Is Gemini 4 Carbon the Best AI Model? Gemini 4 vs Opus 5.5 and GPT-6 Astra
Gemini 4 Carbon might be the best AI model in the world right now, at least on paper. Google announced it on October 28, 2026, only two weeks after Gemini 4 Argon, and it ranks first on the Artificial Analysis Intelligence Index at 59, just ahead of Claude Opus 5.5 at 58 and GPT-6 Astra at 57. But it’s complicated. Carbon isn’t publicly available yet, and its announcement isn’t evidence that Google has the smartest model, only the smartest model it’s willing to announce.
Argon, the first Gemini 4 model, is about as good as Claude Opus 5.5 and GPT-6 Astra. It ranks first on knowledge work, long-context reasoning and long video understanding, and it trails both competitors on agentic coding, which is arguably the most important benchmark right now. Argon caught Google up to OpenAI and Anthropic. Carbon is the one that might actually put Google in the lead.
At TJ Digital, we use AI in every workflow, and it lets us deliver about four times the work at the same rates. So when a new model drops, I want to know if it’s worth switching. This breakdown is based on Google’s published benchmarks, independent testing from Artificial Analysis and Vals AI, and early-access demos.
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Is Gemini 4 Carbon the Best AI Model?
On the benchmarks, probably. Carbon is a big step up from Argon. Google’s own comparison has Carbon posting the top score on 15 of the 17 benchmarks it published, and the Artificial Analysis Intelligence Index, probably the most respected combination of benchmarks, places it first across every model.
Every lab cherry-picks the benchmarks that make it look good, and there are a few noticeable omissions. DeepSWE, which Google included for Argon, isn’t in Carbon’s table, and Carbon still trails GPT-6 Astra on FrontierSWE 2 and Opus 5.5 on OSWorld computer use. But for the most part, Carbon is crushing the benchmarks.
Here’s how Carbon compares on a selection of the benchmarks in Google’s Carbon announcement:
| Benchmark | Gemini 4 Carbon | Gemini 4 Argon | GPT-6 Astra | Claude Opus 5.5 |
| Artificial Analysis Intelligence Index v5.0 | 59 | 50 | 57 | 58 |
| Vals Index v2.2 | 70.4% | 69.2% | 65.0% | 70.2% |
| Terminal-Bench 4.0 (Artificial Analysis) | 65.2% | 57.1% | 59.1% | 59.6% |
| FrontierSWE 2 | 61.2% | 55.0% | 65.5% | 62.3% |
| OSWorld-2.1 (offline subset) | 81.5% | 74.7% | 80.9% | 87.2% |
| GraphWalks, 256K to 1M tokens (F1 score) | 96.2% | 84.2% | 71.8% | 66.8% |
| SEC-bench Pro | 88.2% | 83.4% | 85.4% | Not reported |
Some of these numbers differ from the Argon launch table further down, because Google used newer benchmark versions and Artificial Analysis runs for Carbon’s comparison.
How Much Does Gemini 4 Carbon Cost?
Carbon launches at the same price per token as Argon: $2 per million input tokens and $10 per million output tokens at the introductory rate, then $4 and $20. At the standard $4 and $20 rate, Artificial Analysis measured Carbon at $7.76 per Intelligence Index task, in line with Argon’s $7.68. That puts Carbon’s cost per task somewhere between GPT-6.1 Sol ($1.27) and Claude Opus 5.5 ($10.04), with performance slightly better than both. Carbon also used about 29% fewer output tokens per task than Opus 5.5. We’ll have to actually use it ourselves to know for sure.
When Can You Use Gemini 4 Carbon?
Not yet. Just like Argon at launch, Google is holding Carbon back while it works on safeguards. It’s starting with a subset of cyber defenders and will gradually give more trusted partners access through its Fairwind Program, so they can secure their code before Carbon is released into the wild. Google says developers, businesses and consumers will get it as soon as possible, but it hasn’t given a date.
Does Carbon Mean Google Has the Smartest Model?
Not necessarily. Anthropic and OpenAI both have models they don’t feel are safe to release right now. OpenAI dropped its plan to release an upcoming model in September over safety concerns, and on October 27, Anthropic wrote that it had used an unreleased internal research model to find critical vulnerabilities. Google might be in a similar position, with even smarter internal models of its own. So the announcement of Carbon isn’t evidence that Google has the smartest model right now, only that it has the smartest model it’s willing to announce.
At this point, it feels like all the labs are in a game of who can get the closest without going over. Any of them could move faster and release smarter models. But ever since OpenAI’s models accidentally breached Hugging Face during a security test in July, there’s a big spotlight on all of them, and releasing a model that caused something worse would not bode well for anyone’s eventual stock price. Carbon is going to put a lot of pressure on OpenAI and Anthropic to release another model soon. Let’s just hope they don’t do it before it’s safe.
