parameter model from Alibaba handles multilingual tasks exceptionally well. It supports over 20 languages with near-native fluency in each.

Code generation is another strength. Qwen 3.5 writes clean Python, JavaScript, and Java with minimal bugs. The model understands popular frameworks and libraries, generating working code that rarely needs major revisions.

Resource requirements sit in the middle range. You can run quantized versions on 16GB VRAM, though 24GB gives better performance. The model works well for international projects requiring multiple language support.

MiMo-V2-Flash and Mistral Large: Speed vs Scale

MiMo-V2-Flash prioritizes inference speed over raw capability. This 8B parameter model generates tokens faster than almost anything else. Response times feel instant on decent hardware.

Mistral Large goes the opposite direction. At 123B parameters, it delivers sophisticated reasoning and detailed responses. The trade-off is slower generation and higher resource requirements.

Both serve different needs. MiMo works great for chatbots, quick assistance, and rapid iteration. Mistral Large handles complex analysis, detailed research, and professional writing tasks.

MiniMax M2.5: The Compact Powerhouse

MiniMax M2.5 squeezes impressive performance into just 6 billion parameters. The model uses novel architecture improvements to achieve results comparable to much larger models.

Training focused heavily on efficiency and knowledge compression. M2.5 knows surprising amounts about current events, technical topics, and cultural references despite its small size. It runs comfortably on consumer laptops with 8GB RAM.

The model works particularly well for educational applications, simple coding tasks, and personal AI assistants. It won't replace larger models for complex work, but it covers daily AI needs effectively.

Which Model Should You Choose?

Your choice depends on hardware and use case. DeepSeek V3.2 wins for pure performance if you have the VRAM. Kimi K2.5 offers the best efficiency for modest hardware. Llama 4 Maverick excels at creative tasks.

For developers on a budget, start with GLM-4.7 or MiMo-V2-Flash. They run on reasonable hardware and handle most coding assistance tasks well. Upgrade to larger models once you understand your specific needs.

The best open source llms to date prove you don't need to rent AI from big tech companies. Download a few models, test them on your tasks, and find what works. The future of AI might just be sitting on your local machine.