Gemini 4 Argon vs GPT-6 Astra: Benchmarks, Cost, and Access
Gemini 4 Argon vs GPT-6 Astra: Benchmarks, Cost, and Access
Gemini 4 Argon went public on October 13, so for the first time you can pick between Google's best model and OpenAI's without joining a waitlist. That makes Gemini 4 Argon vs GPT-6 Astra a harder call than it looks, because on Artificial Analysis's independent index, the two are dead even.
I read both launch posts, both sets of API docs and plan pages, Google's full benchmark table, and Artificial Analysis's head-to-head results on October 14, plus ARC Prize's own test runs and one early hands-on test.
Here's which one to pick for coding, long documents, science, legal and finance work, and business automation, what a task costs before and after Google's intro price ends on November 30, and how to get access today.
Gemini 4 Argon vs GPT-6 Astra: the short verdict
They tie on independent testing. Artificial Analysis scores Gemini 4 Argon (high) and GPT-6 Astra (max) at 53 each on its Intelligence Index, which blends 10 evaluations into one number.
Argon is cheaper right now. It costs $2 per million input tokens and $10 per million output tokens through November 30, against $10 and $50 for Astra. On Artificial Analysis, that works out to $1.99 per index task to Astra's $3.26.
That lead shrinks in December. Argon's price doubles to $4 and $20 on December 1, and Artificial Analysis says its cost per task then rises to $3.98, about 1.2x Astra's.
Both are usable today. Argon is on the paid Gemini API and in the Gemini app for Google AI Pro and Ultra subscribers. Astra is on the OpenAI API and in ChatGPT, with regular chat access on individual plans starting at the $100 Pro plan.
Choose Gemini 4 Argon if you:
- Work with long documents: it scores 84.2% on the 256K-to-1M-token GraphWalks test in Google's table, while Astra scores 71.8%, and it doesn't charge more for long prompts.
- Do legal or finance work: it leads Harvey's legal benchmark (19.6% vs 5.4%) and Vals Finance Agent v2 (65.4% vs 53.5%) in Google's table.
- Run business automations: it tops AutomationBench in Google's table and on Artificial Analysis's version of the test.
- Want fewer made-up answers: Artificial Analysis measured a 15% hallucination rate for Argon against 51% for Astra.
- Already pay for Google AI Pro: at $19.99 a month, it's the cheapest individual plan that puts either flagship in regular chats.
Choose GPT-6 Astra if you:
- Take on long, project-scale coding: it beats Argon on FrontierSWE v2 by 10.5 points in Google's own table.
- Need computer use: it leads OSWorld-2.0 at 72.6% to Argon's 69.2%.
- Work in math and science research: it leads Terminal-Bench Science by 10.5 points and posts 97.6% on FrontierMath Tier 4.
- Want shorter answers and predictable bills: it used 27,000 tokens per Artificial Analysis task to Argon's 62,000, and its price isn't scheduled to change.
- Live in ChatGPT and Codex already: Plus includes Astra in ChatGPT Work and Codex, with limited usage.
My take: through November, Argon is the better default for most knowledge work, mainly because you get the same overall score for about 60% of the cost per task. After December 1, the price edge flips, and the choice comes down to which job you're doing.
Specs, pricing, and access side by side
The two models look similar on paper (both have about a million tokens of context), but their limits and pricing rules work differently.
How they compare at a glance:
Argon's prices and limits come from Google's Gemini API pricing page and Argon model page.
Astra's come from OpenAI's Astra model page. The cost-per-task figures come from Artificial Analysis's comparison page.
Two spec details to check before you build:
- Argon's "1M output" needs a special mode. A normal request returns up to 65,536 tokens. Getting near 1 million requires Google's long decode feature, which continues the response in chunks of up to 64K tokens, either in the background or through client-side continuation.
- Argon's million tokens is one shared budget. Input, thinking, and output all come out of the same 1,048,576-token window, so a 900K-token prompt leaves little room for the answer.
How to get Gemini 4 Argon today
Based on Google's GA announcement, there are four ways in:
- The Gemini API (paid tier only). Argon is "not available on the Free tier," per Google's pricing page, so you'll need billing turned on in Google AI Studio or Android Studio.
- The Gemini app and AI Mode in Search. Google AI Pro ($19.99 a month) and Ultra ($99.99 or $199.99 a month) subscribers get it. The $4.99 AI Plus plan doesn't.
- Google Antigravity. Google's agentic development platform has Argon for Ultra subscribers and enterprise customers.
