Why it Matters
The introduction of Gemini 3 presents significant improvements in ‘reasoning’ resulting in higher-quality, if increasingly verbose, outputs. However, this increased quality comes a significant cost increase: IBRS tests place it around 30 times that of its predecessor, Gemini 2.5 pro. This cost differential necessitates a careful evaluation for organisations deploying AI solutions built on Gemini.
For use cases prioritising high throughput, low latency, and cost-efficiency, Gemini 2.5 Flash remains the recommended option. Gemini 2.5 Flash is best suited for processing large volumes of requests and building agentic systems that involve numerous tool calls. Its strengths lie in operational efficiency for scenarios where the primary concern is the economic execution of AI tasks.
Conversely, Gemini 3 Pro is positioned for applications demanding advanced reasoning capabilities, particularly in complex mathematical, scientific, or multimodal problem-solving. This model is intended for situations where quality and benchmark performance outweigh cost considerations, suggesting it offers a higher degree of accuracy or sophistication in its outputs.
When considering competitors, OpenAI’s latest models and Microsoft Copilot also are now arguably, trailing that of Google Gemini. Beyond output quality, Gemini 3 differentiators include granular control parameters, such as `thinking_level` and `media_resolution`, which will assist developers in controlling model behaviour and output fidelity. Its capabilities for image (Nano Banana) and video generation are also noted strengths.
Who’s Impacted?
- Chief Information Officers (CIOs): Evaluate the total cost of ownership for AI deployments using a mix-model approach. Weigh the performance gains of Gemini 3 against its significantly higher cost, and assessing its strategic fit within the organisation’s AI roadmap.
- AI Development Teams & Architects: These teams will need to conduct thorough testing and benchmarking to determine if Gemini 3’s advanced reasoning and multimodal capabilities provide sufficient value to justify the increased operational expenditure, particularly for complex projects. In addition, single AI applications should make use of multiple models at different staging, leveraging the most costly advanced reasoning models only when needed, and reverting to smaller, faster and dramatically less costly models for everything else.
- Project Leads: They are responsible for understanding the cost implications and performance benefits of selecting Gemini 3 versus Gemini 2.5 Flash for specific project requirements, ensuring alignment with budget and technical objectives.
Next Steps
- Conduct a pilot program with Gemini 3 for a specific, complex use case where state-of-the-art reasoning is critical, and compare the outputs and resource consumption against existing Gemini 2.5 (or competitor) implementations.
- Monitor public benchmarks and independent reviews as they emerge to gain a broader perspective on emerging Model’s performance relative to alternatives from OpenAI, Microsoft, Mistral and Anthropic.
- Evaluate the new API parameters (`thinking_level`, `media_resolution`, `thought_signature`) to determine if they offer sufficient control to optimise cost-performance for specific applications.
