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Rankings use category fit, feature coverage, pricing signals, public reception, and recency. Affiliate relationships do not affect scores.

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Best 1 Qwen2.5-Coder-32B-Instruct

Qwen2.5-Coder-32B-Instruct is an open-weights large language model developed by Continue AI. It’s notable for its performance in code generation and instruction following due to its 32 billion parameter Qwen architecture. This model is suitable for developers, researchers, and anyone requiring a cap...

2 Mistral 7B Instruct (GGUF)

The Mistral 7B Instruct model, available in GGUF format, represents a fantastic entry point into self-hosted LLMs. Its relatively small size (7 billion parameters) makes it manageable on consumer hardware, while its instruction-tuned nature allows it to effectively respond to a wide range of prompts...

3 Meta Llama 3.1 8B Instruct

Meta Llama 3.1 8B Instruct is a large language model designed for local use. It’s notable for its instruction-tuned capabilities and multilingual support. Developers and technical users seeking AI assistance within JetBrains IDEs can benefit from self-hosting this model, offering control over data a...

4 Mistral-Instruct-7B (via LM Studio)

Mistral-Instruct 7B delivers impressive code generation and conversational abilities within JetBrains IDEs. Its instruction tuning makes it highly responsive to developer prompts, providing accurate suggestions for code completion, debugging, and documentation. The model's relatively small size allo...

5 Magicoder-S-DS-6.7B

Magicoder-S-DS-6.7B is a 6.7-billion-parameter code generation model optimized for self-hosted deployment and used by JetBrains as the backend for local AI features in its IDEs.

6 WizardCoder-15B-V1.0

WizardCoder-15B-V1.0 is an open-weight language model designed for generating and interpreting computer code. Released by the WizardLM research project, it was built from the 15-billion-parameter StarCoder model and fine-tuned using code-oriented Evol-Instruct data, which expands programming instruc...

7 OpenCoder
OpenCoder

OpenCoder is an extension for Microsoft Copilot that utilizes OpenAI’s Codex models to generate code snippets and complete functions based on natural language prompts, primarily focused on Python development within the Visual Studio Code environment.

8 Phi-3-mini-4k-instruct

Phi-3-mini-4k-instruct is a 3.8-billion-parameter instruction-tuned language model developed by Microsoft as part of the Phi-3 family of small language models. The model is designed for efficiency and can run on local hardware, with a 4,000-token context window. Microsoft released the model weights...

9 Magicoder
Magicoder

Magicoder is an extension for the Substrate blockchain framework, enabling developers to create and deploy smart contracts using Solidity, Rust, or Kódex, offering tools for contract development, testing, and deployment within the Substrate ecosystem.

10 RedPajama-INCITE-3B-Instruct

RedPajama-INCITE-3B-Instruct is an open source large language model built by JetBrains. It’s notable for its instruction-tuned design, enabling effective use in research and academic settings. This self-hosted model provides a viable option for individuals and institutions needing a powerful AI tool...

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Frequently Asked Questions

What leads the Instruct ranking?

Qwen2.5-Coder-32B-Instruct currently leads the Instruct results with a displayed score of 8.62/10. This is an editorial ranking result for the items included on this page, not a universal verdict for every use case.

How should I read the score and confidence label?

The 0 to 10 score is Lunoo's ranking judgment. Strong confidence means 10 or more recorded comparison checks, some means 2 to 9, and provisional means fewer than 2.

What supports this ranking?

Lunoo combines category fit, feature coverage, pricing and value signals, public reception, recency, and peer comparisons. Public source links support factual item details when available, but they are not required for membership in this 10-item ranking.

Can I compare the leading results for Instruct?

Yes. The comparison links put adjacent leaders side by side so you can inspect differences that one ranking score cannot capture.

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