Gemini 4 Argon vs Claude Opus 5.5 vs GPT-6.1 Sol for SEO and Google Ads: What Each Job Costs and Which to Use

Gemini 4 vs Claude Opus 5.5 vs GPT-6.1 Sol: which is best for SEO and Ads?

Gemini 4 Argon went on general sale on 13 October at $2 per million input tokens and $10 per million output tokens. A footnote in Google's announcement says that from 1 December the price becomes $4 and $20, which is Claude Opus 5.5's price to the cent. The launch price is GPT-6.1 Sol's, also to the cent. None of the three flagships wins on price, so search and Ads teams should pick by job: Claude Haiku 5.5 or GPT-6 Luna for labeling search terms, Opus 5.5 in Claude Code for work in a codebase, Argon for very long single prompts, and GPT-6.1 Sol as the cheapest flagship once Google's offer ends.

That convergence is the useful fact of the month. When per-token prices match, the differences that decide a bill sit in the documentation: surcharges on long prompts, whether thinking can be switched off, how each lab says its model copes with long inputs, and which of your tools it already lives in. This piece checks each against the labs' own pages, prices three jobs that search and paid media teams run every week, and takes a position on which model to use for what. Every benchmark quoted is attributed to the lab that published it, on settings that lab chose.

When was Gemini 4 released, and what else shipped?

These are the dates each lab gives on its own pages.

The release trackers circulating this month were mostly right, with three corrections. Google first announced Gemini 4 Argon on 30 September, for cyber-defense partners in its Fairwind Program; the Gemini API changelog records general availability, as gemini-4-argon, on 13 October. The mid-October GPT-6 Pro update is not a new model: OpenAI's release notes for 16 October describe faster responses, better tool use and parallel agents for the Pro option in its consumer app, which the 7 October entry on the same page says runs on GPT-6 Astra. And DeepSeek V4.1 Flash shipped on 10 September, not in October. Mistral Large 4 checks out as a public preview from 6 October, at $1.36 and $4.18 per million tokens with weights promised by the end of the month. As a preview it stays out of the tables below, as does last month's DeepSeek model.

How much do Gemini 4 Argon, Claude Opus 5.5 and GPT-6.1 Sol cost?

Prices are in dollars per million tokens at standard rates, from Google's Gemini API pricing, Anthropic's pricing page and OpenAI's model pages for GPT-6.1 Sol and GPT-6 Luna.

Read across the rows and the pairs appear. Through November, Argon and GPT-6.1 Sol are priced identically, down to the 10-cent cached input rate. From 1 December, Argon and Opus 5.5 are, down to the 20-cent cache read. Haiku 5.5 and Luna share a list price of 10 cents in and 50 cents out. Gemini 3.8 Flash is the outlier at seven and a half times their price, and Google's pricing page shows it doubling on 1 January.

The surcharge column is where the pairs come apart. A GPT-6.1 Sol or Luna request with more than 272,000 input tokens is billed at twice the input rate and 1.5 times the output rate, for the whole request. A Haiku 5.5 prompt over 100,000 tokens, cache reads included, pays five times the base rates. Opus 5.5 and Argon charge one rate across the window, and Argon's model page allows up to 1 million output tokens with what Google calls long decode, against 128,000 per standard request on the Claude and OpenAI models.

Two cautions apply to every per-token comparison. Tokenizers differ: Anthropic says its current tokenizer produces about 30% more tokens for the same text than its older models did, and Google and OpenAI publish no comparable figure, so a gap of a fifth between vendors on paper can vanish on real text. And thinking is billed as output, which Google's price table and Anthropic's context window documentation both state. How each model thinks, and where you can use it:

What does an SEO or Google Ads job cost on each model?

Here are three jobs for Harbour & Finch, a sample client we use for worked examples (it is not a real business), with the token counts stated so you can swap in your own.

1. Classify 10,000 search terms from a quarter's search terms report into eight labels, such as competitor, job seeker and wrong product. That is 100 calls of 100 terms, each carrying a 2,000-token instruction block and about 1,500 tokens of terms, and returning 1,000 tokens of labels plus an assumed 1,000 of reasoning. Total: 350,000 tokens in, 200,000 out.
2. Write one content brief for a category page from a Search Console export: 5,000 query and page rows at about 30 tokens a row, plus 10,000 tokens of page text and instructions, in one call. Out comes a 4,000-token brief plus an assumed 8,000 of reasoning. Total: 160,000 in, 12,000 out.
3. Audit a 500-page crawl, one call per page: about 3,000 tokens of cleaned text and structure in, 400 tokens of findings and an assumed 1,000 of reasoning out. Total: 1.5 million in, 700,000 out.

These are list prices without caching; all three labs' Batch APIs halve them. Bracketed figures are the prices after Google's scheduled increases, on 1 December for Argon and 1 January for Flash. The reasoning allowances are assumptions applied equally to every model, and since output costs five times input on all six, real thinking moves these totals more than anything else.

