Gemini 4 Argon vs GPT-6 Astra vs Claude Opus 5.5: Cost Per Real Task

Gemini 4 Argon looks five times cheaper than GPT-6 Astra on output tokens. On Artificial Analysis's own test run, a task costs $1.99 on Argon against $3.26 on Astra. That is a gap of 1.6 times. The difference between those two ratios is the whole story of this guide.

Argon thinks longer than Astra, so you pay for more tokens per answer. Claude Opus 5.5 thinks longest of the three and scores highest. The price per token tells you very little. The price per finished task tells you what the bill will be. That is the number to plan around.

This is a cost guide. It is not a ranking of which model is smartest, because the three vendors report different benchmarks, run at different reasoning settings, and publish most of their scores themselves, which makes a single leaderboard position a weak basis for a purchase of any size. Every figure below carries a date and a source. Check them. Prices are as of 14 October 2026. The most important number is Argon's introductory input price. It ends on 30 November 2026.

If you are choosing a model for a team, read the cost table first, then the section on who should pick which. If you are still deciding between model families, the broader guide to choosing the right LLM covers the other half of the question.

What each model costs per million tokens

Here is the standard rate card, read from each vendor's own pricing page on 14 October 2026.

Google's Gemini API pricing page states both Argon rates and the date the price changes. It also says Argon supports implicit caching only. You cannot reserve a cache explicitly, so the cached rate applies when Google decides a prompt prefix repeats.

Anthropic's pricier tiers, Claude Fable 5 and Claude Mythos 5, sit at the same $10 and $50 as Astra on Anthropic's pricing page. From 1 December, Argon costs the same as Opus 5.5 per token. Both are 2.5 times cheaper than Astra on input and on output. OpenAI and Anthropic publish their rates in their own docs, and the Astra and Opus 5.5 figures above match those pages.

The discounted tiers matter for batch work. All three vendors halve the price for work that can wait.

Argon also lists a Flex tier at the same rate as Batch and a Priority tier at $3.60 input and $18 output until 30 November, doubling afterwards. Priority is the premium tier. Google lists no free tier for Argon, so every call in these tables is billed.

For scale, the cheaper siblings sit far below all of this. GPT-6.1 Sol lists at $2 and $10 on OpenAI's page, the same as Argon's introductory rate. The cheapest LLM API guide ranks the budget end.

What a real task costs

Per-token prices ignore how many tokens a model burns before it answers. Artificial Analysis measures that. Its Intelligence Index runs every model through the same set of tasks and records the tokens used and the dollars spent.

I read all three pages from Artificial Analysis on 14 October 2026. Argon and Astra tie at 53 on the index. A dead heat. Opus 5.5 leads at 58.

Look at how the sticker gap shrinks. It collapses. Argon's output price is one fifth of Astra's. Its cost per task is 61% of Astra's. Argon used 110 million tokens on the test and Astra used 60 million, nearly half as many. Cheaper tokens. Nearly twice as many of them.

Opus 5.5 is the other extreme. Its per-token price equals Argon's post-December price, yet it cost the most per task because it generated 260 million tokens. It also scored five points higher, so you get something for the spend.

One caveat on the table. The three runs used different reasoning settings, labelled high, max and max with a default fallback. A like-for-like run at one setting would move the numbers. Treat the table as a fair guide to scale, not a lab-grade comparison.

What a thousand tasks cost

Scale the per-task figures up and the gap becomes a budget line. Take a team that runs a thousand tasks of the Artificial Analysis kind in a month.

The December row is my estimate, doubled from Google's published change. The others come straight from Artificial Analysis. Even the cheapest row is real money, and the spread between the cheapest and dearest is three to one.

The price cliff on 1 December

Google's introductory price ends on 30 November 2026. From 1 December every Argon rate doubles. If a task uses the same tokens, its cost doubles too.

Double Artificial Analysis's Argon figure. You get about $3.98 per task. That is my arithmetic, not a published number, and it assumes token use stays flat. It would put Argon above Astra and still below Opus 5.5. In plain terms, the cheapest model of the three in October becomes the middle one in December, and the cheapest becomes Astra, the model with the highest sticker price. That reversal is exactly why per-token tables mislead.

So the ranking by cost per task flips on 1 December. Before it, Argon is the cheapest of the three. After it, Astra is.

Here is the practical consequence. A budget built on Argon's October price breaks in December. If you are signing off a project that will run past 30 November, model it at $4 and $20.

Google's own launch post states the later price in a footnote. Nothing here is hidden. But the headline number, the one most people repeat, is the cheap one, and a team that skims the launch coverage will budget from it and be surprised when the first December invoice arrives with a bill twice the size it expected.

