Continue vs BenevolentAI
Side-by-side comparison to help you choose the best tool.
Continue
freeContinue is an open-source AI coding assistant that connects any LLM to VS Code and JetBrains, enabling developers to customise their AI coding experience with any model - local or cloud. It provides autocomplete, chat, and edit modes, and supports integration with local models via Ollama. Continue is popular with developers who want full control over their AI coding setup without vendor lock-in.
BenevolentAI
paidBenevolentAI is an AI biomedical platform that uses knowledge graphs and machine learning to identify novel drug targets and repurpose existing drugs for new diseases. Its platform integrates scientific literature, clinical data, and biological databases into a unified knowledge graph to surface hidden relationships and accelerate drug discovery. The company has demonstrated success in target identification for diseases including ALS and atopic dermatitis.
| Feature | Continue | BenevolentAI |
|---|---|---|
| Pricing | free | paid |
| Category | - | - |
| Rating | 4.4 | 4.3 |
| Best For | Developers wanting full control over their AI coding assistant — choosing any LLM, including local models, with zero vendor lock-in | Pharmaceutical companies seeking AI drug target discovery and drug repurposing features |
| Views | 68 | 78 |
Pros
- Completely free and open-source
- Use any LLM including local models via Ollama
- Full customisation and no vendor lock-in
Cons
- More setup than Copilot or Codeium
- Quality depends on LLM choice
Pros
- Powerful knowledge graph integrates vast biomedical data
- Proven track record in target identification
- Accelerates drug repurposing
Cons
- Enterprise-only access model
- Primarily focused on pharma partnerships
- Open-source AI coding assistant
- Any LLM support (cloud & local)
- Ollama local model integration
- VS Code & JetBrains plugins
- Customisable context & prompts
- Biomedical knowledge graph
- Drug target identification
- Drug repurposing
- Scientific literature mining
- Machine learning hypothesis generation