Gemini Models
Google organises its Gemini model family into Flash (fast, high-throughput), Pro (balanced capability and speed), and Deep Think (iterative multi-path reasoning for hard problems). Unlike OpenAI, Google does not publicly disclose parameter counts for any Gemini model. Context window size and capability benchmarks are the primary differentiators.
The Model Family
Gemini 2.0 Flash
Context: 1M tokens
The workhorse API model. Designed for fast, low-latency production applications — chatbots, streaming responses, agentic pipelines, and multimodal inputs. Supports native tool use, image input, audio input, video input, and document understanding in a single API call. Ideal when response speed and per-token cost matter more than maximum reasoning depth.
Gemini 2.5 Flash
Context: 1M tokens
The faster, more cost-efficient sibling of 2.5 Pro. Used in the Gemini Free tier app and as the model behind free-tier Deep Research runs. Significantly better reasoning than 2.0 Flash at still-competitive latency. A strong default for most developer use cases where 2.5 Pro would be overkill on cost.
Gemini 2.5 Pro
Context: 2M tokens — largest at launch (Mar 2025)
Google's most capable model at its March 2025 launch. Strong at complex reasoning, long-context tasks (entire codebases, lengthy legal documents, multi-chapter books), and multimodal understanding. Used in the Gemini Pro plan app and available in AI Studio. The 2M token context window was the largest of any mainstream model at launch.
Gemini 3 Flash
Context: 200K tokens
Speed-optimised variant of the Gemini 3 family. Highest throughput, lowest latency in the generation. Best suited for high-volume API workloads — chat interfaces, real-time summarisation, classification pipelines — where cost efficiency and throughput are the priority.
Gemini 3 Pro
Context: 1M tokens (Nov 2025)
The flagship Gemini 3 model. Emphasis on long-horizon reasoning, multimodal understanding, persistent memory, and more reliable agentic behaviour. Trained to reduce hallucinations in agentic workflows. Available via AI Studio, Vertex AI, and the Gemini Pro plan app. The developer-focused Deep Research agent runs on Gemini 3 Pro.
Gemini 3 Deep Think
Context: 1M tokens
Rolling out to Google AI Ultra subscribers. Uses iterative multi-path reasoning — the model thinks through multiple solution paths before producing a final answer. Best suited for deep scientific analysis, complex mathematics, multi-step technical reasoning, and problems where accuracy matters far more than speed.
Flash vs Pro — When to Use Which
The Flash/Pro split follows a consistent pattern across Gemini generations. Flash models offer lower latency and cost at the expense of maximum reasoning depth. Pro models prioritise capability, particularly on complex multi-step tasks, long documents, and agentic reliability.
A practical heuristic: use Flash for anything requiring fast responses at scale (chatbots, summarisation, classification). Use Pro when the task requires extended reasoning, a very large context window, or reliable tool use in an agentic pipeline.
Multimodal Capabilities
All Gemini models (2.0 and above) are natively multimodal — they accept text, images, audio, video, and document inputs in a single API call. There is no separate vision model or audio transcription step; everything goes through the same model endpoint. This simplifies architecture for multimodal applications and reduces latency compared to chaining specialised models.
Checklist
- Does Google publicly disclose parameter counts for Gemini models?
- What context window does Gemini 2.5 Pro offer, and why was it notable at launch?
- When would you choose Gemini 3 Flash over Gemini 3 Pro for a developer project?
- What makes Gemini 3 Deep Think different from Gemini 3 Pro in terms of reasoning?
- Which Gemini plan is required to access Gemini 3 Deep Think?