- Gemini for business and Google Cloud. Enterprises can use it through Gemini for business and the Gemini Enterprise Agent Platform (formerly Vertex AI).
One catch for developers: Google says Argon isn't available on "the Google AI Pro plan" in AI Studio, so a Pro subscription gets you the chat app and AI Mode, and API use is billed separately.
How to get GPT-6 Astra today
Astra launched on September 3, and OpenAI splits access by surface:
- The OpenAI API. Call gpt-6-astra directly, with rate limits set by your usage tier (5,000 requests per minute on the entry Build tier).
- ChatGPT Plus ($20 a month). Astra comes with ChatGPT Work and Codex, with what OpenAI's help center calls "limited Astra usage," plus optional credits once you run out.
- ChatGPT Pro, Business, and Enterprise. Regular chats get "GPT-6 Pro, powered by GPT-6 Astra" on all three Pro plans ($100, $200, and $500), Business, and Enterprise (where admins switch it on).
- Cloud platforms. Astra runs in Microsoft Foundry and went GA on Amazon Bedrock on September 8.
So if you want a flagship model in a normal chat window on an individual plan, Google's is the $20 option and OpenAI's is the $100 one.
If you're picking a subscription, the GPT-6 explainer breaks down every ChatGPT plan, and our Claude vs ChatGPT comparison shows how ChatGPT's app stacks up against Claude's.
What a real bill looks like
Say your app sends 1,000 requests, each with 5,000 input tokens and 2,000 output tokens.
The bill at each price:
Astra's token prices are five times Argon's now and 2.5 times after December 1, but Argon writes much longer answers, so the real gap is smaller.
On Artificial Analysis's index, Argon generated about 62,000 tokens per task, more than twice Astra's 27,000. That's why its per-task cost ($1.99) lands at roughly 60% of Astra's ($3.26).
Once the intro price ends, Artificial Analysis expects Argon's cost per task to hit $3.98, about 1.2x Astra's. So the cheaper-looking model can end up as the pricier one per job (unless your prompts are long enough to trigger Astra's surcharge).
Three ways to shrink either bill:
- Cache repeated context: Argon caches automatically for prompts of 4,096 tokens or more at 95% off, while Astra cuts cached input to $1.00 from $10.
- Batch anything that can wait: both companies charge half price for Batch and Flex jobs.
- Turn the thinking down: Argon defaults to medium thinking, and Astra lets you choose from low to max effort, so test the lower settings before you pay for the top ones.
Gemini 4 Argon benchmarks vs GPT-6 Astra
In Google's published table, Gemini 4 Argon beats GPT-6 Astra on 14 of 19 results, ties on one, and trails on four. Counting Claude Opus 5.5 and Claude Fable 5.1 as well, Argon has the top score outright on 13 and ties for the top on one.
These are the numbers Google chose to publish, from its model card and Gemini page, so treat them as the vendor's best case.
The full table also includes Vibe Code Bench, PostTrainBench, a shorter GraphWalks run, Agents' Last Exam, and Chartography. Argon beats Astra on all five, though Opus 5.5 tops PostTrainBench.
Argon's biggest leads over Astra are on legal work (14.2 points), very long documents (12.4), and finance research (11.9). Astra's biggest leads are FrontierSWE v2 and Terminal-Bench Science, at 10.5 points each.
What independent testing says
Artificial Analysis scores Argon and Astra at 53 each, ranked eighth and seventh of 216 models, and level with Claude Fable 5.1. Claude Opus 5.5 leads the leaderboard at 58.
On the 10 tests inside that index, Argon wins four and Astra wins six:
- Argon wins: AutomationBench-AA (78% vs 68%), GDPval-AA (1624 vs 1573 Elo), SciCode (62% vs 56%), and Humanity's Last Exam (57% vs 55%).
- Astra wins: AA-Briefcase (1570 vs 1488 Elo), GDP.pdf (31% vs 22%), CritPt physics (32% vs 27%), Terminal-Bench 4.0 (59% vs 57%), AA-LCR long-context reasoning (81% vs 80%), and AA-Omniscience (43 vs 42).
On AA-Omniscience, Argon gets fewer questions right than Astra (50% vs 63%), but it makes things up far less often (15% vs 51%), which suggests it's more willing to say it doesn't know.
For research, contract review, or anything you'll forward to a client, I'd take the model that admits gaps over the one that guesses confidently, which favors Argon.
