Last checked 9 October 2026
A frontier AI model is a lab’s current flagship, with a published price and a real access limit. On 9 October 2026, the models worth comparing are OpenAI’s GPT-6 family, Anthropic’s Claude 5.5 family, Google’s Gemini 4 Argon, and DeepSeek’s V4.1 Flash and V4 Pro.
Gemini 4 Argon is announced, and the first access is limited to Google’s Fairwind program. In a 10-prompt TeamAI test on 9 October 2026, GPT-6.1 Sol passed all four checks on 8 prompts and Claude Opus 5.5 on 7. The test measured output quality and instruction following. It did not measure TeamAI credit usage or cost. A team can keep the models it can reach in one workspace.
This is Part 1. It covers the current list, list prices, and which job to test first. Part 2 is for benchmark method and provider detail. Coding-specific routing is in best AI models for coding and agentic workflows.
What a frontier model is
A frontier model is the current top model a lab will sell or gate for serious work. Last year’s flagship can still be useful. It is no longer the model a buyer should treat as current.
Four questions sort the list:
- Is the model on sale, or only announced?
- What is the standard input and output price per million tokens?
- Does a long prompt, a cache, or a fast mode change that price?
- Which job should get the first test: hard work, everyday work, or high volume?
Examples, checked against provider pages on 9 October 2026, are GPT-6 Astra, GPT-6.1 Sol, GPT-6 Luna, Claude Opus 5.5, Claude Sonnet 5.5, Claude Haiku 5.5, Gemini 4 Argon, DeepSeek V4.1 Flash, and DeepSeek V4 Pro.
Current list prices
The prices in this section are public provider API rates. They show how each lab prices direct API access. Cache, long context, batch, and fast mode can change the provider’s own rate. Confirm the figure on the provider’s price page on the day you read it.
Frontier models and list prices, 9 October 2026
Search, filter by lab, show only the models in the TeamAI picker, or click a heading to sort. Price sorting uses the input rate.
| Context, where the provider published it | Access on 9 Oct 2026 | ||||
|---|---|---|---|---|---|
| GPT-6 Astra | OpenAI | $10 input, $50 output | 1.05M context, 128K max output | API and ChatGPT paid plans, with rollout limits | Not listed |
| GPT-6.1 Sol | OpenAI | $2 input, $10 output | 1.05M context, 128K max output | API | Listed, tested |
| GPT-6 Luna | OpenAI | $0.10 input, $0.50 output | 1.05M context, 128K max output | API | Listed |
| Claude Opus 5.5 | Anthropic | $4 input, $20 output | See Anthropic’s model page | Claude and the Claude Platform from 22 Sep 2026 | Listed, tested |
| Claude Sonnet 5.5 | Anthropic | $2 input, $10 output | See Anthropic’s model page | Claude and the Claude Platform from 28 Sep 2026 | Listed |
| Claude Haiku 5.5 | Anthropic | $0.10 input and $0.50 output up to 100K prompt tokens; $0.50 and $2.50 above that | See Anthropic’s model page | Claude and the Claude Platform from 7 Oct 2026 | Not listed |
| Gemini 4 Argon | Introductory $2 input, $10 output; later $4 and $20 | See Google’s announcement | First release is limited to Fairwind cyber defenders | Not listed | |
DeepSeek V4.1 Flash (deepseek-flash) | DeepSeek | Peak cache-miss input $0.30, peak output $1.20 | See DeepSeek’s pricing page | API | Not listed |
DeepSeek V4 Pro (deepseek-v4-pro) | DeepSeek | Peak cache-miss input $1.32, peak output $3.96 | See DeepSeek’s pricing page | API, continued after 14 Sep 2026 | Not listed |
No rows match. Clear the search or press Reset.
Sources: provider price pages and announcements, checked 9 October 2026. The TeamAI picker column is the model menu of one TeamAI workspace read the same day, so confirm your own menu before you plan around a model. DeepSeek rates shown are peak, cache-miss.
Price per 1 million tokens, side by side
Choose input or output. Log scale keeps the cheap models visible next to the flagships.
Standard rates from the table above. Argon shows its introductory price, DeepSeek its peak cache-miss price, and Haiku 5.5 its rate up to 100K prompt tokens.
Three provider rules change the rate after the table.
| Provider | Rule | Effect |
|---|---|---|
| OpenAI | A request goes past 272K input tokens on GPT-6 Astra, GPT-6.1 Sol, or GPT-6 Luna | 2x input and 1.5x output for the full request |
| Anthropic | Haiku 5.5 prompt goes past 100K tokens. Opus 5.5 runs in fast mode | Haiku moves from $0.10 / $0.50 to $0.50 / $2.50. Opus fast mode is $8 / $40 |
| DeepSeek | Request runs off-peak, or hits the cache | Off-peak is half of peak. A V4.1 Flash cache hit at peak is $0.006 per million input tokens |
Cost calculator
Price one request and one month of work at the 9 October 2026 list rates. Enter tokens, not words. The rule changes above are applied for you.