The content brief is the one job where the two small models part company. A 160,000-token prompt crosses Haiku 5.5's 100,000-token line, while Luna's sits at 272,000. Filter the export in code first, keeping the 800 or so rows that touch the page's topic, and the prompt falls to about 34,000 tokens: Haiku returns to its base rate, and every model gets an easier read. Double the export instead, to 10,000 rows and about 300,000 tokens, and GPT-6.1 Sol's long-context rate takes the brief to $1.38, six cents short of Opus 5.5, while Argon at its launch price charges 72 cents.

The crawl, at 1.5 million tokens, fits in none of these windows as a single prompt, and OpenAI's models accept at most 922,000 input tokens. Paged one call per page, it costs $10 on Sol, $20 on Opus 5.5 and 50 cents on either small model. Even the dearest cell in the table is less than an hour of anyone's time. The expensive outcome is a wrong label on a search term that converts, or a wrong fix on a live page, which is why the rest of this piece is about fit.

Which model is cheapest for classifying search terms?

Sorting search terms into labels is the highest-volume model job most Ads teams run. Haiku 5.5 and Luna cost the same, and they are the only two of the six that can skip reasoning entirely: Anthropic's notes on Haiku 5.5 say thinking can be turned off at high effort or below, and Luna's model page lists an effort level of none. Gemini 3.8 Flash's model page says its minimal setting returns an error, and Opus 5.5 cannot switch thinking off at all. For a label that needs no reasoning, thinking tokens are output you pay for and discard.

Anthropic's Haiku 5.5 announcement does compare the two small models: 72.4% against 48.9% for Luna on OSWorld 2.1, and 39.2% against 16.4% on Terminal-Bench 4.0. Those are Anthropic's runs on tests of agents operating computers, and its table has no test of plain classification. The comparison that counts is yours: label 300 search terms by hand, run both models on them, and keep the one that disagrees with you less on the terms that spent money.

Two details decide which is easier to wire up. OpenAI's Decisions API, which returns one answer from a set you define, became generally available on Luna today, according to its API changelog; our OpenAI DevDay 2026 recap covers what OpenAI said about it at launch. On Anthropic's side, Opus 5.5 rejects forced tool use with an error, so a labeling script that forces a "label" tool call breaks the day someone points it at the big model. Anthropic's documented fix is strict tool use or structured outputs.

Keep the arithmetic in code either way. A script should sum spend and decide which terms have enough clicks to judge, then test each proposed negative against the full term list before anyone adds it, as our guide to finding wasted Google Ads spend without blocking the searches that pay sets out. AIO Copilot's free Ads leak check runs that collateral test on a search terms export without touching the account. Leave Gemini 3.8 Flash out of this job: it costs seven and a half times as much and doubles in January, and Google's pricing page marks data on its free tier as used to improve Google's products, which rules that tier out for a client's search terms.

Which model handles long documents best?

All six take roughly a million tokens, about 555,000 words on Anthropic's tokenizer according to its models overview. The labs differ in how frankly they say whether the model reads all of it well.

Google is the only one whose launch materials compare long-context results across labs. The Gemini 4 Argon model card reports GraphWalks, which asks a model to trace paths through a large graph written into the prompt. Up to 128,000 tokens, Argon scores 99.7%, GPT-6 Astra 98.7% and Opus 5.5 90.6%. From 256,000 tokens to 1 million, they score 84.2%, 71.8% and 66.8%. Google chose and ran the test, against Astra rather than Sol, and every model gets worse at length. Its long-context guide adds that retrieving many facts "can vary to a wide degree," that questions work better at the end of the prompt, and that Argon's window is shared by input, thinking and output.

Anthropic's Opus 5.5 announcement gives no long-context score. Its documentation says that "as token count grows, accuracy and recall degrade, a phenomenon known as context rot," and that curating the window matters as much as its size. OpenAI's long-document claim for GPT-6.1 Sol concerns PDFs: on GDP.pdf, which asks professional questions about complex PDFs, its announcement says Sol "scores higher than Opus 5.5 with fallbacks at less than half the cost per task." Again, the vendor's test on the vendor's settings.

For SEO the lesson holds whichever model you pick. A Search Console export is a table, and code reduces a table better than a model reads one: group the queries, keep the page's rows, compute clicks and average position, then hand over the result. When a job truly needs one very long prompt, such as a client's whole category of pages read against a rival's, try Argon first. Our guide on how to use Claude Code for SEO shows the code-first version of several of these jobs.

Which is best for agentic work and tool use?

Agentic work, where a model calls tools and picks its next step from the results, is where the labs' tables disagree most. On AutomationBench, a test of multi-step business workflows, Google's model card puts Argon at 51.3% and Opus 5.5 at 42.5%, while Anthropic's own announcement puts Opus 5.5 at 40.0% on the same test. OpenAI says GPT-6.1 Sol scores 2.2 points above Opus 5.5 at medium effort, at roughly a third of the cost.