Why Google's chart and Artificial Analysis disagree
Google's table makes Argon look like a clear winner, while Artificial Analysis calls it a tie. Three things explain the split:
- Different test lists. Google's table opens with knowledge-work tests and includes the long-context runs where Argon posts its biggest leads, while Artificial Analysis averages its own 10, including document and physics tests where Astra leads.
- Different sources. Artificial Analysis runs every model through the same setup itself, while Google's table mixes its own runs with outside leaderboards and other labs' numbers.
- Different settings. Artificial Analysis tested "Argon (high)" against "Astra (max)," the top setting for each. Argon's API defaults to medium, so out-of-the-box results may land below that tie.
Both can be right, since they answer different questions. Taken together, Argon and Astra are roughly level overall, with big differences by task.
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Two benchmark numbers that need an asterisk
Astra's 99.9% on ARC-AGI-3 depends on the harness. ARC Prize measured 99.9% (at high effort) with its "Provider Adapter" harness, which keeps the model's reasoning state between steps. On ARC Prize's standard, provider-neutral harness, Astra scored 62.7% at max effort.
Astra's scores change depending on who reports them. OpenAI reports Astra at 59.3% on Agents' Last Exam, while Google's table, which takes Astra's score from the public leaderboard, lists it at 34.2%.
A smaller gap shows up on Terminal-Bench 4.0 (57.9% from OpenAI vs 58.2% in Google's table). Compare each source's numbers only against its own.
Which is better for what
1. Coding
It's a split, and neither model is the best pick for long terminal sessions. Argon leads DeepSWE v1.1 (77.9% vs 74.1%) and Vibe Code Bench (91.9% vs 89.6%), while Astra wins FrontierSWE v2 (65.5% vs 55.0%), which tests long, project-scale engineering tasks.
On Terminal-bench 4.0, the two are close (58.2% for Astra, 57.4% for Argon in Google's table), and Claude Opus 5.5 beats both at 66.4%.
One early hands-on test shows the same split. In a single-run kart-game test on dev.to, Argon finished in 220.6 seconds for $0.306, against 323.4 seconds and $0.704 for Astra. The tester still scored Astra's game slightly higher (10/10 vs 9.5).
The same tester found Argon can think for more than five minutes before its first token, which made their coding tool time out. Google itself recommends background execution or client timeouts of at least 15 minutes, because "hard prompts can take several minutes."
Pick: Argon for well-scoped fixes and app builds, Astra for long, project-scale engineering work. If you want a coding tool to run either one, our list of AI coding agents is a good place to start.
2. Long documents
Argon wins. It scores 84.2% on GraphWalks at 256K to 1M tokens, against 71.8% for Astra, and it charges one flat rate across its whole window. Astra doubles its input price once a prompt passes 272K tokens.
Argon also reads audio, video, and PDFs directly, and it leads LVBench, a long-video test, at 91.7% to 87.5%. Artificial Analysis's long-context reasoning test is a near tie (81% for Astra, 80% for Argon), so the edge is mostly price and file types.
Pick: Argon, especially for contract libraries, transcripts, and long reports.
3. Science and math
Astra has the edge on formal math and research workflows. It leads Terminal-Bench Science (68.1% vs 57.6%) and CritPt, a physics test (32% vs 27%), and OpenAI reports 97.6% on FrontierMath Tier 4, a test Google didn't publish for Argon.
Argon still wins LABBench 2, a biology research test (88.8% vs 85.4%), RiemannBench (76.0% vs 72.0%), and SciCode (62% vs 56%).
Pick: Astra for proofs and multi-step research tasks, Argon for lab-style biology questions and scientific coding.
4. Legal and finance work
Argon wins Google's tests by a wide margin. It scores 19.6% on Harvey's Legal Agent Benchmark, more than triple Astra's 5.4%, and leads Vals Finance Agent v2 by 11.9 points.
Artificial Analysis's numbers are closer. Argon wins GDPval-AA, a test of real work across many jobs, while Astra wins AA-Briefcase and GDP.pdf, two document-heavy work tests.
Pick: Argon, mostly because of its lower hallucination rate. Wrong-but-confident answers are the expensive kind in legal and finance work.
5. Business automation
Argon wins. It tops AutomationBench, Zapier's test of running business workflows across apps, at 51.3% to Astra's 41.4% in Google's table, and Artificial Analysis's version of the test agrees (78% vs 68%).
Astra still leads OSWorld-2.0 for computer use (72.6% vs 69.2%), so if your automation involves clicking through apps that have no API, Astra has a small edge.
Pick: Argon for API-driven workflows across tools, Astra for computer-use agents.
