What are the key differences between Continue (with Ollama Backend) and Code Llama (via Ollama)?

Compare Continue (with Ollama Backend) and Code Llama (via Ollama) side by side on Lunoo to see detailed feature differences, AI scores, and expert analysis.

How are Continue (with Ollama Backend) and Code Llama (via Ollama) scored?

Continue (with Ollama Backend) has an AI score of 9.5/10 and Code Llama (via Ollama) has an AI score of 7.9/10. Scores are based on category fit, feature coverage, pricing signals, public reception, and recency.

Continue (with Ollama Backend) vs Code Llama (via Ollama) 2026 — Compared

Continue (with Ollama Backend)

Code Llama (via Ollama)

WINNER Continue (with Ollama Backend)

Continue (with Ollama Backend) edges ahead with a score of 9.5/10 compared to 7.9/10 for Code Llama (via Ollama). While...

emoji_events WINNER

Continue (with Ollama Backend)

9.5 Brilliant

Jetbrains AI Local Get Continue (with Ollama Backend) open_in_new

Code Llama (via Ollama)

7.9 Good

Jetbrains AI Local Get Code Llama (via Ollama) open_in_new

psychology AI Verdict

Continue (with Ollama Backend) edges ahead with a score of 9.5/10 compared to 7.9/10 for Code Llama (via Ollama). While both are highly rated in their respective fields, Continue (with Ollama Backend) demonstrates a slight advantage in our AI ranking criteria. A detailed AI-powered analysis is being prepared for this comparison.

emoji_events Winner: Continue (with Ollama Backend)

verified Confidence: Low

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description Overview

Continue (with Ollama Backend)

Continue is a highly flexible extension that excels by acting as a universal interface for various local LLM backends, most notably Ollama. It allows developers to connect to models like CodeLlama or Mistral running locally, providing chat, context-aware completion, and file editing capabilities directly within the IDE. Its strength lies in its modularity and ability to switch models easily withou...

Code Llama (via Ollama)

When accessed via a robust runner like Ollama, Code Llama remains a benchmark choice. It is specifically trained by Meta on code, giving it inherent strengths in generating syntactically correct and idiomatic code snippets across many languages. For users whose primary goal is high-quality, raw code generation rather than general chat or refactoring, running the dedicated Code Llama model is often...