DeepSeek V3 shows incredible potential, but its 64k context window creates real limitations for large projects. Based on my testing and real usage, here’s what you need to know.
I ran three specific tests to evaluate DeepSeek V3’s capabilities:
- Building an image generation site with prompt enhancement – Perfect execution on first attempt.
- Creating a complex text adventure game that generates itself with custom actions and AI-generated images – Success after 3 attempts.
- Developing a snake game with AI opponent using A-star pathfinding – Nailed it first try.
But here’s the actual issue: While DeepSeek V3 handles new projects excellently, working with existing large codebases becomes problematic. The 64k context window simply doesn’t provide enough room to load and process substantial amounts of code.
Current workarounds include:
– Breaking projects into smaller chunks.
– Creating checkpoints and starting new tasks with project analysis.
– Using tools like Cursor that implement moving context features.
But these solutions aren’t perfect. You often end up in what I call the “spiral of doing the same thing” – fix one error, create another, fix that, return to the first error, and repeat.
The model itself is powerful, boasting 671 billion parameters with 37 billion active parameters per token. It integrates verification patterns from the R1 series and shows strong performance on benchmarks like MMLU (88.5) and DROP (91.6).
For more context on DeepSeek V3’s capabilities, check out my earlier analysis here:
https://adam.holter.com/deepseek-v3-the-open-source-model-that-matches-claude-3-5-sonnet/
The good news? Since DeepSeek V3 is open source, we’ll likely see implementations with extended context windows soon. The community is already working on Long RoPE implementations that could double or triple the current context capacity.
Bottom line:
DeepSeek V3 is excellent for new development but needs context window improvements for practical use with large existing projects.
If you’re starting fresh, it’s a solid choice. If you’re working with big codebases, wait for the extended context implementations.
