Three frontier models arrived within weeks of each other: OpenAI's GPT-6 family, Anthropic's Claude Opus 5.5 and, in October, Google's Gemini 4 Argon. All three are strong enough that the useful question is no longer "which is smartest" but which one fits your work and your budget. The short version: Argon wins on price and long inputs, Opus 5.5 leads independent intelligence testing and terminal-style agent work, and GPT-6 gives the broadest ecosystem through ChatGPT.
Key Takeaways
- Gemini 4 Argon has the lowest launch price ($2 / $10 per million tokens) and the strongest long-context scores, but that price doubles on December 1.
- Claude Opus 5.5 leads the independent Artificial Analysis intelligence index and the Terminal-Bench 4.0 coding test, and costs the most per task at its highest effort setting.
- GPT-6 is spread across several models (Astra, Sol, Luna), and its ChatGPT apps remain the most complete everyday package.
- Coding results are split: each model wins at least one major benchmark. Test with your own tasks.
- Many people do best with two tools rather than one.
How we compared: this guide draws on official documentation, vendor benchmark tables, independent evaluations (including Artificial Analysis) and published pricing as of mid-October 2026. We flag which numbers come from the vendors themselves, because launch-week benchmark tables are written by the companies that want to win them. We did not run our own benchmark suite, and no single score below should decide a purchase for you.
In This Article
- Quick verdict
- Meet the three models
- Specs side by side
- Coding and software engineering
- Long documents and long outputs
- Reasoning and general intelligence
- Agents and automation
- Cost: what you actually pay
- Consumer plans
- Who should use what
- How to test them in 15 minutes
- FAQ
Quick Verdict: The 30-Second Answer
- Best value for developers right now: Gemini 4 Argon, at half the per-token price of Opus 5.5 until November 30.
- Best for terminal-heavy and agentic coding workflows: Claude Opus 5.5, which leads the Terminal-Bench 4.0 row in Google's own comparison.
- Best for very long inputs and outputs: Gemini 4 Argon, with a 1M-token reply limit.
- Best all-round consumer ecosystem: ChatGPT, powered by GPT-6 Sol on paid plans.
Meet the Three Models
Gemini 4 Argon (Google)
Released in October 2026 for developers and enterprises through the Gemini API and Google AI Studio, with Google AI Ultra subscribers able to reach it in AI Studio and Google Antigravity. Google positions it for complex coding, security work and long-running agent tasks. Its standout change is output length: the maximum reply grew from 64,000 tokens to as much as 1 million. The context window is 1,048,576 tokens, shared between input, thinking and output.
GPT-6 (OpenAI)
"GPT-6" is a family, not one model. GPT-6 Astra, launched on September 3, is the top-end model used in most benchmark tables and the engine behind ChatGPT's Pro thinking level. In everyday ChatGPT, paid plans use GPT-6 Sol and Free and Go use the lighter GPT-6 Luna, as covered in our GPT-6 rollout guide. On October 8, OpenAI added an Ultrafast mode for GPT-6.1 Sol, running at up to 8 times the speed in the API and ChatGPT Work. This article uses Astra for benchmark and API comparisons.
Claude Opus 5.5 (Anthropic)
Released on September 22, 2026, Claude's Opus 5.5 has a 1M-token context window and accepts text and images. Its maximum single response is 128K tokens (300K in the Batch API beta). Anthropic's current lineup also includes Sonnet 5.5, Haiku 5.5 and Fable 5.1; Opus 5.5 is the model most comparisons pit against Argon and Astra.
