GPT-6 Astra vs Gemini 4 Argon

Quick Answer

GPT-6 Astra and Gemini 4 Argon are the flagship models from OpenAI and Google as of October 2026. On the main independent benchmark we track, Artificial Analysis’s Intelligence Index, they are tied. On price they are far apart: Argon lists at a fifth of Astra’s token price, and in Artificial Analysis’s run it cost about 40% less per task even though it used more output tokens. Neither tops the index; Anthropic’s Claude Opus 5.5 scores higher.

Choose based on where you work, what the task is, and what a finished task costs you, not on which logo is newer.

At a Glance

Artificial Analysis figures are independent measurements at a point in time and can change as models and providers are updated.

What Each Model Is For

GPT-6 Astra is OpenAI’s frontier model, positioned for computer use, coding, cybersecurity, science, and professional work. In ChatGPT it appears at the Pro thinking level, and it powers OpenAI Dots. It is also available through the OpenAI API, Microsoft Foundry, AWS Bedrock, OpenRouter, and GitHub Copilot. OpenAI’s cheaper GPT-6.1 Sol now covers much of the same ground; see GPT-6.1 Sol vs Claude Opus 5.5.

Gemini 4 Argon is Google’s frontier model for long, multi-step work in software engineering, enterprise knowledge work, and cybersecurity defense. It started as a limited release for vetted cyber defenders and has been generally available since October 13 in the Gemini app, AI Mode in Search, the paid Gemini API, Google AI Studio, and Antigravity.

Benchmarks: Tied, With Caveats

The independent picture is a tie at 53 on Artificial Analysis’s Intelligence Index, a composite of reasoning, knowledge, coding, and agentic tests. Note that Astra’s score is at maximum reasoning effort and Argon’s at high effort.

Vendor numbers tell different stories. Google’s launch tables show Argon leading or tied on more benchmarks than Astra or Claude Opus 5.5, but independent write-ups note Argon trails on some agentic coding suites such as Terminal-Bench. OpenAI highlights Astra’s strength in computer use and long tasks, and Artificial Analysis reported Astra leading its AutomationBench business-workflow test at launch. Treat each vendor’s selection as its own case, and test on your workload.

Cost per Completed Task

List price is where Argon’s advantage is obvious: $2/$10 against $10/$50. But price per token is only half the calculation. Argon used nearly twice as many output tokens as Astra to complete Artificial Analysis’s index, so the gap narrows from 5x to about 1.6x per task ($1.99 versus $3.26). That pattern, cheaper per token but more verbose, is common, and it is why cost per completed task is a better measure than the rate card.

Two price events matter. Argon’s launch price doubles on December 1, 2026, per Google’s pricing page, which would put it at $4/$20, still well below Astra. And OpenAI’s own GPT-6.1 Sol, at $2/$10, scores within a point of Astra on the same index, so for many OpenAI users the realistic comparison is Sol versus Argon.

Speed and Long Outputs

Artificial Analysis measured Argon generating faster (about 79 versus 58 tokens per second). Both models spend significant time thinking before answering at high effort, so time to first output is long on hard prompts. Google’s 1M-token output limit for Argon is unusual and helpful for generating very long documents or large code changes in one pass.

Ecosystem

Astra fits if you work in ChatGPT, Codex, Dots, or Microsoft and AWS clouds. Argon fits if you work in Google Workspace, Search, Antigravity, or Google Cloud. Both are capable agent models; the surrounding tools, admin controls, and data agreements often decide it.

Choose GPT-6 Astra If

You rely on ChatGPT, Codex, or Dots, you need it through Azure or Bedrock, your own tests show it finishes your tasks with fewer retries, or computer-use and business-workflow tasks are your main use.

Choose Gemini 4 Argon If

You want frontier capability at a much lower price, you work in Google’s ecosystem, you need very long outputs, or you are building high-volume agent workloads where per-task cost compounds.

When Neither Is Ideal

For the highest independent score, look at Claude Opus 5.5 vs Gemini 4 Argon. For most routine work, a fast model such as Gemini 3.8 Flash or GPT-6 Luna is cheaper and quick enough, with escalation to a frontier model only when needed; see model escalation. For open weights, see Muse Spark Open vs Mistral Large 4.

Final Recommendation

On independent evidence these two are peers in capability, and Argon is clearly cheaper. Unless your work is tied to OpenAI’s products or your own evaluation shows Astra finishing your tasks more reliably, Argon offers better value. Run both on a sample of real tasks and compare total cost and accepted results.

Related Comparisons

• Claude Opus 5.5 vs Gemini 4 Argon
• GPT-6.1 Sol vs Claude Opus 5.5
• Gemini 4 Argon vs GPT-6.1 Sol
• ChatGPT vs Gemini

For strategy, see model routing and How to Make Cheaper AI Models Work Smarter.