Best open source LLMs on Hugging Face in 2025
llama-3.2 for its state-of-the-art performance and versatility. For lightweight and efficient use cases, mistral-small-latest offers excellent speed and cost-effectiveness.RECOMMENDATION
llama-3.2 for highest quality open source LLM tasks due to its advanced architecture and broad community support, making it ideal for production-grade applications.| Use case | Best choice | Why | Runner-up |
|---|---|---|---|
| General purpose chat | llama-3.2 | Superior language understanding and generation with large context windows | mistral-large-latest |
| Lightweight deployment | mistral-small-latest | Fast inference and low resource requirements | llama-3.1-70b |
| Code generation | llama-3.2 | Strong coding benchmarks and multi-language support | mistral-large-latest |
| Embeddings and retrieval | sentence-transformers/all-mpnet-base-v2 | High-quality embeddings with efficient vector search compatibility | all-MiniLM-L6-v2 |
| Multilingual tasks | llama-3.2 | Robust multilingual understanding and generation | mistral-large-latest |
Top picks explained
For general purpose chat and coding, llama-3.2 leads with its advanced architecture and large parameter count, delivering state-of-the-art results. mistral-large-latest is a strong alternative with competitive performance and faster inference. For lightweight or resource-constrained environments, mistral-small-latest offers a great balance of speed and quality.
For embedding generation, models from the sentence-transformers family like all-mpnet-base-v2 provide high-quality vector representations optimized for retrieval tasks.
In practice
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "meta-llama/Llama-3-2" # Example Hugging Face repo name
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
prompt = "Explain the benefits of open source LLMs in 2025."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)Explain the benefits of open source LLMs in 2025. Open source LLMs provide transparency, flexibility, and community-driven improvements, enabling developers to customize and deploy powerful AI models without vendor lock-in.
Pricing and limits
| Option | Free | Cost | Limits | Context |
|---|---|---|---|---|
llama-3.2 | Fully free, open source | No cost, self-hosted | Hardware dependent, large VRAM needed | Best for high-quality, large-scale use |
mistral-large-latest | Fully free, open source | No cost, self-hosted | Requires moderate GPU resources | Good balance of speed and quality |
mistral-small-latest | Fully free, open source | No cost, self-hosted | Lower accuracy but fast inference | Ideal for edge or low-resource devices |
sentence-transformers/all-mpnet-base-v2 | Fully free, open source | No cost, self-hosted | Embedding size 768 dims | Optimized for semantic search and retrieval |
What to avoid
Avoid older or less maintained models like gpt2 or distilgpt2 for production as they lag in quality and efficiency. Also, steer clear of very large models without hardware support, as they cause latency and cost issues. Models without active community support may lack updates and security patches.
How to evaluate for your case
Benchmark models on your specific tasks using Hugging Face’s evaluate library or custom datasets. Measure latency, accuracy, and resource usage. Use Hugging Face’s transformers and datasets libraries to automate evaluation pipelines and compare models side-by-side.
from transformers import pipeline
from datasets import load_dataset
model_name = "meta-llama/Llama-3-2"
classifier = pipeline("text-classification", model=model_name)
dataset = load_dataset("imdb", split="test[:100]")
results = [classifier(text)[0] for text in dataset["text"]]
print(f"Evaluated {len(results)} samples on {model_name}")Evaluated 100 samples on meta-llama/Llama-3-2
Key Takeaways
- Use
llama-3.2for best open source LLM quality and versatility in 2025. -
mistral-small-latestis ideal for fast, resource-efficient deployments. - Embedding tasks benefit from
sentence-transformers/all-mpnet-base-v2for semantic search. - Avoid outdated or unsupported models to ensure security and performance.
- Benchmark models on your own data to select the best fit for your application.