Gemini 4 Argon (High): Intelligence, Performance and Price Analysis
Google's Gemini 4 Argon (High) scores 53 on the Artificial Analysis Intelligence Index with a 1M token context window, priced at $2.00 per 1M input and $10 per 1M output.
summary is enough The metrics provide a concise benchmark overview, though the discussion offers better context on the model's actual market positioning.
summary
Google released Gemini 4 Argon (High), a reasoning model featuring text and image input, a 1M token context window, and a score of 53 on the Artificial Analysis Intelligence Index. It is priced at $2.00 per 1M input tokens and $10.00 per 1M output tokens, performing competitively with other frontier reasoning models. Hacker News commenters debated its competitive standing, with some viewing it as a routine catch-up release while others highlighted its pricing efficiency and lower hallucination rates.
key points
- Gemini 4 Argon (High) scores 53 on the Artificial Analysis Intelligence Index and supports a 1M token context window.
- Pricing is set at $2.00 per 1M input tokens and $10.00 per 1M output tokens, averaging $1.99 per intelligence task.
- Commenters note it matches or nears rival models like Opus 5.5 on cost-per-task efficiency, though opinions vary on whether Google is innovating or just catching up.
who should read it
AI engineers, developers, and technical leads tracking frontier LLM performance and pricing metrics.
discussion mixed
consensus
Commenters are split on whether the release represents a major milestone for Google or a lukewarm catch-up effort, though many acknowledge its competitive pricing and efficiency.
pushback
A few commenters push back on dismissive takes, noting that its task cost and low hallucination rates effectively rival top offerings from Anthropic and OpenAI.
What are you talking about? It’s comparable to Opus 5.5 on “high” (54 vs 53; $1.82 vs $1.99), crushes every model except the most modern OAI/Ant ones, has way lower hallucination than every existing model and probably broader support for multimodal like existing Gemini models. This is so ludicrously off base.
It defends the model's competitive viability by pointing out comparable intelligence scores, low hallucination rates, and cost efficiency against peer models.
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