Gemini 4 Argon
At a glance
- Price per 1M tokens (input)
- $2.00
- Price per 1M tokens (output)
- $10.00
- Context
- 1,048,576 tokens
- Free access
- yes (see below)
Refreshed daily; data verified October 15, 2026. Published October 15, 2026.
Gemini 4 Argon by Google is designed for developers and enterprises building applications that require visual understanding combined with logical reasoning and external tool integration. It excels at tasks such as interpreting diagrams or charts, executing multi-step workflows via function calls, and generating structured outputs based on complex inputs. The model distinguishes itself through its tight integration with Google’s ecosystem, enabling seamless access to APIs and services during reasoning chains without requiring custom orchestration layers.
Specifications & pricing
| Input (per 1M tokens) | $2.00 |
|---|---|
| Output (per 1M tokens) | $10.00 |
| Cache read (per 1M tokens) | $0.10 |
| Context window | 1,048,576 tokens |
| Max output | 65,536 tokens |
| Capabilities | images, tool calling, reasoning |
LiteLLM community dataset (MIT), verified October 15, 2026. Official Google (Gemini) pricing.
What Gemini 4 Argon would cost on your workload — run it through the cost calculator →
Where to try Gemini 4 Argon 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 types of tasks is Gemini 4 Argon best suited for?+
It is well suited for applications that involve analyzing visual inputs like schematics or product images, making decisions based on that input, and then triggering actions in external systems such as databases or APIs through function calls.
How does a developer begin using this model in their workflow?+
Access is provided through Google’s AI platform where users can send multimodal prompts containing both text and images, define available functions for tool calling, and receive reasoned responses that may include calls to those functions.
How does Gemini 4 Argon differ from other models in the Gemini series?+
Compared to earlier versions, it places stronger emphasis on combining image understanding with reliable step-by-step reasoning and tool use, making it more capable in scenarios where visual context must inform sequential actions.
What are current limitations users should be aware of?+
While strong in reasoning and tool use, the model may still struggle with highly ambiguous visual inputs or extremely long chains of dependent functions, and its performance depends on the clarity and specificity of the provided function definitions.
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.