Gemini 4 Argon
Gemini 4 Argon is Google's frontier model, announced on 13 October 2026 and trained to work longer and hold context better than any previous Gemini. It targets long-horizon engineering, security remediation and dense multimodal analysis, with a unified 1M-token input and output window and tunable thinking levels — now on Hoodgen AI.
Technical Specifications
Live data fetched from the OpenRouter model registry — always up to date.
What is Gemini 4 Argon?
Gemini 4 Argon is a frontier reasoning model from Google, announced on 13 October 2026. Where earlier Gemini releases optimised for fast answers, Argon is optimised for long work: multi-file debugging, codebase refactors from C++ to Rust, vulnerability patching, and analysis that spans video, PDFs and spreadsheets in one grounded output. Input, thinking and output share a single 1M-token window, so a large repository and its resulting diff fit in the same request.
Features & Capabilities
Frontier Reasoning
A 52.6 Intelligence Index and 57.1 percent on Humanitys Last Exam — top 2 percent of measured models.
Unified 1M Context
Input, thinking and output share one 1M window, so a whole repository plus its diff fits in a single pass.
Tunable Thinking
Set thinking_level to low, medium or high to trade latency against reasoning depth per request.
Long-Horizon Engineering
Built to refactor legacy systems, patch vulnerabilities and verify its own work until the job is done.
Multimodal Synthesis
Cross-reference long video, PDFs, spreadsheets and ledgers, then return one grounded answer.
95% Cached Discount
Implicit caching is automatic, cutting the cost of repeated context input to a twentieth.
Who Is Gemini 4 Argon For?
Engineering & Security
C++ to Rust migrations, legacy untangling, and vulnerability remediation that runs to a measurable target.
Business Document Work
Turn mixed folders of filings, ledgers and slide decks into dashboards, summaries or audited redlines.
3D, Simulation & Apps
Spatial reasoning plus visual code generation for Three.js and WebGPU scenes and full-stack builds.
Long-Running Agents
Multi-step agents that plan, call tools, check their own output and keep going.
Gemini 4 Argon Pricing
- $2.00 per million input tokens, $10.00 per million output
- 95 percent off cached input tokens — applied automatically
- Run it from your Hoodgen free credits, no credit card required
- Higher allowances on Hoodgen AI paid plans
What Makes Gemini 4 Argon Different?
The differentiator is stamina. Instead of answering once and stopping, Argon is built to hand a measurable target — untangle a legacy subsystem, remediate a security finding, prove a theorem — build its own verification harness, test hypotheses against compiler or runtime output, and iterate until the task is complete. That is why Google moved the output ceiling: input, thinking and output now share one 1M-token window, up from a 64K cap on previous models.
Control comes from thinking_level rather than sampling knobs. Low keeps latency and cost down for classification and routing, medium is the default balance for interactive coding and tool use, and high spends the tokens on multi-file root-cause debugging, formal mathematics and upfront architecture design. Temperature, top_p and top_k are ignored on this model, and explicit context caching is replaced by automatic implicit caching at a 95 percent discount.
On measured work it lands in the top two percent of models: a 52.6 Intelligence Index, 57.1 percent on Humanitys Last Exam, 61.8 percent on SciCode and 57.1 percent on Terminal-Bench 4.0, with an 84.9 percent non-hallucination rate on AA-Omniscience. In practice that means an agent that can hold a large codebase, a long investigation or a dense document set in view and still finish.
Gemini 4 Argon Performance
Independent measurements put Argon at a 52.6 Intelligence Index with 57.1 percent on Humanitys Last Exam, 61.8 percent on SciCode for scientific computing and 57.1 percent on Terminal-Bench 4.0 for agentic terminal work. Latency sits near 2.7 seconds with roughly 45 tokens per second, and provider uptime is 99.99 percent over the last three days.
What Can You Do With Gemini 4 Argon?
Argon is the model you reach for when the task will not finish in one shot. Point it at a legacy subsystem, a security finding, a dense folder of filings or a half-built 3D scene, and let it work through the problem end to end inside a single 1M-token window.
Best Use Cases
- Multi-file debugging and root-cause analysis
- Large-scale refactors and language migrations
- Security vulnerability remediation
- Cross-format document and ledger analysis
- 3D scenes, simulations and full-stack builds
Gemini 4 Argon Privacy & Data Policy
Gemini 4 Argon is developed by Google and served through OpenRouter, which routes to Google Vertex or Google AI Studio. Prompts and completions are processed by Google's API under Google's terms.
Because this is a third-party hosted model, avoid pasting passwords, personal data or proprietary source code you would not share with an external provider. Hoodgen AI keeps cheaper and free alternative models available on the same account, so you can switch without losing your work.
Gemini 4 Argon vs Other Models
Frequently Asked Questions
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