{"article":{"slug":"google-gemini-4-argon-launch","kind":"model","source":{"title":"Gemini 4 Argon: Built to solve your hardest problems","url":"https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-argon/","date":"2026-10-13"},"headline":"Gemini 4 Argon is half price until November 30, then $4 and $20","whatChanged":"Google made Gemini 4 Argon generally available on October 13 through the Gemini API in AI Studio, its enterprise Gemini platform, Antigravity for AI Ultra subscribers, and the Gemini app and AI Mode for AI Ultra and Pro subscribers. The introductory price is $2 per million input tokens and $10 per million output tokens, with cached input 95% cheaper. A Google footnote says the public rates become $4 and $20 after November 30, 2026. The window is 1 million input and output tokens combined.\n\nGoogle's model card compares Argon with GPT-6 Astra, Claude Fable 5.1 and Claude Opus 5.5. Argon scores 68.9% on the Vals Index against 67.0% for Opus 5.5, and 19.6% on Harvey's Legal Agent Benchmark (results via Vals AI) against 6.7% for Fable 5.1. It trails Opus 5.5 on Terminal-bench 4.0, 57.4% against 66.4%. Google ran Argon at its highest thinking setting, and most rival figures are the providers' own reports or public leaderboards, so settings are not matched.\n\nOn Gray Swan's prompt-injection test, as charted by Google, attacks succeeded 0.7% of the time within 15 attempts, against 1.0% for Opus 5.5 and Fable 5.1 and 8.5% for GPT-6 Astra. The model card says Google cannot rule out that Argon reaches its cybersecurity critical capability level, and applies safeguards.","whyItMatters":"Price is the first decision. A team comparing Argon with Opus 5.5 should price both at the later rate, because the introductory rate is half of what Google will charge from December. Google's own table has Argon ahead on the Vals Index and behind Opus 5.5 on Terminal-bench 4.0, so the better model depends on the job. Google chose the benchmarks and ran its own model at its highest thinking setting.\n\nFor any agent that reads email or web pages, the Gray Swan chart is the most useful part. A 0.7% success rate for attackers is low and still above zero, so keep permissions narrow and test with your own documents.","issuer":"Google","glance":"Google released Gemini 4 Argon October 13 at $2 in and $10 out per million tokens, rising to $4 and $20 after November 30.","keyFact":"$2 in, $10 out per 1M tokens to Nov 30","action":"Compare Argon with your current model on one task, priced at $4 and $20.","visuals":[{"url":"https://storage.googleapis.com/gweb-uniblog-publish-prod/images/gemini_4_cyber_evals_gray_swan_i.width-1600.format-webp.webp","alt":"Bar chart of Gray Swan indirect prompt injection attack success at 15 attempts, lower is better: Gemini 4 Argon 0.7%, Claude Opus 5.5 and Fable 5.1 1.0%, GPT-6 Astra 8.5%, Kimi K3 52.7%.","caption":"Google's chart of Gray Swan's indirect prompt-injection test, embedded on its September 30 Argon page; the October 13 launch post cites the 0.7% figure. Attack success rate within 15 attempts, lower is better: Gemini 4 Argon 0.7%, Claude Opus 5.5 1.0%, Claude Fable 5.1 1.0%, GPT-6 Astra 8.5%, GPT-6 Sol 10.1%. Google published the chart and chose the comparison set. It measures one attack type and does not establish safety for a particular email, browser or document workflow.","credit":"Google DeepMind; results attributed to Gray Swan","source":{"title":"Gemini 4 Argon: our next era of frontier intelligence","url":"https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/","date":"2026-09-30"}}],"attachments":[{"title":"Gemini 4 Argon Model Card","url":"https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-4-Argon-Model-Card.pdf","description":"Benchmark table against GPT-6 Astra, Fable 5.1 and Opus 5.5, known limitations, and the frontier safety assessment, including the cybersecurity capability level Google says it cannot rule out.","source":{"title":"Gemini 4 Argon: Built to solve your hardest problems","url":"https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-argon/","date":"2026-10-13"}},{"title":"Gemini 4 Argon model evaluation methodology","url":"https://deepmind.google/models/evals-methodology/gemini-4-argon","description":"States that Argon ran at the highest thinking settings through the Gemini API, that most non-Gemini results are providers' self-reported numbers, and which benchmark results come from Vals AI or public leaderboards.","source":{"title":"Gemini 4 Argon: Built to solve your hardest problems","url":"https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-argon/","date":"2026-10-13"}}],"companyLabel":"Public edition"},"issue":{"id":"2026-10-19","publishedLabel":"Week of October 19, 2026","isLatest":true},"position":{"index":1,"total":5},"next":{"slug":"meta-muse-wonder-launch","headline":"Meta's Muse Wonder leads six of 14 benchmark rows and publishes no price"},"session":null}