My advice if your business uses these models: don’t get locked into one model. Keep your data and skills stored somewhere that any model can connect to, like a shared AI knowledge base.
Is Gemini 4 Argon Better Than Claude Opus 5.5 and GPT-6 Astra?
Argon beats Opus 5.5 and GPT-6 Astra on some benchmarks and loses on others. Google’s own comparison shows Argon winning big on enterprise knowledge work, long context and video. Argon falls behind on the harder agentic coding benchmarks.
Here’s how the three models compare on the benchmarks Google published:
| Benchmark | What It Tests | Gemini 4 Argon | GPT-6 Astra | Claude Opus 5.5 |
| Vals Index | Finance, legal, tax and coding work | 68.9% | 63.1% | 67.0% |
| Finance Agent v2 | Multistep financial analysis | 65.4% | 53.5% | 58.6% |
| GraphWalks, 256K to 1M tokens (F1 score) | Reasoning over very long context | 84.2 | 71.8 | 66.8 |
| LVBench | Long video understanding | 91.7% | 87.5% | 83.7% |
| DeepSWE v1.1 | Long software engineering tasks | 77.9% | 74.1% | 74.2% |
| Vibe Code Bench | Interactive coding | 91.9% | 89.6% | 90.3% |
| FrontierSWE v2 | Harder long-horizon engineering | 55.0% | 65.5% | 62.3% |
| Terminal-Bench 4.0 | Agentic coding in a terminal | 57.4% | 58.2% | 66.4% |
| OSWorld 2.0 | General computer use | 69.2% | 72.6% | Not reported |
Google computed some of these results itself, including DeepSWE and LVBench. Independent testing from Vals AI backs up the knowledge work lead, though. Vals currently ranks Argon first on both the Vals Index and Finance Agent v2.
How Does Argon Do in Hands-On Tests?
Argon looks less impressive once people start building with it. On the WebDev Arena, Gemini 4 came in eighth, which isn’t great. Early-access demos where Gemini 4 builds apps and 3D environments next to the other frontier models look about the same or a little worse.
Why Is Argon Behind Opus 5.5 on Agentic Coding?
Opus 5.5 is stronger at long, terminal-based agent work, and Anthropic built it specifically for long-running agentic coding. Opus 5.5 scores 66.4% on Terminal-Bench 4.0, compared to 57.4% for Gemini 4.
Agentic coding means an AI works through a command line on long, multistep tasks. The model has to plan, run commands, recover from its own mistakes and keep track of everything it’s done. These skills are what let AI agents build software and finish real projects on their own, so I weigh agentic coding benchmarks heavily.
The gap is even bigger on FrontierSWE v2, which tests harder long-horizon engineering. Argon scores 55.0% on FrontierSWE v2, compared to 65.5% for Astra and 62.3% for Opus 5.5.
Gemini 4 still does some stuff better than any other model, including some kinds of coding. Argon ranks first on DeepSWE v1.1 at 77.9% and edges out both competitors on Vibe Code Bench. Google also says its agents have been used for large code migrations, including converting big C and C++ codebases toward Rust.
What Can a 1 Million Token Output Limit Do?
A 1 million token output limit lets Gemini 4 keep working through one long task without stopping, compressing its notes and starting over. That’s about eight times the 128K output cap on Opus 5.5 and GPT-6 Astra. Even Anthropic’s 300K beta limit for batch requests is less than a third of what Google announced.
The output limit is separate from the context window, which controls how much a model can read at once. Opus 5.5 and Astra can already read around 1 million tokens. Argon’s advantage is how much it can write and reason through in a single run.
That should make Gemini 4 really good at long-running tasks like large code migrations, long research jobs and agents that go through many rounds of testing and fixing. Still, a few things keep me from getting too excited about the 1 million token limit:
- Cost grows with output. One full million-token output costs $10 at the introductory price and $20 after the increase, before input and tool calls.
- Longer runs create new problems. Agents can get stuck in loops or drift away from the original goal the longer they run.
- Outside limits still apply. Sandbox lifetimes, API rate limits and timeouts can end a task long before the model runs out of tokens.
- Nobody outside Google has confirmed it. Vals AI’s early-access evaluation only exposed a maximum output of 262,144 tokens.
How Much Will Argon Cost Compared to GPT-6.1 Sol?
Argon costs the same as GPT-6.1 Sol right now, at $2 per million input tokens and $10 per million output tokens. That’s an introductory price, and Google says it will double to $4 and $20. The introductory price runs through November 30, 2026, and the higher price starts December 1, according to Google’s pricing page.
