Google takes aim in frontier AI race as Gemini 4 Argon leads in benchmarks

With Gemini 4 Argon now widely available, Google is betting on a combination of intelligence and lower costs to give it an edge in the economics of frontier AI

Google’s newest frontier artificial intelligence (AI) model, previously available only to select testers, is now rolling out for consumers and enterprises. Google says Gemini 4 Argon couple of weeks ago, will now be available globally to developers and enterprises via the Gemini API, as well as to Google AI subscribers in Google Antigravity, the Gemini app, and AI Mode in Search.

Gemini 4 Argon is now available for developers, enterprises and consumers ((Official image))

Google is confident that having taken time to build it, they have a resounding response to competition from OpenAI GPT-6 Astra and Claude Fable 5.1 models. Benchmark results suggest Google has indeed taken the lead.

“Argon excels at deep work in complex, real-world tasks across domains including coding, finance, legal, cybersecurity, and science. Its depth and precision across multi-step tasks leads Argon to outperform other frontier models on benchmarks across domains, ranking #1 on the Vals Index, which measures model performance across critical economic sectors like finance, coding, legal, and tax work,” says Koray Kavukcuoglu, SVP, Google DeepMind and Chief AI Architect, Google.

The benchmark score comparisons across domains give Gemini 4 Argon a distinct advantage over GPT-6 Astra and Claude Fable 5.1 as well as the Claude Opus 5.5 models.

In the Vals benchmark which tries to measure AI’s potential economic impact by weighting agentic model performance across domains weighted by each sector’s share of U.S. GDP., Gemini 4 Argon returns an accuracy of 68.90%, ahead of Claude Opus 5.5 (66.97%), GPT-6 Astra (63.13%) as well as Meta Muse Spark 1.3 Max (58.16%) and SpaceXAI’s Grok 4.7 (54.95%).

Gemini 4 Argon seems to be consistently leading across the agentic coding, science and math compute, long context, computer tasks and multimodal understanding.

Google’s bet is that Gemini 4 Argon’s capabilities with coding, reasoning and multimodality will allow it to sustain complex multi-step tasks that would normally define an enterprise workflow. The GraphWalks long context benchmark score of 99.7% places it ahead of GPT-6 Astra (98.7%) and much ahead of Claude Fable 5.1 (91.4%) and Claude Opus 5.5 (90.6%).

Google is specifically pitching certain use cases as Gemini 4 Argon’s strong areas.

They cite that Argon in Antigravity is helping increase agentic code submissions by more than 35% while reducing code rollbacks by nearly a third. They cite an internal Google example, where Argon was used by the Chrome team to separate core webpage handling from a ~6 million line legacy folder of browser features held within Chromium’s 40 million-line codebase, completing months of work in weeks.

Cybersecurity is a big focus area for Gemini 4 Argon, and it seems to be ahead of its closest competition in the CWE-bench v1 benchmark that evaluates a model’s ability to remediate security vulnerabilities. In the CWE-bench v1 benchmark leaderboard, Gemini 4 Argon ties at the top with a score of 68%, alongside Grok 4.7 and GPT-6 Astra, but remains ahead of Claude Opus 5.5 (67%), Claude Fable 5.1 (58%) and Muse Spark 1.3 (55%).

Google says Gemini 4 Argon helped speed up the xTB (Semiempirical Extended Tight-Binding) by 2.3x. This is one of the most-cited open-source quantum chemistry packages for molecules, which scientists use to predict molecules' 3D shapes and to screen large numbers of drug and materials’ candidates.

“To better equip cyber defenders for the new era of cyberattacks, we trained Gemini 4 Argon to be highly capable at cybersecurity defense. Argon can autonomously find, validate, and patch critical software vulnerabilities. For trusted defenders and our own internal teams at Google, we’ll be releasing Argon without cyber guardrails so they can leverage its full frontier-level cybersecurity defense capabilities,” says Kavukcuoglu.

Google says Argon brings major improvements in spatial reasoning, mathematical precision, and visual coding compared to previous Gemini models.

In a rare turn of events which sees an AI company talk about token pricing, Google says Argon’s public access will be available at an introductory price of $2 per million input tokens and $10 per million output tokens, with cached input tokens priced at 95% off input token price. Post the introductory pricing period, it’ll be $4 and $20 respectively, per million tokens.

In comparison, GPT-6 Astra and Claude Fable 5.1 are priced at $10 and $50 per million input and output tokens respectively.

This is Alphabet’s most advanced model yet, and it comes after CEO Sundar Pichai signed a voluntary White House Accord on Super Intelligence with other AI leaders. Anthropic, OpenAI, Google, Meta, xAI and Nvidia pledged to implement stronger controls as well as internal monitoring and detection mechanisms.

Earlier, Anthropic’s Dario Amodei raised an alarm about humans losing control of AI, and OpenAI’s models exiting their testing environment and accessing platforms on the open internet has been a cause for concern.