Deepcoder 14B Preview
Code Like o3-mini
Scale Code Reasoning
Long Context
64K Token Inference
Generalizes from 32K training to 64K contexts with 60.6% LiveCodeBench Pass@1.
RL Fine-Tuned
Beats Base 8%
Distributed RL from DeepSeek-R1-Distilled-Qwen-14B boosts coding accuracy to o3-mini level.
Efficient Training
Weeks Not Months
Overlong filtering and verl optimizations cut RL training time by 2x.
Examples
See what Deepcoder 14B Preview can create
Copy any prompt below and try it yourself in the playground.
LeetCode Sort
“Write a Python function to sort a list of integers using quicksort algorithm. Include time complexity analysis and handle edge cases like empty lists or duplicates. Output complete runnable code.”
Graph Traversal
“Implement BFS in Python for a directed graph represented as adjacency list. Find shortest path from node A to node Z. Add visualization using networkx if possible.”
Dynamic Programming
“Solve knapsack problem with weights [1,3,4,5], values [1,4,5,7], capacity 7 using memoization. Return max value and selected items as JSON.”
API Parser
“Parse OpenAPI spec YAML and generate Python client functions for all POST endpoints. Include error handling and authentication headers.”
For Developers
A few lines of code.
Code solves. API calls.
ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.
- Serverless: scales to zero, scales to millions
- Pay per token, no minimums
- Python and JavaScript SDKs, plus REST API
import requestsresponse = requests.post("https://modelslab.com/api/v7/llm/chat/completions",json={"key": "YOUR_API_KEY","prompt": "","model_id": ""})print(response.json())
Ready to create?
Start generating with Deepcoder 14B Preview on ModelsLab.