Gemini 4 Argon is Google DeepMind’s multimodal reasoning model for complex software engineering, financial research, legal drafting, and cybersecurity defense. It supports sustained reasoning across long, multi-step workflows, combining coding capabilities with chart and long-video understanding. It can also autonomously discover, validate, and patch software vulnerabilities.
Pricing
Input Modalities
- Text
- Vision
- Audio
- Video
Output Modalities
- Text
Context length
- 1.05M tokens
Max output
- 65.5K tokens
Capabilities
- Thinking
- Streaming
- Tool calling
- Web search
- URL context
- Code interpreter
- Computer use
- File search
- Memory tool
- Structured outputs
- Structured decision
- Citations
- Prompt caching
- Background mode
- Server-side sessions
Providers
VertexAI gemini-4-argon
Pricing$2$10
Cache Read$0.1/M tokens
Input Video$2/M tokens
Input Audio$2/M tokens
Web Search$0.014/request
PrecisionⓘNot Disclosed
Latency19.7S
Throughput33.0TPS
Uptime
99.84% uptime 2 days ago
99.99% uptime yesterday
99.99% uptime today
Google AI Studio gemini-4-argon
Pricing$2$10
Cache Read$0.1/M tokens
Input Video$2/M tokens
Input Audio$2/M tokens
Web Search$0.014/request
PrecisionⓘNot Disclosed
Latency12.5S
Throughput41.6TPS
Uptime
99.95% uptime 2 days ago
99.97% uptime yesterday
100.00% uptime today
Performance for gemini-4-argon
Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).
Uptime
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Try this model
Python