What caching saves

Caching changes the picture for any app that resends the same long prompt. Take a 50,000-token system prompt reused on every call.

These are my sums. They use the published input and cached rates, per call, input only. Cached reads cost a twentieth of fresh ones on Argon and Opus 5.5, and a tenth on Astra.

The catch sits with Argon. Its caching is implicit, so Google decides when a prefix counts as repeated. Anthropic lets you write and hold a cache on purpose. If your savings plan assumes a high hit rate, test it before you trust it.

Where Argon wins

Output length is the clearest advantage. Google says it raised the output limit to 1 million tokens, up from 64,000. Astra and Opus 5.5 both list 128,000 on their hokai records. For a job that must produce a very long result in one pass, such as a full code migration, only Argon fits.

Google reports 77.9% on DeepSWE v1.1, which tests long software engineering jobs. The hokai record for Astra lists 74.1% on the same benchmark. Google ran its own test, so treat the gap as a claim to check on your code, not a settled lead.

Google also reports 91.7% on LVBench, a long video understanding test. No comparable score exists on the other two records, so there is nothing to set it against.

On price, Argon is the cheapest of the three until 30 November, on both per-token and per-task measures. If you have a one-off project that finishes by then, the discount is real money.

For tooling, Google points developers to Google AI Studio to try it. Argon comes from Google and the lab behind Google DeepMind, and the consumer Gemini app is the same maker's front door. The family's smaller models, Gemini 3.8 Flash and the budget Gemini 3.5 Flash-Lite, are the ones to check if cost matters more than depth. Flash-Lite lists at $0.30 input and $2.50 output on Google's paid tier.

Where Astra wins

Astra's edge is token economy. It used 60 million tokens on the same test where Argon used 110 million and Opus 5.5 used 260 million. Fewer tokens mean fewer surprises on a bill. That counts.

It also has the most published benchmark detail of the three. Its record lists 96% on GPQA Diamond, 72.6% on OSWorld v2 for computer use and 57.2% on Humanity's Last Exam. Those come from the vendor and an aggregator. Verify any that decide a purchase.

Astra's cost is the sticker. At $10 input and $50 output it is the most expensive of the three per token by a wide margin, and the premium only pays off where its brevity offsets it. After 1 December that is true on cost per task against Argon.

Astra also carries a premium tier above its standard rate. Fast mode doubles the price, and OpenAI lists a regional-processing uplift of 10% on models released after March 2026. Neither applies unless you ask for it.

If you pay for ChatGPT seats rather than API calls, the model choice is bundled into a plan instead. The ChatGPT plans guide covers which tier gets what, and the Agents API pricing guide covers metered agent costs.

Where Opus 5.5 wins

Opus 5.5 scores highest on the Artificial Analysis index at 58. Its record lists 89.9% on SWE-bench Pro, a coding benchmark, taken from Anthropic's system card. That number is not comparable to the DeepSWE scores above, because they are different benchmarks.

It is the fastest of the three where speed was measured. Artificial Analysis records 95 tokens per second for Opus 5.5 and 60 for Astra. It lists no speed for Argon, so I cannot rank it on speed at all.

The price you pay is verbosity. Artificial Analysis ranks it 112th of 114 models for output tokens on the index. If your prompt asks for short answers, you may see far fewer tokens than the benchmark shows, and the cost per task would drop with them.

Opus 5.5 is the one to pick when the quality of the answer is worth more to your team than the size of the monthly bill, which is true for legal drafting, hard debugging and anything where a wrong answer costs a person's afternoon. Anthropic's own front ends are Claude and Claude Code. The Claude model chooser explains when a smaller Claude is enough. Compared with the earlier Claude Opus 5, the 5.5 release is a fifth cheaper per token.

Can you buy Argon today

Access is the open question. Google's launch post on 30 September 2026 said Argon was rolling out to vetted cyber defenders first, with wider access to follow "as soon as possible". It gave no date.

By 14 October the Gemini API pricing page listed Argon on its paid tier, with no free tier, and a banner saying the model was available. I could not confirm from the public page whether every paid account can call it yet. Check your own console before you plan around it. Google's post also frames Argon as an upgrade on its security-focused Gemini 3.8 Flash Cyber, so the first buyers are security teams.

Astra and Opus 5.5 are listed as generally available on their hokai records, so they carry no such caveat. If you are comparing a model you can use today with one you may get next week, that is a real cost. Google's older preview, Gemini 3.5 Pro, shows the risk: hokai's record, checked at the end of September, found no price for it on Google's page.

