GPT-4o Mini VS. Gemini 1.5 Flash comparison in 2025

Explore the key differences between OpenAI and Google's latest cost-effective language models

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GPT-4o Mini: Strengths and Advantages

GPT-4o Mini is OpenAI's cost-efficient small model, designed to make advanced AI capabilities more accessible. It offers impressive performance at a fraction of the cost of larger models.

Key strengths include:

  • Multimodal capabilities (text and vision)
  • Large context window of 128K tokens
  • Strong performance in reasoning tasks
  • Improved efficiency and significantly lower cost compared to larger models
  • Support for up to 16.4K output tokens per request
  • Knowledge cutoff up to October 2023

GPT-4o Mini is particularly well-suited for applications requiring a balance between advanced capabilities and cost-effectiveness.

Best Use Cases for This Model

  1. High-Volume Data Processing: GPT-4o Mini's large context window and efficiency make it ideal for processing full code bases or extensive conversation histories in applications.
  2. Real-Time Customer Support: Its low latency and cost-effectiveness make GPT-4o Mini perfect for powering fast, real-time customer support chatbots.
  3. Multimodal Applications: With support for both text and vision inputs, GPT-4o Mini is suitable for developing applications that require processing and understanding of multiple data types.

Gemini 1.5 Flash: Strengths and Advantages

Gemini 1.5 Flash is Google's advanced language model, designed for fast performance and improved capabilities. It offers significant improvements over previous models at a competitive price point.

Key strengths include:

  • Multimodal capabilities (text and vision)
  • Massive context window of 1 million tokens
  • Advanced reasoning and problem-solving abilities
  • Extremely fast output speed
  • Support for over 100 languages
  • Knowledge cutoff up to November 2023

Gemini 1.5 Flash is particularly well-suited for applications requiring a balance between advanced capabilities, speed, and cost-effectiveness.

Best Use Cases for This Model

  1. Large-Scale Information Processing: Gemini 1.5 Flash's massive context window makes it ideal for analyzing and processing large volumes of data, such as entire codebases or extensive documents.
  2. Real-Time AI Applications: With its extremely fast output speed, Gemini 1.5 Flash excels in real-time applications like live customer support, instant content generation, and rapid data analysis.
  3. Multilingual and Multimodal Tasks: Supporting over 100 languages and having multimodal capabilities, Gemini 1.5 Flash is perfect for diverse applications requiring language understanding and visual processing.

In summary

When comparing GPT-4o Mini and Gemini 1.5 Flash, several key differences emerge:

  • Context Window: Gemini 1.5 Flash offers a much larger context window (1 million tokens) compared to GPT-4o Mini (128K tokens), allowing for processing of significantly larger data volumes.
  • Speed: Gemini 1.5 Flash has a faster output speed at 163.6 tokens per second, compared to GPT-4o Mini's 86.8 tokens per second.
  • Latency: GPT-4o Mini has lower latency with a Time to First Token (TTFT) of 0.45 seconds, while Gemini 1.5 Flash has a TTFT of 1.06 seconds.
  • Cost: Gemini 1.5 Flash is more cost-effective, with a blended price of $0.53 per million tokens, compared to GPT-4o Mini's $0.15 for input and $0.60 for output per million tokens.
  • Performance: Both models perform similarly on benchmarks, with GPT-4o Mini slightly outperforming Gemini 1.5 Flash on MMLU (82.0% vs 78.9% for 5-shot) and MMMU (59.4% vs 56.1%).
  • Maximum Output: GPT-4o Mini can generate up to 16.4K tokens per request, while Gemini 1.5 Flash is limited to 8,192 tokens.
  • Language Support: Gemini 1.5 Flash supports over 100 languages, while GPT-4o Mini's language support is not explicitly specified but is described as multilingual.

For most applications requiring a balance between advanced capabilities, speed, and cost-effectiveness, both models offer compelling options. Gemini 1.5 Flash may be preferable for tasks requiring extensive context processing or extremely fast output, while GPT-4o Mini might be better suited for applications needing lower latency or slightly higher performance on certain benchmarks.

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