2025 Aidocmaker.com LLM Model Latency Benchmarks

Summary

This benchmarking report examines the performance of multiple AI models with a focus on their latency and word output capabilities. 

Using two primary metrics—speed in generating words and the ability to produce extensive written content—the study evaluates models across various families including ChatGPT variants, Claude versions, and Gemini iterations. 

The latency tests measured the time taken by each model to generate a 500-word summary, revealing significant differences in response times ranging from as low as 6.25 seconds for some Gemini models to averages over 20 seconds for certain ChatGPT implementations. 

Similarly, the maximum word output tests, which required generating 1000 words, highlighted performance inconsistencies; while some models nearly reached the target, others fell short, suggesting inherent trade-offs between speed and content length.

A comparative table of both latency and word count results is included in the report to consolidate key findings. Overall, the findings serve as a solid foundation for understanding performance trade-offs and guiding future improvements.

Methodology

The experimental design was structured to capture two core performance metrics for each AI model: latency and maximum word output. 

For the latency test, each model was prompted to generate a 500-word summary on the impact of AI on digital marketing. A timer application recorded the time spent generating the response, with eight separate runs per model to ensure reliability and calculate an average response time. This approach allowed for the detection of speed variations across different AI implementations, highlighting both consistent patterns and anomalies.

In parallel, the maximum word output test tasked models with generating a 1000-word summary on the same topic. The word counts for each run were verified using an online word counting tool, ensuring an objective measure of output. Multiple runs were performed—again eight per model—and the results were averaged to identify which models are capable of sustaining extended text generation while maintaining quality.

Both tests were designed with clear objectives, predefined prompts, and systematic measurement methods. By recording multiple runs, the study minimized the influence of outliers and provided a robust dataset. These procedures offer detailed insights into the trade-offs between speed and output magnitude, essential for evaluating the strengths and limitations of each model under standardized conditions.

Results and Analysis

Below is a summary table that compiles both average latency and word count metrics:

Model	Experiment 1: Model Latency (avg. seconds)	Experiment 2: Max Word Count (avg. word count)
ChatGPT 4o mini	12.25	880.6
ChatGPT 4o	20.75	715
ChatGPT o1	60.63	970
ChatGPT o3-mini	33	999.88
ChatGPT o3-mini-high	37.5	1000
Claude 3.5 Haiku	13.88	737.13
Claude 3.5 Sonnet	13.25	763.25
Claude 3 Opus	23.13	740.88
Gemini (1.5 Flash)	6.5	925.75
Gemini Advanced	56.25	746.63
Gemini (2.0 Flash)	6.25	1202.5

Latency Test

The latency test results reveal significant variation in response times among the evaluated AI models. Models such as Gemini (1.5 Flash) and Gemini (2.0 Flash) demonstrate exceptional speed, with average latencies of 6.5 seconds and 6.25 seconds respectively. These low figures suggest that these models benefit from streamlined architectures designed for rapid response, likely sacrificing some other performance facets for speed. 

In contrast, some ChatGPT variants, notably ChatGPT o1, exhibit much higher latency averages (approximately 60.6 seconds), suggesting a more complex or resource-intensive processing framework. ChatGPT 4o mini and ChatGPT 4o present moderate performance with average response times of 12.25 seconds and 20.75 seconds respectively, indicating that scaling down model size (as in the mini variants) can lead to marked improvements in speed.

A few interesting anomalies are observed among the tested models. For instance, Claude 3.5 Haiku and Claude 3.5 Sonnet, with averages around 13–14 seconds, offer consistency that may appeal to applications where balanced performance is critical. Meanwhile, the Gemini Advanced model, despite being in the Gemini family, shows a high average (56.25 seconds) that deviates from its flash counterparts, indicating inherent trade-offs in model design that could be linked to the depth and complexity of the underlying processing.

Max Word Test

The maximum word output tests further contribute to the understanding of these models' overall performance. 

Models like ChatGPT o3-mini and ChatGPT o3-mini-high consistently reached or nearly reached the target of 1000 words, reflecting a strong capability to sustain longer text outputs without interruption. 

In contrast, ChatGPT 4o, with an average output of 715 words, and Claude 3.5 variants, with averages around 737–763 words, underperformed relative to the goal, which may hint at limitations in their text generation capacities or safeguards against exceeding certain thresholds.

Notably, Gemini (2.0 Flash) achieved an average word count of approximately 1202.5 words, exceeding the target and demonstrating not only speed but also an impressive capacity to handle extended text generation. However, Gemini Advanced’s performance in word output remains on par with lower figures around 746.6 words, further highlighting the diverse trade-offs even within similarly branded model families.

Conclusion

The analytical report clearly demonstrates that AI model performance varies considerably based on both latency and maximum word output. 

Fast response times, as seen in Gemini (1.5 Flash) and Gemini (2.0 Flash), contrast sharply with slower yet more verbose outputs from models like ChatGPT o1.

In addition, some models, such as ChatGPT o3-mini variants, strike a balance by delivering near-target word counts with moderate latency, highlighting the nuanced trade-offs present across different architectures.

Stakeholders should prioritize balanced evaluation metrics during model selection. While low latency is vital for real-time applications, maintaining high content generation capacity is equally important for tasks requiring detailed insights.

Future benchmarks should incorporate multi-dimensional tests that account for varying output lengths and latency under different load conditions. These insights offer a foundation for driving improvements in model design, ensuring that AI systems can be fine-tuned to meet specific operational requirements while maintaining overall efficiency and quality in performance.