Who should pick which

The choice turns on two things. How long the job runs. How it is billed. This table summarises it.

Published averages cannot replace your own numbers. The next section shows how to get them. Smart Match can narrow the field by job and budget first, and the model leaderboard lists price and benchmark scores side by side.

The model recommender helps when you have a hard constraint, such as a maximum price or a minimum context window. For coding specifically, Cursor lets you switch models inside one editor, which makes a real side-by-side test cheap. The local coding models guide covers the no-API option.

How to measure your own cost per task

You can reproduce the idea of the benchmark with a day of work. Here is the method.

1. Pick ten real tasks from your backlog, not toy prompts. Include one that fails often.
2. Run each task on all three models with the same instructions and the same tool access.
3. Log the input tokens, output tokens and any cached tokens from each response.
4. Multiply by the rates in the first table, using the 1 December rate for Argon.
5. Divide by the number of tasks that produced a correct result. Failed runs still cost money.
6. Repeat the run on a different day, because reasoning length varies from run to run.

Step 5 gets skipped. Often. A model that costs half as much but needs two attempts has no price advantage. The cost per correct result is the number to compare.

What it costs to switch

Switching between these three is cheap at the API level. At the prompt level it is not. All three accept text and images and return text, with a 1 million token context window each. The endpoints differ, but a thin wrapper hides that.

The hidden cost is behaviour, and it is the one nobody budgets for, because a prompt that was tuned for months against one model's habits of length, formatting and refusal will quietly produce different output on another, and the first sign is usually a complaint from a user rather than an error in a log. A model that thinks for 110 million tokens behaves differently from one that thinks for 60 million. Prompts tuned to one will drift on another. Plan a week of re-testing. Not an afternoon.

The cache works differently too. Argon offers implicit caching only, while Anthropic's docs describe explicit cache writes you can pay for and control. If your savings depend on a reliable cache hit rate, that difference matters more than the headline discount.

For the wider field, including Grok and Gemini 3.8, the four-way comparison sets these models beside their rivals.

How each handles your data

The three vendors differ on data. Google's pricing page marks the paid Argon tier as not used to improve its products. Google's launch post says the model is rolling out first to vetted cyber defenders, and hokai's record notes that zero data retention is published only for Gemini Enterprise access.

OpenAI states that API prompts sent to Astra are not used for training by default. Eligible enterprise customers can request zero data retention. Anthropic's standard API retention applies to Opus 5.5, with zero retention available to eligible enterprise customers.

Retention terms change, and they differ by contract, by plan and by region, so before you send anything sensitive to any of the three you should read the current terms yourself rather than rely on a summary of them, including this one. Do not skip it.

The strongest case against this guide

The best objection is that cost per task on a benchmark is not cost per task on your work. A coding agent that loops on a failing test burns tokens no index run predicts. A short-answer chatbot burns almost none. Both fall outside Artificial Analysis's mix.

The point stands. It is the reason the table works as a scale guide and not as a quote. But the direction holds up. A model with cheaper tokens that thinks longer will lose some of its price advantage on almost any reasoning-heavy job. Measure on your own prompts, and the benchmark becomes a starting estimate.

What would change this

Four things could change the picture. Any single one could reopen a decision you thought was closed.

• Google extends the introductory price past 30 November. The cost ranking would then stay as it is today.
• Artificial Analysis publishes a speed for Argon, which would settle the one gap in the table.
• OpenAI or Anthropic changes a price, which would reorder the table.
• A run at matched reasoning settings shows a different token count for any of the three.

Check the vendor pricing pages again before committing a large budget. This guide was written on 14 October 2026, and the 1 December change is already scheduled.

Questions people ask

The short answers are in the FAQ below. The decision rule is simpler. If the work ends by 30 November, price Argon at the introductory rate. If it runs longer, price it at the later rate and compare it with Astra and Opus 5.5 again.

Frequently asked questions

Is Gemini 4 Argon cheaper than GPT-6 Astra?

Until 30 November 2026, yes. Argon lists at $2 input and $10 output per million tokens against Astra at $10 and $50. Per task on Artificial Analysis it is $1.99 against $3.26. After 1 December Argon's rates double, which would put it near Astra's cost per task.

What happens to Argon's price after November?

Google's pricing page lists $4 input and $20 output per million tokens from 1 December 2026, up from $2 and $10. Cached input moves from $0.10 to $0.20. Budget any project that runs past 30 November at the later rate.

Which of the three costs the most per task?