Specs Side by Side
| Gemini 4 Argon | GPT-6 Astra | Claude Opus 5.5 | |
|---|---|---|---|
| Released | October 2026 | September 3, 2026 | September 22, 2026 |
| Context window | 1,048,576 tokens | About 1.05M tokens | 1M tokens |
| Max single output | Up to 1M (65,536 default) | Not reported | 128K (300K Batch beta) |
| API input / output | $2 / $10 per 1M tokens until Nov 30, then $4 / $20 | $10 / $50 per 1M tokens (reported) | $4 / $20 per 1M tokens |
| Cached input | $0.10 per 1M (95% off) | Not reported | $0.20 per 1M (cache reads) |
| Where to use it | Gemini API, AI Studio, Antigravity | ChatGPT Pro thinking level, API | Claude apps, Claude API, cloud platforms |
Coding and Software Engineering
This is where headlines mislead most, because the three models trade wins depending on the test. These are results from Google's published comparison table, as relayed by several outlets, so treat them as vendor-reported:
| Benchmark | Gemini 4 Argon | GPT-6 Astra | Claude Opus 5.5 | Leader |
|---|---|---|---|---|
| DeepSWE v1.1 (long-horizon software tasks) | 77.9% | 74.1% | 74.2% | Argon |
| FrontierSWE v2 | 55.0% | 65.5% | 62.3% | Astra |
| Terminal-Bench 4.0 | 57.4% | 58.2% | 66.4% | Opus 5.5 |
Argon looks best on long, multi-step repository tasks, Astra on the hardest engineering problems in FrontierSWE, and Opus 5.5 on work that happens in a terminal. A lead on one row should not be assumed on the others. In one third-party DevOps test of nine calls, Argon at default settings cost $0.045, Opus 5.5 cost $0.254 and GPT-6.1 Sol cost $0.062, which shows how much price and effort settings change the real bill.
Coding verdict: no clean winner. Pick Opus 5.5 for terminal and agent workflows, Argon for long repository tasks at low cost, and Astra for the hardest engineering problems. For editor-based tools, see our guide to the best AI coding assistants.
Long Documents and Long Outputs
All three accept roughly a million tokens, but what they do with them differs. On GraphWalks, a long-context benchmark, Argon scores 99.7% up to 128K tokens against 90.6% for Opus 5.5, and 84.2% against 66.8% between 256K and 1M tokens. That 17-point gap at the far end is Argon's biggest margin in the published table. Output length widens the difference: Argon can write a single reply up to 1M tokens long, while Opus 5.5 tops out at 128K per response.
Long-context verdict: Gemini 4 Argon, both for retrieval across very large inputs and for very long single outputs.
Reasoning and General Intelligence
On Artificial Analysis's independent Intelligence Index, Argon and GPT-6 Astra land close together and Claude Opus 5.5 scores a few points above both. On the Vals Index, which Google includes in its table, the order is Argon at 68.9%, Opus 5.5 at 67.0% and Astra at 63.1%. The two indexes disagree on who leads, which is a useful reminder that the margins are small and depend on what each index measures.
Reasoning verdict: effectively a three-way tie at the top. Opus 5.5 has the edge on the independent index; Argon on Vals. No published benchmark here settles writing style or nuance, so judge those with your own prompts.
Agents and Automation
Google pitches Argon for long-running agent tasks and reports 51.3% on Zapier's AutomationBench and 91.7% on LVBench, a long-video understanding test. OpenAI built Astra as a computer-use agent, and Opus 5.5's lead on Terminal-Bench 4.0 points to strength in command-line agent work. We found no single head-to-head agent benchmark covering all three on the same tasks, so the honest summary is that the right choice depends on the environment your agent works in: browser and desktop, terminal, or large document sets.
Cost: What You Actually Pay
Per-token prices are only half the story, because models use different numbers of tokens to finish the same task. Artificial Analysis measured the cost of its full evaluation per average task:
| Model | Cost per average task | Note |
|---|---|---|
| Gemini 4 Argon | $1.99 now, about $3.98 after Nov 30 | Uses roughly 62K output tokens per task, more than twice its rivals |
| GPT-6 Astra | $3.26 | Higher per-token price, fewer tokens used |
| Claude Opus 5.5 | $5.98 at maximum effort | Lower effort settings cost less |
The key date is November 30. Until then Argon costs half as much per token as Opus 5.5. From December 1 it moves to the same $4 / $20 rate, and because it generates more tokens per task, its advantage over Astra shrinks or reverses. If you are choosing for a production workload, model the cost on your own prompts rather than the launch price.
