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Ranking ChatGPT Models by API Latency

API latency, the time it takes for an API to process a request and return a response, is a critical factor in the performance and user experience of applications using large language models (LLMs). This analysis ranks various ChatGPT models based on their API latency, drawing from multiple sources to provide a comprehensive overview. Latency is typically measured in seconds or milliseconds, and can be expressed as time to first token (TTFT) or as the time per generated token. It's important to note that performance can vary based on factors such as network speed, server load, input length, and output length.

Key Metrics

When evaluating API latency, several metrics are important:

Time to First Token (TTFT): The time elapsed from sending the API request to receiving the first token of the response. This is a crucial metric for perceived responsiveness.
Latency per Token: The average time taken to generate each token in the response. This metric is useful for estimating the total response time for longer outputs.
Output Speed (Tokens per Second): The rate at which tokens are generated, often measured in tokens per second. This metric indicates how quickly the model can produce the full response.

Ranking of ChatGPT Models by API Latency

Based on the available data, here is a ranking of ChatGPT models from fastest to slowest in terms of API latency:

GPT-3.5 Turbo

GPT-3.5 Turbo is consistently cited as one of the fastest models in the ChatGPT family. It is designed for applications requiring quick responses and lower costs.
- Latency: Approximately 0.29 seconds (TTFT) according to some sources, though other sources cite a latency of 73 milliseconds per token when hosted by OpenAI, and 34 milliseconds per token when hosted by Azure. The Azure-hosted version is significantly faster.
- Output Speed: Around 73-80 tokens per second when hosted by OpenAI, with Azure deployments showing even faster speeds.
- Analysis: The speed of GPT-3.5 Turbo makes it suitable for real-time applications and scenarios where quick responses are essential. The difference in performance between OpenAI and Azure hosting is significant, with Azure providing a substantial speed advantage.
GPT-4o (Various Versions)

GPT-4o, the latest generation model, is designed for improved performance and efficiency. Different versions of GPT-4o show varying latency characteristics.
- Latency: The original GPT-4o model (May 2024) has a latency of approximately 0.41-0.45 seconds (TTFT). Later versions (August 2024 and November 2024) show similar latency, with some sources indicating 0.37 seconds for the November 2024 version.
- Output Speed: GPT-4o has a high output speed, with some sources reporting around 134.9 tokens per second.
- Analysis: GPT-4o is a strong contender for applications requiring a balance of speed and quality. While not as fast as GPT-3.5 Turbo, it offers a significant improvement over previous GPT-4 models. The consistency in latency across different versions suggests a focus on maintaining performance.
GPT-4o Mini

GPT-4o Mini is a smaller variant of the GPT-4o model, designed for efficiency.
- Latency: Approximately 0.41-0.63 seconds (TTFT), slightly slower than the full GPT-4o model.
- Output Speed: Around 112.2 tokens per second, which is faster than GPT-3.5 Turbo but slower than the full GPT-4o model.
- Analysis: GPT-4o Mini provides a good balance between speed and resource usage, making it suitable for applications where the full capabilities of GPT-4o are not necessary.
o1-mini and o1-preview

These models are described as high-quality and efficient, with a focus on both quality and speed.
- Latency: Approximately 0.41 seconds (TTFT), matching the performance of the fastest GPT-4o variants.
- Output Speed: Specific output speed metrics are not consistently available, but these models are designed for efficient performance.
- Analysis: These models offer competitive latency and are likely optimized for both quality and speed, making them a good option for various applications.
GPT-4 Turbo

GPT-4 Turbo is an enhanced version of GPT-4, designed for improved speed and cost-effectiveness.
- Latency: Approximately 0.42-0.68 seconds (TTFT), with some sources indicating a slightly higher latency than GPT-4o.
- Output Speed: Around 50-60 tokens per second.
- Analysis: GPT-4 Turbo offers a good balance between performance and cost, making it suitable for applications that require high-quality outputs without the highest possible speed.
GPT-4 (Base)

The base GPT-4 model is known for its high quality but has a higher latency compared to other models.
- Latency: Approximately 0.42 seconds (TTFT) according to some sources, while others cite 196 milliseconds per generated token, which translates to a significantly slower response time for longer outputs.
- Output Speed: Around 27 tokens per second.
- Analysis: GPT-4 is slower than other models in the ChatGPT family, making it less suitable for applications requiring real-time responses. However, its high quality makes it a good choice for tasks where accuracy is paramount.
GPT-4 (Various Specialized Models)

Specialized models like GPT-4 Vision, GPT-4o Audio, GPT-4o Realtime, and GPT-4o Speech Pipeline are designed for specific tasks.
- Latency: Approximately 0.43 seconds (TTFT), consistent with the general-purpose GPT-3.5 Turbo models.
- Output Speed: Specific output speed metrics are not consistently available, but these models are optimized for their respective tasks.
- Analysis: These models offer consistent latency, making them suitable for their specialized applications.

Additional Considerations

Azure vs. OpenAI Hosting: Models hosted on Azure often exhibit lower latency compared to those hosted directly by OpenAI. For example, Azure-hosted GPT-3.5 Turbo is significantly faster than the OpenAI-hosted version.
Input and Output Length: Longer inputs and outputs can increase latency. The latency per token metric is useful for estimating the total response time for longer outputs.
Network Conditions: Network speed and server load can affect API latency. These factors are external to the models themselves but can impact overall performance.

Summary Table

The following table summarizes the latency metrics for the various models discussed:

Model	Latency (TTFT, seconds)	Output Speed (tokens/second)	Notes
GPT-3.5 Turbo	0.29 (some sources), 0.073 per token (OpenAI), 0.034 per token (Azure)	73-80 (OpenAI), higher (Azure)	Fastest overall, Azure hosting significantly faster
GPT-4o (November 2024)	0.37	~135	Very fast, comparable to GPT-3.5 Turbo
GPT-4o (May 2024)	0.41-0.45	~135	Fast, balanced performance
GPT-4o Mini	0.41-0.63	~112	Slightly slower than full GPT-4o
o1-mini and o1-preview	0.41	N/A	Competitive latency, optimized for speed and quality
GPT-4 Turbo	0.42-0.68	50-60	Good balance of speed and quality
GPT-4 (Base)	0.42 (some sources), 0.196 per token (other sources)	~27	Slower than other models, high quality
GPT-4 (Specialized Models)	0.43	N/A	Consistent latency, optimized for specific tasks

Conclusion

For applications requiring the lowest latency, GPT-3.5 Turbo, especially when hosted on Azure, is the fastest option. The various versions of GPT-4o also offer very competitive latency, with the November 2024 version showing particularly strong performance. GPT-4o Mini and o1-mini/o1-preview provide a good balance between speed and resource usage. GPT-4 Turbo is a good choice for applications that require high-quality outputs without the highest possible speed, while the base GPT-4 model has the highest latency among the models listed. Specialized models like GPT-4 Vision and GPT-4o Audio have consistent latency, making them suitable for their specific tasks.

It is important to consider the specific requirements of your application when choosing a model. If speed is the primary concern, GPT-3.5 Turbo or the latest GPT-4o versions are the best choices. If high quality is more important, GPT-4 or GPT-4 Turbo may be more suitable, despite their higher latency. Always refer to the most recent data from reliable sources for the most up-to-date information.

Note: Latency metrics can vary based on specific use cases and conditions such as network speed and server load.