Fast Coding Mixture of Agents
A fast variant of the Coding Mixture of Agents (MoA) that uses GPT-4o-mini to classify task difficulty. Simple tasks are quickly processed by GPT-4o, while complex ones utilize the full MoA. With 68% classification accuracy and a bias towards the MoA when uncertain, this mix optimizes for speed without compromising on quality for challenging problems.
API
Example
Models
This mix uses the models below:
claude-3-5-sonnet-20241022
Provided by anthropic
gpt-4-turbo-2024-04-09
Provided by openai
gpt-4o-2024-08-06
Provided by openai
Readme
Fast Coding Mixture of Agents
This is an optimized version of our Coding Mixture of Agents (MoA) designed for faster response times while maintaining high-quality outputs for a wide range of coding tasks.
With 68% classification accuracy and a bias towards the MoA when uncertain, this mix optimizes for speed without compromising on quality for challenging problems.
Categories
- 👩🏽💻 Coding
- 🏃🏻 Fast
- 🦾 Mixture of Agents
How it Works
- Task Classification: Your coding request is first processed by GPT-4o-mini, which classifies the task difficulty.
- Adaptive Processing:
- For normal difficulty tasks, the request is sent directly to GPT-4o for a fast response.
- For complex tasks, the full MoA is engaged, utilizing multiple models for comprehensive solutions.
Performance
- Classification Accuracy: The difficulty classifier has a 68% accuracy rate.
- Bias: When uncertain, the classifier leans towards using the full MoA, ensuring complex tasks receive comprehensive attention.
- Speed: For normal difficulty tasks (the majority of requests), responses are generated significantly faster than the standard MoA.
- Quality: Complex tasks still benefit from the full capabilities of the MoA, maintaining high-quality outputs for challenging problems.
Composition
This mix adaptively uses the following models:
| Model Name | Role |
|---|---|
| GPT-4o-mini | Task Difficulty Classifier |
| GPT-4o | Fast Response (Normal Tasks) |
| coding-moa | Comprehensive Solution (Complex Tasks) |
The Full MoA composition remains the same as our standard Coding MoA:
| Model Name | Type |
|---|---|
| Claude 3.5 Sonnet | Proposer |
| GPT-4 Turbo | Proposer |
| GPT-4o | Aggregator |
Use Cases
This mix is ideal for:
- Developers who need quick responses for routine coding tasks
- Projects with a mix of simple and complex programming challenges
- Scenarios where processing speed is crucial, but not at the expense of solution quality for difficult problems
Limitations
- The 68% classification accuracy means that some complex tasks might be processed by the single model, potentially resulting in less comprehensive solutions in those cases, however the cost savings are passed on to you.
- While faster on average, response times may vary depending on task classification and processing path.
Future Improvements
We're continually working to enhance this mix, with focus areas including:
- Improving the accuracy of the task difficulty classifier
- Optimizing the balance between speed and comprehensive processing
- Expanding the range of coding tasks that can be handled efficiently
We welcome feedback and suggestions from the community to help refine and improve this fast coding assistance solution.