I recently conducted an experiment as part of a company project - and I’m excited (and a little frustrated) to share my findings. While I’ve been using OpenAI models for a long time, I decided to test out DeepSeek (which is everywhere these days) to see if its much‑talked‑about low cost really translates into great performance. What I found was a clear trade‑off between cost and latency that every developer should consider. My Experiment: Setup and Measurements For this experiment, I set up a simple script that runs two prompts using the same underlying logic. The input in my test was a fixed text to analyze, and the script then executed two separate prompts. I ran this script using three different configurations: DeepSeek Chat Model on Nebius Platform: Measured response times: 6.95 sec, 11.8 sec, and 12.44 sec Average: Approximately 10.4 seconds DeepSeek Chat Model via the Original API: Measured response times: 20.1 sec, 16.69 sec, and 22.62 sec Average: Approximately 19.8 seconds OpenAI GPT‑4o: Measured response times: 4.62 sec, 4.19 sec, and 3.77 sec Average: Approximately 4.2 seconds I meticulously recorded these timings by taking notes on a second screen while the script was running. The results clearly show that while GPT‑4o is fast—delivering responses in just over 4 seconds on average - DeepSeek’s responses are significantly slower. Depending on the access method, DeepSeek takes roughly 10 to 20 seconds per call. Cost Analysis: How Much Cheaper Is DeepSeek? From a pricing perspective, DeepSeek is very attractive. The quoted prices are: DeepSeek Original API (deepseek‑chat): Input Tokens: $0.27 per million tokens Output Tokens: $1.10 per million tokens OpenAI GPT‑4o: Input Tokens: $2.50 per million tokens Output Tokens: $10.00 per million tokens A direct cost comparison reveals: For input tokens: 2.50/0.27≈9.26 For output tokens: 10.00/1.10≈9.09 Thus, DeepSeek’s pricing makes it roughly 9 times cheaper than GPT‑4o for both input and output tokens. This dramatic difference is especially attractive for high‑volume applications or projects where budget constraints are paramount. Additional Considerations Server Resource Constraints During my testing, I discovered that DeepSeek’s platform is currently facing server capacity issues. Their official notice explains: Due to current server resource constraints, we have temporarily suspended API service recharges to prevent any potential impact on your operations. This not only affects performance but also signals potential scalability limits for real‑world applications—especially those where quick response times are critical. Task Suitability and Use‑Case Trade‑Offs In my experiment, it became evident that if you’re running non‑interactive or batch processes where latency isn’t a critical factor, DeepSeek’s cost‑effectiveness (being roughly 9 times cheaper) could be a major win. However, for interactive applications—such as chatbots or real‑time data processing—where user experience depends on fast response times, the 10- to 20‑second delays from DeepSeek may outweigh the cost benefits. Some experts suggest that parallelizing requests might help mitigate these delays, but that adds another layer of complexity. Conclusion The decision between DeepSeek and GPT‑4o comes down to a clear trade‑off between cost and speed: DeepSeek offers a staggering cost advantage—approximately 9 times cheaper per million tokens. This makes it an attractive option for high‑volume, non‑real‑time queries. However, my experiment showed that its response times range from 10 to nearly 20 seconds, which can be a significant drawback for interactive applications. GPT‑4o delivers rapid responses (around 4.2 seconds on average) thanks to its robust and scalable infrastructure. For applications where speed is essential, GPT‑4o is the clear winner, albeit at a higher cost. As I discovered firsthand, while the allure of lower pricing is strong, the performance gap is a crucial factor to consider. For developers and enterprises, the choice should be guided by the specific needs of your application. If speed is paramount, you may want to stick with GPT‑4o (or even explore emerging models like OpenAI’s o3). But if your project can tolerate some delay and budget constraints are a major concern, DeepSeek presents a compelling alternative. This experiment has given me valuable insights into the current AI landscape - and I’m excited to see how these trade‑offs evolve as the technology continues to advance. I recently conducted an experiment as part of a company project - and I’m excited (and a little frustrated) to share my findings. While I’ve been using OpenAI models for a long time, I decided to test out DeepSeek (which is everywhere these days) to see if its much‑talked‑about low cost really translates into great performance. What I found was a clear trade‑off between cost and latency that every developer should consider. OpenAI DeepSeek My Experiment: Setup and Measurements For this experiment, I set up a simple script that runs two prompts using the same underlying logic. The input in my test was a fixed text to analyze, and the script then executed two separate prompts. I ran this script using three different configurations: DeepSeek Chat Model on Nebius Platform: Measured response times: 6.95 sec, 11.8 sec, and 12.44 sec Average: Approximately 10.4 seconds DeepSeek Chat Model via the Original API: Measured response times: 20.1 sec, 16.69 sec, and 22.62 sec Average: Approximately 19.8 seconds OpenAI GPT‑4o: Measured response times: 4.62 sec, 4.19 sec, and 3.77 sec Average: Approximately 4.2 seconds DeepSeek Chat Model on Nebius Platform: Measured response times: 6.95 sec, 11.8 sec, and 12.44 sec Average: Approximately 10.4 seconds DeepSeek Chat Model on Nebius Platform: Nebius Measured response times: 6.95 sec, 11.8 sec, and 12.44 sec Average: Approximately 10.4 seconds Measured response times: 6.95 sec, 11.8 sec, and 12.44 sec Measured response times: 6.95 sec, 11.8 sec, and 12.44 sec Average: Approximately 10.4 seconds Average: Approximately 10.4 seconds Average: DeepSeek Chat Model via the Original API: Measured response times: 