Gemini 4 Argon vs GPT-6: Specs, Benchmarks and Which to Pick

Gemini 4 Argon (Google, 13 October 2026) and GPT-6 Astra (OpenAI, 3 September 2026) are the two vendors' flagship models, and on paper they're close. In Google's published table Argon beats Astra on 14 of 19 benchmarks, ties one, and trails on four, including FrontierSWE v2 and OSWorld 2.0. Argon is far cheaper than Astra per token ($2/$10 introductory, $4/$20 from December, against $10/$50) and accepts video, audio and PDF input, which OpenAI's model pages for Sol and Luna don't list. GPT-6 is easier to get for free (Luna powers free ChatGPT) and has a cheaper middle tier in GPT-6 Sol and 6.1 Sol. Pick by job, not by headline.

Quick answer

Key facts side by side

Prices are as of October 2026, standard tier, short prompts. GPT-6 prompts over 272K input tokens are billed at higher rates; Argon has no long-context surcharge.

Benchmarks: what each vendor published

Two warnings before the numbers. First, every figure below is vendor-reported; nobody independent has rerun these yet. Second, Google and OpenAI ran different harnesses and effort settings, so a 1-point gap is noise, and you shouldn't mix columns from different sources.

Google's table (Gemini 4 Argon vs GPT-6 Astra)

From DeepMind's Gemini model page. Higher is better.

Google's table also includes Claude Opus 5.5, which leads Terminal-bench 4.0 (66.4%) and PostTrainBench (49.3%) there.

OpenAI's numbers for GPT-6 Astra

OpenAI's Astra launch post doesn't include Gemini 4 Argon (it predates it), but it does report several of the same benchmarks:

The numbers mostly agree, which is a good sign that Google took Astra's figures from OpenAI's own reporting. OpenAI also claims Astra results Google doesn't list, such as 96.0% on GPQA Diamond and 97.6% on FrontierMath Tier 4 (v2).

Where GPT-6 Sol fits

OpenAI reports GPT-6 Sol at 68.8% on DeepSWE v1.1 (max effort) and 60.5% on OSWorld 2.0 offline (xhigh). For 6.1 Sol it gives relative claims instead of a full table, for example that it "matches GPT-6 Astra" on DeepSWE v1.1 at roughly one fifth of the cost. Sol is the fairer price comparison for Argon, since both list $2 input and $10 output today, but no vendor has published a head-to-head of the two.

Which to pick for which job

Long documents, video and huge outputs: Gemini 4 Argon

Argon's unified 1M budget and long decode, plus native video, audio and PDF input, make it the stronger choice for "read all of this and write a lot back" work. Google's long-context GraphWalks gap at 256K to 1M (84.2% vs 71.8%) is the largest it reports. The catch: outputs beyond 64K need background mode and polling.

Computer use and terminal-heavy agents: GPT-6 Astra

Both vendors' tables show Astra ahead on OSWorld 2.0, and Google's own table has it narrowly ahead on Terminal-bench 4.0. If your agent clicks through real software, start your evaluation with Astra, and see AI agent security before you give any model a live desktop.

Knowledge work (finance, legal, business workflows): test Argon first

Google's biggest claimed leads are on Vals Index, Finance Agent v2, Harvey's legal benchmark and AutomationBench. These are exactly the domains where you should run your own eval set, because domain benchmarks vary a lot by prompt and tooling.

Cost-sensitive production: GPT-6.1 Sol, Luna, or Argon before December

At $2 / $10, 6.1 Sol and introductory Argon cost the same. After 30 November, Argon doubles to $4 / $20 and Sol becomes half the price. For volume work, Luna at $0.10 / $0.50 is in a different price class from both. See our GPT-6 Sol vs Luna guide for tiering.

Consumers

If you just want the best free assistant, ChatGPT's free tier now runs GPT-6 Luna. For a broader take on the apps, read ChatGPT vs Claude vs Gemini.

How to run your own comparison

1. Pick 20 to 50 real tasks from your work, with a clear pass or fail rule for each.
2. Run Argon at low, medium and high thinking and GPT-6 at matching reasoning efforts; Argon ignores temperature and OpenAI's changelog says Astra doesn't accept a custom one, so control quality with effort.
3. Log tokens and cost per task, not just per call; thinking tokens are billed as output on both.
4. Re-run anything that fails once, to separate flakiness from real errors.
5. Choose the cheapest setting that passes your bar, then re-test when prices change on 1 December.

For more on structuring that, see the best LLM guide and our Gemini 4 Argon explainer.