The result worth noticing is a concession. On Terminal-Bench 4.0, which tests agents in a real terminal, Google's own card shows Opus 5.5 ahead of Argon, 66.4% to 57.4%. Anthropic ran that score at xhigh effort, above Claude Code's medium default, but a rival printing the gap on its launch card is a fair signal for terminal-heavy work such as template, schema and redirect changes. Google does claim the top DeepSWE v1.1 score, 77.9% against 74.2% for Opus 5.5, and OpenAI says Sol matches Astra on that test at about a fifth of the cost.

For search work the tool lists matter more than the scores. Argon supports function calling, code execution, structured outputs, URL context, computer use in preview, and grounding with Google Search, the one tool here that queries Google itself. Google bills grounding at $14 per 1,000 search queries after 5,000 free a month and warns that one request can trigger several searches. Sol's model page lists web search, file search, a code interpreter, a hosted shell, computer use, MCP and tool search. Opus 5.5 supports Anthropic's server and client tools, minus forced tool choice.

Where can you use Gemini 4, Claude Opus 5.5 and GPT-6.1 Sol?

Gemini 4 Argon in Google's own tools

Google's launch post puts Argon in the Gemini app and AI Mode in Search for Google AI Pro and Ultra subscribers, in AI Studio and Android Studio on the paid Gemini API tier, and in Gemini for business. Antigravity was listed for Ultra subscribers at launch; its pricing page now says Pro and Ultra. Argon is not on the API's free tier, and none of Google's 13 October posts mentions Google Ads or Search Console, so it reaches Ads and SEO work through the API or through what you paste into the app.

Claude in Claude Code

Opus 5.5 is Claude Code's default on Pro, Max, Team, Enterprise and API accounts, at medium effort, according to the model configuration docs. Haiku 5.5 needs Claude Code 2.1.293 or later and is what the haiku alias selects on Anthropic's API. On Amazon Bedrock and Google Cloud, the same page shows haiku still pointing to Haiku 4.5 and sonnet to Sonnet 4.5, so an agency running Claude Code through a cloud account should check what each alias selects. All four current Claude models are on Anthropic's API, Bedrock, Google Cloud and Microsoft Foundry.

GPT in Codex

GPT-6.1 Sol is in Codex and the Work tab of OpenAI's consumer app on Plus, Pro, Business, Enterprise and Edu, and OpenAI's Codex model guide recommends it "for complex coding and agentic workflows." The 7 October notes say the Chat tab runs GPT-6 Sol on paid plans and Luna on Free and Go, while the Pro option runs Astra. Our comparison of Claude Code, Codex, Cursor and Antigravity covers the tools' own plans and limits.

Which model should you use for SEO and Google Ads work?

Our position: run three models, each on the work it is priced and built for, and let code produce every number.

Labeling search terms and pages: Haiku 5.5 or GPT-6 Luna

Same price, and both can skip reasoning. Test them on 300 rows you labeled yourself; if you already build on OpenAI, the Decisions API tips it toward Luna. A flagship on this job costs 20 to 40 times as much for answers drawn from a list of eight.

Site-wide changes in a codebase: Opus 5.5 in Claude Code

Title templates, structured data, redirect maps and internal link rewrites are terminal-heavy jobs, and Claude Code is built for them with Opus 5.5 as its default, the model a rival's own card puts ahead in a terminal. Our piece on Claude Opus 5.5 for SEO and Google Ads covers the effort setting, which now decides the bill on that model.

Auditing hundreds of pages: a cheap first pass, then a flagship

Run two passes. A small model reads every page and flags the ones that need a fix, and a flagship writes the fixes. If 50 of Harbour & Finch's 500 pages are flagged, that is 50 cents on Haiku 5.5 plus $2 on Opus 5.5, $2.50 in all, against $10 to $20 for a flagship reading everything.

One very long prompt: Gemini 4 Argon

Argon charges the same rate at 900,000 tokens as at 9,000 and has the best long-context figures any lab has published. Until 30 November it also costs what Sol does. Thinking and output share its window, and the price doubles on 1 December.

Briefs, drafts and diagnosis: GPT-6.1 Sol from December

One brief costs under a dollar on any flagship, so run your template through all three once, $1.76 at list price for the job above, and keep the output your editors change least. For steady drafting and diagnosis after that, Sol is the default we would set from December, at half the per-token price of the other two flagships for prompts under 272,000 tokens. Teams standardized on Anthropic have an equivalent in Sonnet 5.5, which Anthropic prices at the same $2 and $10 with no long-context surcharge.

Whatever the mix, the numbers inside the work should not come from any of these models. Spend, conversion rates, clicks lost since a change, the pages a fix touched: code computes them, and the model explains them. That is how AIO Copilot runs SEO, AI search and Ads work, with the math shown under every figure and nothing changed in a client account until a person approves it; get started and we email pricing and the setup steps.

Prices will move again, and Google has already printed the date of its next change. Build the pipeline so a model can be swapped in an afternoon, and 1 December becomes a one-line config change.

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