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google/gemini-4-argonCatalogue: Indexed in the reference catalogue; nothing on this deployment answers as this model yet.
Published by the vendor; the platform bills at the target's declared price.
Google google/gemini-4-argon
Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across...
Gemini 4 Argon earns a request when it needs step-by-step reasoning, tool calls, very long inputs; a short, general request would go to Gemini 4 Argon (batch) instead - $1.00 per million input tokens against $2.00 here; and the hardest requests, where the quality floor rules it out, to Claude Opus 5.5 (batch). Your rules decide the floor; the trace shows the comparison for every request.
Lower input price and an intelligence index within 5 points.
Nothing on this deployment answers as this model yet. Connect your own provider under Model providers, or add a target to your catalogue - the router picks it up without retraining.
# targets.yaml - declare a target that runs Gemini 4 Argon
targets:
- id: gemini-4-argon
kind: llm
name: Gemini 4 Argon
metadata:
model: google/gemini-4-argon # links the target to this catalogue entry
capabilities:
domains: [general]
supports_tools: true
context_window: 1048576
cost:
usd_per_1k_tokens: 0.006000Input list price per million tokens; Gemini 4 Argon is the most expensive of 13 listed.
Photo: RDNE Stock project
Cached input reads $0.10 per 1M
A higher intelligence index, best first, with the price it comes at.
Other models by the same vendor in the catalogue.
The catalogue prints input and output prices and what three typical monthly workloads cost on each.