20.1 sec, 16.69 sec, and 22.62 sec Average: Approximately 19.8 seconds DeepSeek Chat Model via the Original API: Measured response times: 20.1 sec, 16.69 sec, and 22.62 sec Average: Approximately 19.8 seconds Measured response times: 20.1 sec, 16.69 sec, and 22.62 sec Measured response times: 20.1 sec, 16.69 sec, and 22.62 sec Average: Approximately 19.8 seconds Average: Approximately 19.8 seconds Average: OpenAI GPT‑4o: Measured response times: 4.62 sec, 4.19 sec, and 3.77 sec Average: Approximately 4.2 seconds OpenAI GPT‑4o: Measured response times: 4.62 sec, 4.19 sec, and 3.77 sec Average: Approximately 4.2 seconds Measured response times: 4.62 sec, 4.19 sec, and 3.77 sec Average: Approximately 4.2 seconds Average: I meticulously recorded these timings by taking notes on a second screen while the script was running. The results clearly show that while GPT‑4o is fast—delivering responses in just over 4 seconds on average - DeepSeek’s responses are significantly slower. Depending on the access method, DeepSeek takes roughly 10 to 20 seconds per call. Cost Analysis: How Much Cheaper Is DeepSeek? From a pricing perspective, DeepSeek is very attractive. The quoted prices are: DeepSeek Original API (deepseek‑chat): Input Tokens: $0.27 per million tokens Output Tokens: $1.10 per million tokens OpenAI GPT‑4o: Input Tokens: $2.50 per million tokens Output Tokens: $10.00 per million tokens DeepSeek Original API (deepseek‑chat): Input Tokens: $0.27 per million tokens Output Tokens: $1.10 per million tokens DeepSeek Original API (deepseek‑chat): Input Tokens: $0.27 per million tokens Output Tokens: $1.10 per million tokens Input Tokens: $0.27 per million tokens Input Tokens: $0.27 per million tokens Input Tokens: Output Tokens: $1.10 per million tokens Output Tokens: $1.10 per million tokens Output Tokens: OpenAI GPT‑4o: Input Tokens: $2.50 per million tokens Output Tokens: $10.00 per million tokens OpenAI GPT‑4o: Input Tokens: $2.50 per million tokens Output Tokens: $10.00 per million tokens Input Tokens: $2.50 per million tokens Input Tokens: Output Tokens: $10.00 per million tokens Output Tokens: A direct cost comparison reveals: For input tokens: 2.50/0.27≈9.26 For output tokens: 10.00/1.10≈9.09 For input tokens: 2.50/0.27≈9.26 For output tokens: 10.00/1.10≈9.09 Thus, DeepSeek’s pricing makes it roughly 9 times cheaper than GPT‑4o for both input and output tokens. This dramatic difference is especially attractive for high‑volume applications or projects where budget constraints are paramount. 9 times cheaper Additional Considerations Server Resource Constraints During my testing, I discovered that DeepSeek’s platform is currently facing server capacity issues. Their official notice explains: Due to current server resource constraints, we have temporarily suspended API service recharges to prevent any potential impact on your operations. Due to current server resource constraints, we have temporarily suspended API service recharges to prevent any potential impact on your operations. This not only affects performance but also signals potential scalability limits for real‑world applications—especially those where quick response times are critical. Task Suitability and Use‑Case Trade‑Offs In my experiment, it became evident that if you’re running non‑interactive or batch processes where latency isn’t a critical factor, DeepSeek’s cost‑effectiveness (being roughly 9 times cheaper) could be a major win. However, for interactive applications—such as chatbots or real‑time data processing—where user experience depends on fast response times, the 10- to 20‑second delays from DeepSeek may outweigh the cost benefits. Some experts suggest that parallelizing requests might help mitigate these delays, but that adds another layer of complexity. Conclusion The decision between DeepSeek and GPT‑4o comes down to a clear trade‑off between cost and speed: DeepSeek offers a staggering cost advantage—approximately 9 times cheaper per million tokens. This makes it an attractive option for high‑volume, non‑real‑time queries. However, my experiment showed that its response times range from 10 to nearly 20 seconds, which can be a significant drawback for interactive applications. GPT‑4o delivers rapid responses (around 4.2 seconds on average) thanks to its robust and scalable infrastructure. For applications where speed is essential, GPT‑4o is the clear winner, albeit at a higher cost. DeepSeek offers a staggering cost advantage—approximately 9 times cheaper per million tokens. This makes it an attractive option for high‑volume, non‑real‑time queries. However, my experiment showed that its response times range from 10 to nearly 20 seconds, which can be a significant drawback for interactive applications. DeepSeek offers a staggering cost advantage—approximately 9 times cheaper per million tokens. This makes it an attractive option for high‑volume, non‑real‑time queries. However, my experiment showed that its response times range from 10 to nearly 20 seconds, which can be a significant drawback for interactive applications. DeepSeek GPT‑4o delivers rapid responses (around 4.2 seconds on average) thanks to its robust and scalable infrastructure. For applications where speed is essential, GPT‑4o is the clear winner, albeit at a higher cost. GPT‑4o delivers rapid responses (around 4.2 seconds on average) thanks to its robust and scalable infrastructure. For applications where speed is essential, GPT‑4o is the clear winner, albeit at a higher cost. GPT‑4o As I discovered firsthand, while the allure of lower pricing is strong, the performance gap is a crucial factor to consider. For developers and enterprises, the choice should be guided by the specific needs of your application. If speed is paramount, you may want to stick with GPT‑4o (or even explore emerging models like OpenAI’s o3). But if your project can tolerate some delay and budget constraints are a major concern, DeepSeek presents a compelling alternative. This experiment has given me valuable insights into the current AI landscape - and I’m excited to see how these trade‑offs evolve as the technology continues to advance.