Consumer Plans: What Do You Get for $20?
| Service | Free | Entry | Main plan | Power tiers |
|---|---|---|---|---|
| ChatGPT | Yes (GPT-6 Luna) | Go, $8 | Plus, $20 | Pro, $100 / $200 / $500 |
| Claude | Yes | None | Pro, $20 ($17/mo billed yearly) | Max, $100 / $200 |
| Gemini | Yes | Google AI Plus (under $10) | Google AI Pro, $19.99 | Ultra, $99.99 / $199.99 |
The mainstream plans sit within a dollar of each other, so price alone will not decide it. ChatGPT's $500 Pro tier is the one that includes the Ultrafast mode. Argon is reaching Google's consumer plans through the top Ultra tiers first, so check which model your plan actually uses on each vendor's pricing page before subscribing, since model access changes often.
Who Should Use What
- Developer running high-volume API jobs: Gemini 4 Argon, while the launch price lasts, then re-check the numbers on your own workload.
- Team building terminal or agent-style coding workflows: Claude Opus 5.5.
- Working with huge documents or needing very long outputs: Gemini 4 Argon.
- Want one polished app for everyday work: ChatGPT, with GPT-6 Sol on Plus and above.
- Hardest engineering problems or computer-use agents: test GPT-6 Astra alongside Opus 5.5.
- Already inside Google Workspace: the Gemini plan that comes with your Google account is the natural place to start.
How to Test Them in 15 Minutes
- Pick three real tasks from your own week, such as a bug fix, a long document summary and a first draft.
- Run the exact same prompt in ChatGPT, Claude and Gemini.
- Note how many follow-up edits each answer needs, not just how good the first reply looks.
- If you use the API, log tokens and cost per task, since that is where the models differ most.
- Repeat after a week, because rollouts and settings change these results quickly.
Things to Check Before You Commit
- Vendor benchmarks are marketing-adjacent. Independent numbers, where available, are more reliable than a launch table.
- The introductory Argon price ends on November 30.
- Effort settings change cost and quality. A "max effort" result is not what you get at default.
- Rollouts are staged. A model may reach APIs before consumer apps.
Frequently Asked Questions
Is Gemini 4 Argon better than GPT-6?
On Google's published table, Argon leads GPT-6 Astra on several rows, including DeepSWE and Vals Index, and trails it on others such as FrontierSWE. Independent testing puts them close together on intelligence, so it depends on your task.
Which is the cheapest to use?
For API use, Argon is cheapest per token until November 30. After that, Astra can be cheaper per task despite a higher token price, because Argon uses more output tokens. For subscriptions, all three mainstream plans cost about $20 a month.
Which has the biggest context window?
All three are around 1 million tokens. Argon stands out for output, with a 1M-token reply limit against 128K for Opus 5.5.
Which is best for coding?
There is no single winner. Argon leads DeepSWE v1.1, Astra leads FrontierSWE v2 and Opus 5.5 leads Terminal-Bench 4.0.
When does Argon's price go up?
On December 1, 2026, from $2 / $10 to $4 / $20 per million tokens for input and output.
Can I use more than one?
Yes, and many people do. A common setup is one general assistant for daily work plus a second model for coding or long documents.
Our Final Take
The gap between these models is smaller than the marketing suggests, and it changes with every release. Argon is the best deal on paper today, Opus 5.5 is the strongest on independent testing and terminal work, and GPT-6 has the most complete consumer ecosystem. Match the model to the job, re-check prices after November 30, and revisit the choice every few months.
More assistants and productivity apps are listed in Vinggle's AI Productivity Tools directory. If you are building one, submit it to Vinggle.