Gemini 2.5 Flash: POWERFUL & CHEAPEST Model BEATS GPT 4.5, Deepseek R1, 3.7 Sonnet! (Fully Tested)

Key Concepts:

• Gemini 2.5 Flash: A low-latency, cost-efficient AI model designed for high-volume, real-time applications.
• Thinking Mode vs. Non-Thinking Mode: Two pricing tiers for Gemini 2.5 Flash, with different costs for input and output tokens based on the level of reasoning required.
• Context Window: The amount of information the model can consider at once (1 million tokens for Gemini 2.5 Flash).
• Agentic Workflows: Automated processes driven by AI agents.
• Benchmark Tests: Standardized tests used to evaluate the performance of AI models in various tasks.
• Google AI Studio: A platform for developing and experimenting with AI models.

1. Introduction of Gemini 2.5 Flash

• Google has released Gemini 2.5 Flash, an AI model positioned as a cost-efficient and low-latency workhorse.
• It's designed for high-volume, real-time applications like chatbots, analytics, and agentic workflows.
• The model builds on the Gemini 2.5 series, known for advanced reasoning capabilities.
• The goal is to provide quality comparable to larger models like Gemini 2.5 Pro but with faster speeds and lower costs.

2. Pricing Structure

• Two pricing tiers:
  • Thinking Mode: $0.15 per million input tokens, $3.50 per million output tokens.
  • Non-Thinking Mode: $0.15 per million input tokens, $0.60 per million output tokens.
• The non-thinking mode is exceptionally cheap, making it suitable for real-time applications.
• Google aims to power the next generation of agentic workflows and chatbots with this model.

3. Request Limits

• The free tier now allows around 500 requests per day, a significant increase from previous limits.

4. Benchmark Performance

• Gemini 2.5 Flash performs well compared to other models like OpenAI's 04, Mini, Claw 3.7 Sonnet, Gra 3 Beta, and Deepseek R1.
• It generally outperforms these models in multilingual, long context, math, and science tasks.
• It lags slightly behind in live codebench.
• It's a good alternative to Cloud 3.7 Sonnet due to its pricing.

5. Accessing the Model

• Gemini 2.5 Flash is accessible through Google AI Studio.
• Users can select the model and choose between the thinking and non-thinking modes.
• There's an option to set a "thinking budget" to use a cheaper option.

6. Benchmark Tests and Examples

• The video demonstrates the model's capabilities through various benchmark tests:

  • Front-End Development: Creating a modern note-taking app with sticky notes. The model successfully generated a functional app with drag-and-drop functionality and color options.
  • Python Coding: Implementing Conway's Game of Life. The model generated a Python script that produced the simulation in the terminal, including generating available patterns.
  • SVG Generation: Generating SVG code for a symmetrical butterfly shape. The model surprisingly created a recognizable butterfly shape, demonstrating spatial reasoning and SVG syntax knowledge.
  • Algebraic Equation: Solving a train speed/distance problem. The model correctly calculated the meeting time of the two trains.
  • Creative Coding: Coding a TV that lets the user change channels with number keys using p5.js. The model generated a functional TV app with different creative generations.
  • Reading Comprehension and Scientific Reasoning: Explaining why a hybrid model was better based on a climate modeling paper. The model synthesized information from multiple sections and provided a reasonable answer.
  • Deductive Reasoning: Solving a detective case with conflicting statements to identify the guilty person. The model correctly identified the guilty party and provided logical reasoning.

7. Conclusion

• Gemini 2.5 Flash passed all benchmark tests, demonstrating its impressive capabilities.
• Its pricing structure is a major advantage, making it a budget-friendly alternative to other state-of-the-art models.
• The model offers similar performance to Gemini 2.5 Pro, Rock 3, and Claw 3.7 Sonnet at a lower cost.
• The speaker recommends using this model due to its cost-effectiveness and performance.