How good is DeepSeek v3–0324 ?
Yesterday, Deepseek came back to business and released a new version of DeepSeek V3 i.e. DeepSeek V3–0324, their first non-reasoning LLM that was released in December 2024.
Surprisingly, to keep up the suspense, they haven’t released any information about the model, only the model weights that are open-sourced.
https://medium.com/media/14ff5503fe95b198d51bbf0ac026ac86/href
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How good is DeepSeek V3–0324?
No benchmark data has been released. So, based on metrics we really can’t say anything, but
https://medium.com/media/3f79923c3fb91fd47ce8a705c3830cca/href
Early Reddit and social media reviews say it’s a blast of a model. though DeepSeek mentioned that it’s just a small update, actually, it is much more than that. The model’s context length has increased massively, and it is looking to be performing great on coding and mathematical tasks.
DeepSeek V3–0324 vs DeepSeek V3
1. Coding & Technical Tasks
- DeepSeek V3–0324: Excels in frontend/UI development, generating cleaner, more efficient, and production-ready code. Handles complex frontend logic significantly better than the original V3.
- Original V3: Struggles with dynamic UI development but performs well in basic scripting and backend logic.
2. Reasoning & Problem-Solving
- DeepSeek V3–0324: Features enhanced Chain of Thought (CoT) reasoning, enabling step-by-step breakdowns for debugging, mathematical proofs, and structured decision-making.
- Original V3: More direct but less explanatory — faster for simple queries but weaker in multi-step reasoning.
3. Benchmark Performance
- DeepSeek V3–0324: Outperforms competitors like Claude 3.5 Sonnet in code generation, logical reasoning, and accuracy.
- Original V3: Delivers reliable but mid-tier results, making it more suitable for general-purpose tasks.
4. Token Usage & Response Style
- Original V3: Highly token-efficient, producing short, direct responses — ideal for cost-effective chatbots and automation.
- DeepSeek V3–0324: 31.8% more verbose, often expanding explanations with examples and structured answers — better for clarity but increases token costs.
5. Cost Implications
- Original V3: $0.34 per benchmark task → Best for large-scale, budget-conscious deployments.
- DeepSeek V3–0324: $1.12 per benchmark task → Justifiable for high-value applications like debugging, tutoring, and advanced coding.
6. Potential Drawbacks
- DeepSeek V3–0324: May over-explain or ask unnecessary follow-up questions, potentially slowing workflows.
- Original V3: May oversimplify responses, lacking depth in technical or analytical queries.
Best Use Cases & Recommendations
✔ When to Choose DeepSeek V3–0324
Advanced coding projects (React, Angular, complex JavaScript).
Technical support & tutoring (detailed explanations required).
Research, data analysis, and structured reasoning tasks.
When performance matters more than cost.
✔ When to Stick with Original DeepSeek V3
High-volume, low-cost automation (e.g., customer service bots).
Quick fact-based queries and summaries.
Basic scripting and lightweight backend tasks.
When conciseness is more important than depth.
DeepSeek V3–0324 vs Claude 3.7 Sonnet
A user has just come up with some Benchmark numbers comparing DeepSeek V3–0324 vs Claude 3.7 Sonnet, and the model looks to be overpowering Claude 3.7 quite easily.
- DeepSeek V3–0324 is the clear winner, excelling in coding, logic, and complex problem-solving. It’s the best choice for developers and technical users.
- Claude 3.7 remains a solid generalist, but it falls behind in math-heavy and structured reasoning tasks.
For most users, DeepSeek V3–0324 is the better pick, especially if coding or numerical reasoning is required.
Conclusion
DeepSeek V3–0324 is a major upgrade, excelling in reasoning, coding, and structured problem-solving, even outperforming Claude 3.7 in key areas. While it’s more verbose and costly, the trade-off is justified for advanced coding, debugging, and tutoring. If you need efficiency and low-cost automation, the original DeepSeek V3 remains a solid choice.
DeepSeek V3–0324 vs DeepSeek-V3 was originally published in Data Science in your pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
