GPT-5 Nano's Performance Under Scrutiny Amidst Strong Showing by Qwen3-32B and Mistral Small in "Thinking Effort" Benchmarks

Recent discussions within the AI community have cast a critical eye on the performance of OpenAI's newly released GPT-5 Nano, particularly concerning its "thinking effort" capabilities, as compared to established models like Alibaba's Qwen3-32B and Mistral AI's Mistral Small. A tweet from "Lisan al Gaib" succinctly captured the sentiment, stating, "GPT-5-nano with high thinking effort behind Qwen3-32B and even Mistral Small yikes." This observation points to a surprising disparity in efficiency and reasoning prowess among leading large language models.

OpenAI's GPT-5 Nano, launched as part of the broader GPT-5 family in August 2025, was positioned as an edge-optimized, fast, and cost-efficient variant. While the flagship GPT-5 model was touted as a significant leap in intelligence, early testers and independent benchmarks have raised concerns about GPT-5 Nano's real-world performance, especially in complex coding and logical reasoning tasks. Some reports have described its results as "atrocious" when pushed for higher reasoning, failing to consistently format code or solve multi-step problems effectively.

In contrast, Alibaba's Qwen3-32B, introduced in April 2025, has garnered attention for its innovative "thinking mode" and "non-thinking mode," allowing it to dynamically adjust its computational effort based on task complexity. This dense model has demonstrated strong performance in coding, mathematics, and general reasoning benchmarks, with some analysts noting its competitiveness against models like OpenAI's own o1 and o3-mini, and even GPT-4.1. Its ability to explicitly manage "thinking effort" appears to give it an edge in tasks requiring deeper analysis.

Mistral AI's Mistral Small, released earlier in February 2024, is optimized for latency and cost, proving to be a robust performer for simpler tasks and ranking third on the MT-Bench. Although primarily designed for efficiency in less complex scenarios, the tweet's implication suggests that even this model might surpass GPT-5 Nano in tasks demanding "high thinking effort," highlighting the unexpected challenges faced by OpenAI's smaller GPT-5 variant. The comparison underscores a growing focus on not just raw capability, but also the efficiency and reliability of models when engaged in more demanding cognitive processes.

The ongoing evaluations suggest that while OpenAI aims for broad advancements with its GPT-5 series, the performance of its more compact models like GPT-5 Nano in high-effort reasoning tasks may not yet meet the high expectations set by its larger counterparts or by competitors. The "yikes" expressed in the tweet reflects a broader sentiment in the AI development community, prompting further scrutiny into how different model architectures and training methodologies translate into practical, reliable "thinking effort" in real-world applications.