Where Claude Opus 5.5 and GPT-6.1 Sol fit
Two other models belong on your shortlist before you pick.
Claude Opus 5.5 is the top scorer on Artificial Analysis at 58, five points above both, and it leads Terminal-bench 4.0 at 66.4%.
It costs $4 per million input tokens and $20 per million output tokens, the same as Argon's December price, but $5.98 per Artificial Analysis task. Our Opus 5.5 vs Astra breakdown covers that matchup, and the Claude Opus 5.5 guide covers the model itself.
GPT-6.1 Sol is OpenAI's cheaper option at $2 and $10, the same as Argon's intro price. It scores 52 on Artificial Analysis, one point behind both flagships, for $0.72 per task. Our GPT-6.1 Sol guide has its benchmarks and access details.
If cost matters more than the last point of intelligence, GPT-6.1 Sol is the model to test before either flagship. For Google's cheaper tier, see Gemini 3.8 Flash, and for more on Argon itself, see our Gemini 4 Argon explainer.
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Pick by job, then recheck on December 1
Gemini 4 Argon vs GPT-6 Astra comes down to the job and the calendar. Until November 30, Argon gives you the same overall score for about 60% of the cost per task, so it's the one to try first for documents, legal and finance work, and automations.
Here's a quick way to decide on your own work:
- Pick 10 real tasks. Use work you do every week: a contract review, a long report, a refactor, a finance question.
- Run both at their default settings. Then run them again at the top setting and note the cost, time, and quality for each.
- Price it at both Argon rates. Check your results at $2 and $10 and again at $4 and $20, so December's bill doesn't surprise you.
- Route by result. Send each kind of task to the model that won it, and keep Astra for computer use and long, project-scale coding.
The same routing works in any tool that lets you pick the model for each task. If your team uses Lindy, an AI teammate that works in Slack, check which models are on its list and send each job to the one that won your tests.
Frequently asked questions about Gemini 4 Argon vs GPT-6 Astra
Is Gemini 4 Argon better than GPT-6 Astra?
Gemini 4 Argon is about as good as GPT-6 Astra overall, with both scoring 53 on Artificial Analysis's Intelligence Index. Argon wins legal, finance, long-document, and automation tests, and Astra wins computer use, long project-scale coding, and formal math.
Can I use Gemini 4 Argon yet?
Yes, Gemini 4 Argon has been generally available since October 13, 2026. You can use it on the paid Gemini API, in the Gemini app and AI Mode with Google AI Pro or Ultra, in Antigravity with Ultra, and through Gemini for business.
Is Gemini 4 Argon free?
No, Gemini 4 Argon isn't free. It isn't on the Gemini API's free tier or the $4.99 AI Plus plan. The cheapest way to use it in chat is Google AI Pro at $19.99 a month.
Which is cheaper, Gemini 4 Argon or GPT-6 Astra?
Gemini 4 Argon is cheaper until November 30, 2026, at $2 and $10 per million tokens against Astra's $10 and $50, and $1.99 per Artificial Analysis task against $3.26. From December 1, Argon costs $4 and $20, and its cost per task rises to about $3.98, roughly 1.2x Astra's.
Is GPT-6 Astra better than GPT-5.6 Sol?
Yes, GPT-6 Astra beats GPT-5.6 Sol, OpenAI's previous-generation model, on nearly every benchmark OpenAI published, including Terminal-Bench 4.0 (57.9% vs 37.3%) and AutomationBench (41.4% vs 18.1%). OpenAI says Astra also finishes OSWorld-2.0 tasks in about 47% less time.
Is Gemini 4 Argon better than Claude Opus 5.5?
Gemini 4 Argon beats Claude Opus 5.5 on 14 of the 18 results in Google's table where both are scored, including DeepSWE v1.1 (77.9% vs 74.2%). Opus 5.5 scores higher on Artificial Analysis's independent index (58 vs 53) and leads Terminal-bench 4.0 at 66.4%.
Which is better, Gemini or GPT?
Gemini and GPT are roughly tied at the top right now. Gemini 4 Argon and GPT-6 Astra both score 53 on Artificial Analysis. Gemini has lower token prices and puts its best model in a $19.99 plan, while GPT leads on computer use and long project-scale coding.
What is the #1 AI model right now?
Claude Opus 5.5 is the top model on Artificial Analysis's Intelligence Index at 58, as of October 14, 2026. Claude Sonnet 5.5 and Meta's Muse Wonder Full follow at 56, and Gemini 4 Argon, GPT-6 Astra, and Claude Fable 5.1 tie at 53.