Claude Opus 5.5, at $5.98 per task on Artificial Analysis, because it used 260 million tokens on the test. It also scored highest, 58 against 53 for both Argon and Astra. Its per-token price equals Argon's post-December price.

Why does Argon cost more per task than its token price suggests?

It thinks longer. Artificial Analysis recorded 110 million tokens for Argon against 60 million for Astra on the same test. Cheaper tokens used in larger numbers shrink the gap from five times on output price to about 1.6 times per task.

Can I trust these cost-per-task figures for my own work?

Use them for scale, not as a quote. The three runs used different reasoning settings, and your prompts will differ from the benchmark mix. Run ten real tasks on each model, count the tokens, and price those.

Covered in this guide

• Gemini 4 Argon: Google DeepMind's Gemini 4 frontier model, announced 30 Sep 2026: text, image and video input, 1M-token output, limited to Fairwind Program partners for now.
• Claude Opus 5.5: Anthropic's September 2026 flagship LLM with a 1 million token context window, built for long-running agentic coding and computer use.
• GPT-6 Astra: OpenAI's flagship model, launched September 2026 as the first ever rated at the Preparedness Framework's Critical cybersecurity level.
• Anthropic: Anthropic is a Public Benefit Corporation started by 7 ex-OpenAI researchers. It builds the Claude model family and counts Amazon and Google among its largest backers.
• ChatGPT: ChatGPT is OpenAI's AI assistant, with 1.2 billion weekly users by OpenAI's count, the GPT-6 model family and plans that run from a free tier to a premium Pro tier.
• Claude: Claude is Anthropic's AI assistant, with a free tier, paid personal plans, and Team and Enterprise seats. Opus access starts at the first paid plan.
• Claude Code: Anthropic's agent for the terminal, IDEs, desktop, web and mobile that edits files, runs tests and opens pull requests. There is no free plan.
• Claude Fable 5: The first generally available Mythos-class Claude model, with 1M context and 95.0% SWE-bench Verified. Released June 2026 by Anthropic.
• Claude Mythos 5: Anthropic's Mythos-class model — the same underlying model as Claude Fable 5, with its cybersecurity, biology/chemistry, and distillation safeguards lifted. Available through Project Glasswing to vetted infrastructure-security partners.
• Claude Opus 5: Anthropic's July 2026 Opus with a 1M token context and xhigh reasoning effort, still active but superseded by Claude Opus 5.5 on September 22, 2026.
• Cursor: Cursor is an AI code editor built on VS Code, widely adopted across large enterprise engineering teams, with Agent Mode, Tab completion, and Cloud Agents.
• Gemini: Google's multimodal AI assistant and model family, sold as a free app, tiered subscriptions and a pay-per-token API
• Gemini 3.5 Flash-Lite: Gemini 3.5 Flash-Lite is Google DeepMind's July 2026 model built for high-volume AI subagents: 1M-token context, fastest in the Gemini 3.5 family.
• Gemini 3.5 Pro: Google named Gemini 3.5 Pro at its May 2026 developer conference, but the model has not been released. Google has published no context window, price or benchmarks for it.
• Gemini 3.8 Flash: Google DeepMind shipped Gemini 3.8 Flash on September 2, 2026, a multimodal Gemini 3 model tuned for long-horizon coding and agentic enterprise work.
• Gemini 3.8 Flash Cyber: Gemini 3.8 Flash Cyber (Google DeepMind, Sept 2026) reports 86.2% on CyberGym and is limited to vetted Fairwind Program defenders; Gemini 4 Argon now sits above it.
• Google: Google (Alphabet, NASDAQ: GOOGL), founded 1998, serves 8B+ monthly Search users with Gemini 3.5, 190,820 employees, and $402.84B FY2025 revenue.
• Google AI Studio: Free web-based IDE for building, testing, and deploying generative AI applications with Google's Gemini models
• Google DeepMind: Alphabet's AI research lab, created from DeepMind and Google Brain. It makes the Gemini, Gemma and AlphaFold models, and its newest frontier model is still limited to partners.
• GPT-6 Luna: GPT-6 Luna (Sept 2026) is OpenAI's lowest-cost GPT-6 model, tuned for high-volume chat, extraction and classification with six adjustable reasoning-effort levels.
• GPT-6.1 Sol: GPT-6.1 Sol is OpenAI's September 2026 mid-tier reasoning model for coding, computer use and document work, built to sit just below GPT-6 Astra.
• OpenAI: OpenAI builds ChatGPT, the GPT-6 family (Astra, Sol, Luna), Codex and the OpenAI API. It reports 1.2B weekly ChatGPT users and an $852B valuation after its March 2026 round.

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