With the release of GPT-4.1 Nano, OpenAI is directly positioning itself against Google’s Gemini 2.0 Flash — two models that now compete not only technologically but also on pricing. Both systems are highly efficient large language models, priced at just $0.10 per million input tokens and $0.40 per million output tokens. They also offer a massive context window of up to one million tokens, allowing for extensive instruction sets and the integration of enterprise knowledge. Clearly, these models are aimed at businesses looking for scalable AI solutions for real-world applications like classification, document processing, or support automation.
The launch is part of OpenAI’s deliberate model strategy. While GPT-4.1 Nano is optimized for speed and scalability, the “Mini” variant covers logical applications, and GPT-4.1 via API is targeted at developers. Similarly, Google’s Gemini strategy offers its Pro variant, currently regarded as one of the best models for developers. Businesses can thus flexibly balance computational resources and costs based on their use cases. Especially for areas like chatbots, ticket triage, or email classification — where deep reasoning is less critical but efficiency is key — GPT-4.1 Nano and Gemini Flash become very attractive options.
Testing and Comparing Both Models
At ComposableAI, we conducted a practical test comparing the new high-context LLMs: OpenAI’s GPT-4.1 Nano and Google’s Gemini Flash 2.0. The goal was to automatically rate news headlines in the field of „AI in health and fitness apps“ — not via traditional fine-tuning, but purely through context and examples.
Both models received the same prompt: A list of ten example titles with expected relevance scores (between 0 and 1) and twenty new titles that needed to be evaluated.
The results were returned in structured JSON format and analyzed.
At the core of our testing setup is a modular program, specifically developed for working with large language models like GPT-4.1 Nano or Gemini Flash. It processes any list of news headlines — for example, from newsfeeds, industry updates, or social media — and automatically generates a “prompt” for the LLMs to evaluate the headlines. This means the system creates a fully LLM-compatible input structure, including examples and clear scoring instructions in JSON format.
As a result, users receive a prioritized output directly from the chosen model.
How the Comparison Process Works
First, ten manually curated example titles with known relevance scores (between 0.0 and 1.0) are prepared as a prompt. These examples serve as a guide for the LLM, helping it understand the intended evaluation logic. Next, the system takes the 20 titles to be evaluated and integrates them into the same prompt, combined with a clear instruction: “Please return a relevance score for each title in JSON format.”
The result is a precise, model-agnostic prompt that can be generated within seconds.
The system becomes particularly powerful when integrated into the ComposableAI toolchain:
Generated prompts can be fed into different models (such as GPT-4.1 Nano, Gemini Flash, or Claude).
The returned results are automatically parsed, compared, and analyzed — including difference analysis, mean comparisons, and threshold evaluation. This enables fast testing to determine which model is best suited for different types of classification — all without fine-tuning, purely through context and examples.
The Result: Google Gemini Flash Takes the Lead
The results revealed clear differences in model quality.
GPT-4.1 Nano delivered fast evaluations but showed weaknesses in differentiating between the middle and lower relevance ranges. In some cases, fewer news titles were classified than were submitted. In contrast, Gemini Flash impressed with a complete and finely detailed evaluation of all 20 titles. The scale distribution largely matched expectations and remained stable across multiple runs. For high-volume automatic content classification requiring finely graduated relevance ratings, we therefore recommend Gemini Flash.
It’s important to note that the test was conducted immediately after the release of GPT-4.1 Nano.
The observation that not all 20 titles were consistently rated suggests that model stability and prompt processing may not yet be fully mature in this version