DeepSeek-V3 Outperforms GPT-4.5
- DeepSeek has officially released the technical report for its DeepSeek-V3 model update.
- This achievement is attributed to improved post-training methods. DeepSeek-V3-0324 utilizes the same base model as its predecessor, dispelling rumors that this version's base model was R2.
- The new version has a parameter volume of approximately 660 billion, differing from the previously speculated 685 billion.
DeepSeek-V3 Model Update: Surpassing GPT-4.5 in Key Areas
Table of Contents
- DeepSeek-V3 Model Update: Surpassing GPT-4.5 in Key Areas
- deepseek-V3: Your Questions Answered (2025 Update)
- What is DeepSeek-V3?
- What are the Key Improvements in DeepSeek-V3?
- How Does DeepSeek-V3 Compare to Previous Versions?
- What Are the Practical Applications of DeepSeek-V3?
- Is deepseek-V3 Open Source?
- How Can I Access and Use DeepSeek-V3?
- What Are the Deployment Requirements for DeepSeek-V3?
- What’s the context length for the open-source DeepSeek-V3?
- DeepSeek-V3 at a Glance
2025-03-26
DeepSeek has officially released the technical report for its DeepSeek-V3 model update. The new version, V3, reportedly outperforms GPT-4.5 in mathematics and code-related evaluations.
This achievement is attributed to improved post-training methods. DeepSeek-V3-0324 utilizes the same base model as its predecessor, dispelling rumors that this version’s base model was R2.
The new version has a parameter volume of approximately 660 billion, differing from the previously speculated 685 billion. The open-source version features a context length of 128K (with 64K context provided via web pages, apps, and APIs).
For private deployments, only the checkpoint and tokenizer_config.json (tool calls related changes) require updating.
Users can experience this model version by logging into the official web page, app, or mini program and entering the dialog interface. The API interface and usage method remain consistent.
The company recommends using the new V3 version for non-complex reasoning tasks.
Enhanced Capabilities
The company has demonstrated the new version’s capabilities across various dimensions:
Front-End Growth
The generated code is reportedly more usable with improved visual effects.
Chinese Writing
Compared to the R1 version, further optimizations have been implemented, particularly improving the quality of medium and long-form content. For example, the model can write an essay about Su Shi’s life:
Chinese Search
When connected to the internet, the search output content of the new V3 version is more detailed, accurate, and visually appealing. The model can also generate a 3,000-word market report.
The new V3 version has also improved in tool calls, role-playing, and Q&A chats.
Early testers have explored various capabilities,including creating small games.
This model version adopts a permissive MIT open-source protocol and can be deployed directly on M3 Ultra’s Mac Studio, lowering the barrier to entry for large-scale model development applications.
deepseek-V3: Your Questions Answered (2025 Update)
Are you curious about the latest advancements in AI? DeepSeek-V3 is making waves, and this Q&A provides insights into its capabilities.
What is DeepSeek-V3?
DeepSeek-V3 is the newest iteration of the DeepSeek AI model. It’s designed with enhanced capabilities and performance improvements, particularly in areas like mathematics and code-related tasks. Key features include improved post-training methods.
What are the Key Improvements in DeepSeek-V3?
DeepSeek-V3 boasts notable advancements:
Performance: It reportedly outperforms GPT-4.5 in mathematics and code-related evaluations.
Model Size: It has a parameter volume of approximately 660 billion.
Open Source: The open-source version offers a 128K context length (with 64K context accessible via the web).
How Does DeepSeek-V3 Compare to Previous Versions?
deepseek-V3 utilizes the same base model as its predecessor, DeepSeek-R1, dispelling earlier speculation that the underlying base model was R2.
What Are the Practical Applications of DeepSeek-V3?
DeepSeek-V3 is versatile and suitable for various tasks:
front-End growth: Generates more usable code with improved visual effects.
Chinese Writing: Produces high-quality medium and long-form content, even essay-length pieces.
Chinese Search: Provides detailed, accurate, and visually appealing search results; capable of generating comprehensive reports.
Tool Calls, Role-Playing, and Q&A: Enhanced capabilities in conversational AI.
game Creation: Early testers have demonstrated the ability to create small games.
Is deepseek-V3 Open Source?
yes,DeepSeek-V3 adopts a permissive MIT open-source protocol. This makes it easier for developers to deploy and experiment with the model.
How Can I Access and Use DeepSeek-V3?
Users can access DeepSeek-V3 through:
The official DeepSeek web page
The DeepSeek app
* Mini-programs
The API interface and usage methods are consistent with previous versions. Consider using DeepSeek-V3 for non-complex reasoning tasks.
What Are the Deployment Requirements for DeepSeek-V3?
For private deployments, only the checkpoint and tokenizer_config.json (for tool call-related changes) require updating. it can be deployed directly on an M3 Ultra’s Mac Studio.
What’s the context length for the open-source DeepSeek-V3?
The open-source version features a context length of 128K (with 64K context provided via web pages, apps, and APIs).
DeepSeek-V3 at a Glance
| Feature | Description |
| ————————— | —————————————————————————————————————————————————————— |
| performance | Outperforms GPT-4.5 in mathematics and code-related evaluations. |
| Model Size | Approximately 660 billion parameters. |
| Base Model | Same as its predecessor, DeepSeek-R1. |
| Context Length | 128K (open-source version, with 64K context available via web pages, apps, and APIs).|
| Open Source Licence | MIT |
| Deployment | Can be deployed on Mac Studio. |
| Key Improvements | Front-End Growth, Chinese Writing, Chinese Search, Tool Calls, Role-Playing, Q&A. |