The same job on every model (standard options, cheapest first)
List prices only. This does not include TeamAI credit usage. Gemini 4 Argon access is limited to Fairwind, so its price is not an option for most teams yet.
Which model to test first
There is no single best frontier model in this set. The miss is sending every task to the flagship, or sending a hard task to a narrow model and shipping the result.
Quick finder: what should I test first?
Pick your job. Each model shows whether it was in the TeamAI picker on 9 October 2026.
Which model to test first, by job
Filter by job or search for a model name.
| Job | Start the test with | Why this is the first test | Leave it when |
|---|---|---|---|
| Hard reasoning, research, or a long agent run | GPT-6 Astra or Claude Opus 5.5 | OpenAI describes Astra as its most capable model. Anthropic positions Opus 5.5 for long-running work. Astra was not in the TeamAI picker on 9 Oct 2026. | The task is routine |
| Everyday coding, documents, and scoped professional work | GPT-6.1 Sol or Claude Sonnet 5.5 | Both are the current mid-tier models for frequent professional work. Sol was in the picker and was the model tested. | Quality drops on the hard cases you actually care about |
| High-volume classification, extraction, or short answers | GPT-6 Luna, Claude Haiku 5.5, or DeepSeek V4.1 Flash | These are the small models each lab offers for narrow, high-volume tasks. Luna was listed. Haiku 5.5 and DeepSeek V4 were not. | The error rate on a checked sample is too high |
| Visual or long-horizon work where Argon is approved | Gemini 4 Argon | Google announced it on 1 Oct 2026 and limited first access to Fairwind. It was not in the picker. | Your team is not in the Fairwind rollout. Use a model you can call this week |
| Work that has to stay on an open-model API | DeepSeek V4.1 Flash, then V4 Pro | Flash is the current small model in DeepSeek’s V4 family. V4 Pro remains on the API. Neither was in the picker on 9 Oct 2026. | A closed model is allowed and beats it on your own sample |
No rows match. Clear the search or press Reset.
What a 10-prompt test showed
On 9 October 2026, we ran 10 prompts through Claude Opus 5.5 and GPT-6.1 Sol inside one TeamAI workspace. Each prompt went to each model once, in a new chat, with the same wording. GPT-6 Astra was not in the model menu, so Opus 5.5 was the flagship. The test measured output quality and instruction following. It did not measure credit usage or cost.
The model menu in that workspace, read the same day:
- Listed and tested: Claude Opus 5.5, GPT-6.1 Sol
- Listed, and not part of this test: GPT-6 Luna, Claude Sonnet 5.5
- Not listed: GPT-6 Astra, Claude Haiku 5.5, Gemini 4 Argon, DeepSeek V4.1 Flash, DeepSeek V4 Pro
Haiku 4.5, DeepSeek V3, and DeepSeek R1 were on the menu. They are earlier models, so they were left out of this comparison.
The prompts were written for the test, not taken from client work: 3 support replies, 3 short specs, 2 code reviews, and 2 long-document edits. Each answer was checked against four yes/no rules: the format asked for, no invented facts, every part present, and sendable after a light edit. A Claude model applied the checklist and quoted the defect for each failure. It marked three Opus 5.5 failures and two Sol failures.
| Result | Claude Opus 5.5 | GPT-6.1 Sol |
|---|---|---|
| Prompts that passed all four checks | 7 of 10 | 8 of 10 |
| Missed the requested format | 1 (code review) | 2 (spec, long document) |
| Added a fact the prompt did not supply | 1 (long document) | 0 |
| Needed a rewrite before sending | 1 (support reply) | 0 |
Both models passed all four checks on 5 of the 10 prompts.
Passes by prompt type
Each square is one prompt. Filled means it passed all four checks. Select a prompt type to see what failed.
Totals: Opus 5.5 passed 7 of 10 and Sol passed 8 of 10. Counts by prompt type come from the failure table above.
No answer failed because it reached a wrong conclusion. Both models found the four-tries bug in the retry-loop code review. Opus 5.5 lost points by adding material: prose after a table it was told to return alone, word counts in a memo that banned new numbers, and customer coaching inside an email meant for an admin. Sol lost points on layout, running five required lines and four labeled lines together as paragraphs.
Treat this as a small test, not a ranking. Ten prompts, one run each, and one reviewer cannot settle which model is better. Sol also ran workspace skill steps on 3 prompts and Opus did not, which may have helped it. On scoped work, Sol passed all four checks on 8 prompts and Opus 5.5 on 7. The flagship’s failures were extra output rather than wrong answers.
