As AI-assisted coding tools evolve from simple auto-completion to autonomous agentic reasoning, software engineers must evaluate which LLM architecture best serves their development workflow. Comparing OpenAI’s ChatGPT (GPT-4o / o1) against DeepSeek’s open-weights models (DeepSeek-V3 / DeepSeek-R1) highlights distinct trade-offs in architecture, token economics, and privacy.

Architecture Comparison: Dense Transformers vs Mixture-of-Experts (MoE)

Model Architecture & Benchmark Comparisontext
+-----------------------+-------------------------------+-------------------------------+
| Feature               | OpenAI ChatGPT (GPT-4o / o1)  | DeepSeek (DeepSeek-V3 / R1)   |
+-----------------------+-------------------------------+-------------------------------+
| Model Architecture    | Dense / Multi-Modal           | Mixture-of-Experts (MoE)      |
| Active Parameters     | Proprietary (~220B active)    | 37B active out of 671B total  |
| Open Weights          | Closed / API Access Only      | Open Weights (MIT License)    |
| API Cost (1M tokens)  | ~$2.50 Input / $10.00 Output  | ~$0.14 Input / $0.55 Output   |
| Self-Hosting          | Not Possible                  | Supported (ollama / vLLM)     |
+-----------------------+-------------------------------+-------------------------------+

Self-Hosting DeepSeek Locally via vLLM

vLLM Local Server Launchbash
# Run DeepSeek quantized model on local GPU server with OpenAI API compatible server
python3 -m vllm.entrypoints.openai.api_server \
    --model deepseek-ai/DeepSeek-V3 \
    --tensor-parallel-size 4 \
    --port 8000