Gemini Omni 1.2 Flash
At a glance
- Price per 1M tokens (input)
- $1.50
- Price per 1M tokens (output)
- $9.00
- Context
- 131,072 tokens
- Free access
- yes (see below)
Refreshed daily; data verified November 8, 2026. Published November 7, 2026.
Gemini Omni 1.2 Flash is a multimodal model from Google designed for teams that need to process images and text together. It suits tasks such as document analysis, visual question answering, and step-by-step reasoning for complex problems. Google's approach centers on integrating multimodal understanding directly into a single model rather than relying on separate specialized systems. This allows the model to interpret charts, diagrams, and photographs while maintaining logical coherence across long chains of thought.
Specifications & pricing
| Input (per 1M tokens) | $1.50 |
|---|---|
| Output (per 1M tokens) | $9.00 |
| Context window | 131,072 tokens |
| Max output | 65,536 tokens |
| Capabilities | images, reasoning |
LiteLLM community dataset (MIT), verified November 8, 2026. Official Google (Gemini) pricing.
What Gemini Omni 1.2 Flash would cost on your workload — run it through the cost calculator →
Where to try Gemini Omni 1.2 Flash for free
- Google (Gemini) offers a free chat — Gemini (free plan). A vendor's free chat may run a different model from the same family — the exact model is not guaranteed.
- 🎁 On our promo-codes page: Gemini for free: access and discounts.
Frequently asked questions
What tasks is this model good for?+
The model excels at image understanding combined with text reasoning. Common uses include extracting information from scanned documents, interpreting visual data such as charts and diagrams, and solving multi-step problems that require showing work before reaching a conclusion.
How do I get started?+
Access the model through Google's AI platform by selecting it from the model catalog. You can send prompts that include both text and images using the provided API or console interface. No separate image processing pipeline is required because the model handles multimodal input natively.
How does it differ from sibling models in the line?+
This variant prioritizes speed and efficiency for high-volume tasks while retaining multimodal capabilities. Other models in the family may focus on deeper reasoning at the cost of response time or lack native image understanding altogether.
What are the limitations?+
The model may struggle with extremely fine-grained visual details or highly specialized domain knowledge. Step-by-step reasoning can still produce errors if the underlying facts are incorrect, so outputs involving critical decisions should be reviewed by a human.
Compare with others
Org chart: how to move your company onto AI
A practical map: which company roles and processes AI agents can take over, where to start, and in what order to roll it out.