Self-test: can your sample tell two models apart?
Enter how many prompts you ran and how many passed all your checks. The bars show a 95 percent range for each pass rate (Wilson interval). If the ranges overlap, the run cannot separate the models.
Use 10 to 20 items your team actually sends, write the pass rules down first, and count failures by type. A statistical range does not replace reading the failures.
Run the same kind of test on your own work before you lock a default. Use 10 to 20 items your team actually sends, write the pass rules down first, and count failures by type, not by impression.
For a closer look at coding and agent tasks, use the coding model guide. For how to choose among labs once the shortlist exists, use how to choose the right LLM.
What changed after March 2026
The March 2026 version of this article stopped at GPT-5.2, Claude 4.6, Gemini 3.1 Pro in preview, and DeepSeek V3 and R1. Those names are the wrong comparison for a buyer reading this in October.
The lineup, March vs October 2026
Switch the view to see how each lab’s current models changed.
| Lab | Models in this view | Status |
|---|
What replaced them, from the provider pages linked below:
- OpenAI’s current API guidance starts with GPT-6 Astra, then GPT-6.1 Sol, then GPT-6 Luna.
- Anthropic shipped Claude Opus 5.5 on 22 September, Sonnet 5.5 on 28 September, and Haiku 5.5 on 7 October.
- Google announced Gemini 4 Argon on 1 October and limited the first access.
- DeepSeek’s current API names are
deepseek-flashfor V4.1 Flash anddeepseek-v4-profor V4 Pro. Older V3 and R1 rows should not be treated as the current price card.
A model from March can still be in a product. It should be labeled as a prior generation, with its own price check, rather than left in a table that says it is current.
Provider pages checked on 9 October 2026:
- OpenAI models
- OpenAI pricing
- GPT-6 Astra
- Claude Opus 5.5
- Claude Sonnet 5.5
- Claude Haiku 5.5
- Gemini 4 Argon
- DeepSeek pricing
Keep the choice in one workspace
Use one workspace when your shortlist spans more than one lab. The 9 October 2026 test needed a Claude model and an OpenAI model side by side, with the same prompt, in the same place.
Example: marketing keeps a ChatGPT plan, engineering keeps Claude, and someone else pastes DeepSeek output into a doc. Each release adds another login, and another place where the good prompt lives on one person’s account. In the test workspace, every new chat opened on Claude Sonnet 5, and switching to Opus 5.5 or Sol took one menu change.
TeamAI is the collaborative workspace for that problem. A team can use more than one model, keep shared agents and workflows, ground answers in company files, and let an admin control access. ChatGPT Teams stays inside one model family. The comparison is on TeamAI vs ChatGPT Teams.
Multiple models is the page for routing a task across labs. Shared prompts, workflows, and knowledge stay with the team when someone changes roles. Slack, Jira, Google, and Guru are where that work already runs.
WebFX runs TeamAI internally with 500+ employees. That is the production proof.
If the buyer needs managed paid marketing, with a specialist signing off on execution, that product is Growth Loops. TeamAI is the workspace the team runs itself.
Questions teams ask
What are examples of frontier AI models?
GPT-6 Astra, GPT-6.1 Sol, GPT-6 Luna, Claude Opus 5.5, Claude Sonnet 5.5, Claude Haiku 5.5, Gemini 4 Argon, DeepSeek V4.1 Flash, and DeepSeek V4 Pro. Argon is on the list because Google announced it as a frontier model on 1 October 2026. It is not on the list as a model every team can call today.
What are the current frontier AI models?
The table above was checked on 9 October 2026. A March 2026 chart that still leads with GPT-5.2, Claude 4.6, Gemini 3.1 Pro, and DeepSeek V3 is out of date.
Which frontier model is best?
Match the model to the job. Test Astra or Opus 5.5 on the hard work. Test Sol or Sonnet 5.5 on everyday work. Test Luna, Haiku 5.5, or DeepSeek V4.1 Flash on high volume. Pick after a sample from your own work, not after a single benchmark column. In our 9 October 2026 test of 10 scoped prompts, GPT-6.1 Sol passed all four checks on 8 and Claude Opus 5.5 on 7.
How many frontier models are there?
There is no stable count. The March 2026 version of this article listed GPT-5.2, Claude 4.6, Gemini 3.1 Pro, and DeepSeek V3 and R1. By 9 October 2026 the current rows were GPT-6, Claude 5.5, Gemini 4 Argon, and DeepSeek V4. Count the models your team can access this month, then review the list when a provider changes the lineup or access.
Do we need a separate account for each lab?
Separate lab accounts are one way to reach these models. They also split prompts, files, and admin. A multi-model workspace keeps the task, the workflow, and access in one place. That is the job TeamAI is built for.