AI Models Compared, October 2026: GPT-6, Claude 5.5 and Gemini 4 Argon on Price, Specs and Benchmarks

Picking a frontier AI model in October 2026 means comparing three vendor families, eight named models, and a pile of leaderboards that disagree. This page puts the comparable facts in one place: what each model costs, how much it can read and write, where independent rankings put it, and which model leads which test. It will be updated as prices and rankings move.

How we compared. We did not run our own benchmark tests. Prices and specs come from the vendors' own pages where we could read them: OpenAI's GPT-6.1 Sol announcement, Anthropic's model overview and Google's Gemini API pricing page. Rankings come from a Fello AI roundup that reports Artificial Analysis and Arena results, because we could not open the leaderboards directly. Where a figure comes from a secondary source, the text says so.

Pricing and Specs Side by Side

Prices are US dollars per million tokens (MTok) at standard API rates, as listed by each vendor. Cached-input and batch discounts are left out of the table because they differ by vendor.

Asterisks mark figures from secondary sources. OpenAI's Sol announcement lists prices but not context windows, so the OpenAI context and output figures are from third-party aggregators and should be checked against OpenAI's documentation. Google's pricing page lists no context window either, and sources disagree on Argon's maximum output: launch coverage says up to 1 million tokens, while Vals AI lists 262,000. And Argon's price doubles on Dec. 1, 2026, according to Google's pricing page, so any comparison that uses $2 / $10 expires in five weeks. Aggregators also report higher OpenAI rates for prompts above roughly 272,000 input tokens; confirm that on OpenAI's own pricing page if you work with very long inputs.

The pattern is easy to see. Both OpenAI and Anthropic sell a top tier at $10 / $50, a mid tier at $2 / $10 and a small tier at $0.10 / $0.50 or above, with Anthropic adding a $4 / $20 step in between. Argon's introductory price matches the mid tier of both rivals. For more on Google's launch, see our Gemini 4 Argon pricing and rollout report.

Independent Rankings: The Expensive Model Is Not the Top Scorer

Artificial Analysis publishes a composite Intelligence Index. In the October snapshot reported by Fello AI (index version 4.3.2), the order of the models in this comparison was:

The same list also placed Meta's Muse Wonder Full (55.8) and Moonshot's Kimi K3.1 Pro (55.1) in the top five, ahead of the models above. We left them out of the table because they are outside the three vendor families compared here. Our open-weight comparison covers the open models.

Two things stand out. First, the two most expensive models, Fable 5.1 and Astra, do not top this index. Opus 5.5 leads it at less than half their price. Second, the whole range among these six models is under six points, and Fello notes that a point or so is within measurement noise. A composite index also averages many tasks, so a model can lose the average and still win the task you care about.

Who Leads Which Leaderboard

Different tests crown different models. As reported by Fello AI in October:

Fello's own page gave Argon's Arena text score inconsistently (about 1517 in one place and about 1525 in another), so treat that figure as approximate. Arena scores come from human preference votes, which measure what people prefer to read, not whether an answer is correct.

OpenAI's own materials make a different kind of claim: cost per task. On Terminal-Bench Science 0.1 at maximum effort, OpenAI reports GPT-6.1 Sol averaging $5.47 per task, against $23.21 for Opus 5.5 and $23.80 for Astra. Astra scored highest on that test at 68.1%. These are OpenAI-reported figures on one benchmark, so they show what Sol can do on a chosen task, not a general price ranking. They do illustrate a real point: list price per token and cost per finished task are different numbers, because models differ in how many tokens they spend reasoning.

Availability and Plans

• OpenAI: GPT-6.1 Sol is available in ChatGPT Plus, Pro, Business, Enterprise and Edu (including ChatGPT Work and Codex), and in the API as `gpt-6.1-sol`, according to OpenAI. OpenAI's models are also in private preview on Google Cloud, with the model names reported by Google; see our report on OpenAI models coming to Google Cloud.
• Anthropic: Claude API IDs are `claude-fable-5-1`, `claude-opus-5-5`, `claude-sonnet-5-5` and `claude-haiku-5-5`. Anthropic's documentation recommends Opus 5.5 for most workloads and Fable 5.1 for demanding reasoning and long-running agent work.
• Google: Argon (`gemini-4-argon`) is a paid-tier model and is not on the Gemini API free tier, per Google's pricing page. It launched in stages, starting with a cyber-defenders program; see our earlier report for the rollout details.

How to Choose

This section is analysis, not reporting.

• If price per token is your constraint, compare at the mid tier first. Sol, Sonnet 5.5 and Argon's introductory rate are all $2 / $10, and Opus 5.5 at $4 / $20 sits just above. On the index above, the mid-priced Anthropic models scored higher than the top-priced models from either vendor.
• If you buy on a single leaderboard, pick the one that resembles your work. Coding in a browser-based app points at WebDev results, where Opus 5.5 leads. Everyday chat and writing points at the Arena text and creative-writing boards, where Argon and Opus 5.5 lead. Math-style reasoning points at LiveBench reasoning, where Astra leads by a hair.
• If your prompts are long or your agents run many steps, measure tokens used per completed task, not list price. OpenAI's cost-per-task figures show the gap can be several-fold in either direction.
• If you are choosing for a company, run your own test. Pick 20 to 50 real tasks, run each candidate, and compare quality, cost per completed task and failure modes. Our AI power-user guide and guide to AI tools worth paying for cover how to set that up for individuals.
• Plan for change. Argon's price doubles on Dec. 1, and OpenAI is reported to have replaced GPT-6 Sol with GPT-6.1 Sol at the same price about a week after launch, so build so that the model name is a setting rather than hard-coded.

What We Cannot Conclude

We did not test these models. The benchmark figures are reported by third parties from leaderboards we could not open directly, and vendor benchmark tables are self-reported. Index and Arena rankings move week to week. Some details, including the OpenAI and Google context windows and a possible Anthropic model named Mythos that appears on some aggregators as limited access, are unconfirmed and are not in the tables. Prices shown were the vendors' published list prices when we checked; enterprise discounts, batch discounts and caching change what you actually pay. Nothing here is a recommendation to buy any particular product, which is one more reason to run your own tests.

Why It Matters

• List price no longer predicts quality. In this snapshot, a $4 / $20 model led the independent index while $10 / $50 models trailed it.
• There is no single best model. Four different models led the leaderboards above, depending on the test.
• Cost per task beats cost per token. The cheapest-looking model can cost more in practice, and the reverse.
• Pricing windows are short. Argon's introductory rate ends Nov. 30, and any comparison you saved last month may already be out of date.