Naren Manoharan


At Wolfia, we’re building an AI agent designed to handle technical sales questions—such as generating accurate responses to security questionnaires and detailed API explanations—by synthesizing information from platforms like Notion and Slack. To identify the most effective reasoning model for these tasks, we compared two advanced models: **o3-mini** and **GPT-4o**. This article breaks down our evaluation, highlights key findings, and explains why we selected **o3-mini** for our production environment.

## Evaluation overview

We tested both models on tasks representative of technical sales scenarios, measuring:

- **Accuracy:** Factual correctness (maximum 40 points)
- **Clarity (relevance):** Precision and relevance to queries (maximum 30 points)
- **Completeness:** Thoroughness of responses (maximum 30 points)
- **Total score:** Overall performance (maximum 100 points)
- **Latency:** Response time in seconds

## Performance highlights

- **Accuracy:** GPT-4o averaged slightly higher at 39.2 compared to o3-mini’s 39.0, but o3-mini consistently reached the maximum accuracy score more frequently (73% vs. 47%).
- **Clarity:** o3-mini led with an average clarity score of 29.8 versus GPT-4o’s 29.5, with o3-mini achieving maximum clarity on 87% of tasks, significantly outperforming GPT-4o’s 67%.
- **Completeness:** GPT-4o slightly outperformed o3-mini (29.7 vs. 29.1), demonstrating fewer dips in completeness. o3-mini occasionally dropped scores slightly on complex inputs.
- **Total Score:** GPT-4o’s overall average was slightly higher (98.5 vs. 97.9), but o3-mini reached the maximum total score more often (60% compared to GPT-4o’s 47%).

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## Latency insights

Surprisingly, GPT-4o was only marginally faster overall, averaging 42.7 seconds per response compared to o3-mini’s 46.5 seconds. The range of latency was wider for o3-mini (29.5–69.9s) than GPT-4o (29.7–60.8s), yet the median latencies were almost identical (41.3s vs. 41.5s).

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## Unique insights

- **Peak performance:** o3-mini excelled notably at the upper performance boundary, consistently hitting maximum clarity (87%) and accuracy (73%) more frequently than GPT-4o. However, its performance showed variability, especially on particularly challenging inputs, suggesting sensitivity to highly complex queries.
- **Consistency vs. excellence:** GPT-4o provided steadier results, rarely scoring below 94 total points. o3-mini, in contrast, demonstrated remarkable highs and occasional lows, reflecting a “high-risk, high-reward” profile.

## Why we chose o3-mini for production

o3-mini’s exceptional clarity and peak accuracy align precisely with our primary goal: enabling sales and security teams to deliver concise, precise, and reliable answers. Its ability to consistently achieve maximum clarity and accuracy makes it particularly effective for crucial sales documents such as security questionnaires and technical queries, where precision directly impacts credibility and deal speed.

Furthermore, the slight latency variability is acceptable for our use case, and the leaner structure of o3-mini positions it well for future scalability as we expand our AI agent capabilities.

## Conclusion

Both o3-mini and GPT-4o are highly capable reasoning models, but o3-mini’s remarkable ability to consistently deliver precision and relevance in critical sales contexts makes it our clear choice. By integrating o3-mini into our production AI agent, we aim to drastically improve the efficiency and effectiveness of technical sales teams.


Why we chose o3-mini over GPT-4o to improve our AI agent’s accuracy and clarity for technical sales scenarios

o3-mini vs. GPT-4o: Do reasoning models improve accuracy for technical sales questions?

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