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Available Today? |
| Gemini 4 Argon (introductory, through Nov 30) | $2 | $10 | Yes, paid API tier |
| Gemini 4 Argon (from Dec 1) | $4 | $20 | Yes, paid API tier |
| Gemini 4 Carbon (introductory) | $2 | $10 | Cyber defenders only |
| Claude Opus 5.5 | $4 | $20 | Yes |
| GPT-6 Astra | $10 | $50 | Yes |
| GPT-6.1 Sol | $2 | $10 | Yes |
At the introductory price, Gemini 4 is 50% cheaper than Opus 5.5 and 80% cheaper than GPT-6 Astra. Once the price doubles, Gemini 4 costs the same as Opus 5.5 and twice as much as Sol. OpenAI hasn’t announced any plans to raise Sol’s price.
Is Argon Cheaper Per Task?
Argon can be cheaper per finished task, depending on the job. Vals AI found Argon averaged $15.68 per Vals Index test at the $4 and $20 rate, about half of Opus 5.5’s $32.14. On a long computer-use benchmark, the same model cost $193.78 per test.
I cover the bigger picture in my post on how AI model pricing works.
When Will Gemini 4 Be Released?
Argon is out. It started with a group of trusted cyber defenders through Google’s Fairwind Program, and on October 13, 2026, Google opened it to developers: paid Gemini API customers can build with it in Google AI Studio, and Google AI Ultra subscribers can try it in AI Studio and Google Antigravity. That’s the rollout laid out in Google’s Argon announcement.
Carbon is following the same path. For now it’s only going to a subset of cyber defenders, and Google hasn’t given a public release date.
Why Did Argon Start With Security Teams?
Google says Argon can find, validate and patch software vulnerabilities on its own, so it started with a staged rollout to security teams. No one cares about your model these days unless it’s too dangerous to release. So, of course, Gemini 4 is too dangerous to release.
Is Google Back in the AI Race?
Google is keeping up with OpenAI and Anthropic again, so in one sense, yes, Google is so back. In my view, Argon alone wasn’t enough for Google to take the lead.
On one hand, good for Google. Everyone thought Google was out of the race, and here they are at the frontier.
The problem is how this race works. Anthropic and OpenAI have only stayed in it by leapfrogging each other substantially with each release. Argon doesn’t feel like it’s leapfrogging anyone.
With how quickly Anthropic and OpenAI move, they’ll likely have a much more intelligent model out by the time Carbon is widely available.
Carbon changes the picture, at least on paper. Two weeks after Argon, Google has the top model on the Artificial Analysis Intelligence Index. It still isn’t publicly available, though, and Anthropic and OpenAI are both holding back models of their own, so whether Google is actually ahead depends on what the other labs release next.
Why Is Catching Up Easier Than Leapfrogging in AI?
Catching up is easier because the leaders have already shown what’s possible. As we’ve seen with open-source Chinese models, it’s much easier to catch up than it is to leapfrog.
You might say, “Sure, TJ, but look at how fast Google’s moving. They were so far behind, and look how quickly they caught up.” We saw the same thing with Gemini 2.5, when it looked like Google was moving faster than anyone.
I thought Google was about to win the whole thing back then. So I hope I’m wrong, and I hope Google keeps moving at lightning speed because I want more competition. I just wasn’t super excited about Argon.
Does Google Still Have the Best AI Search Model?
Yes. Google still has by far the best search model, and that’s going to stay very valuable for the foreseeable future.
Google AI Mode and AI Overviews put Google’s models directly into search results. For most of the businesses we work with, showing up in those answers matters more than which model wins a coding benchmark.
Which AI Model Should Your Business Use Right Now?
The best AI model depends on the job. Based on the published benchmarks, here’s where each model stands today:
- Gemini 4 Carbon for the strongest overall benchmark scores, once Google opens access
- Gemini 4 Argon for knowledge work like finance and legal analysis, plus very long documents, codebases or video, now on the paid Gemini API
- Claude Opus 5.5 for terminal-heavy agentic coding and long-running agents
- GPT-6 Astra for computer use and scientific workflows
- GPT-6.1 Sol for frontier-level work on a budget
- Opus 5.5, Astra, Sol or Argon for anything you need to deploy today, since Carbon isn’t publicly available yet
For a broader comparison across tasks, see my breakdown of what each model does best.
What Else Should You Know About Argon?
Is Argon Good for Legal Work?
Argon has a large lead on legal agent work. On the Harvey Legal Agent benchmark, Argon scored 19.6%, compared to 5.4% for GPT-6 Astra and 3.8% for Opus 5.5. The low scores across the board show how hard legal agent tasks still are for every model.
How Does Argon Do on Cybersecurity Benchmarks?
Argon is roughly tied with the leaders on vulnerability fixing. Argon scored 68% on CWE-bench v1, tied with GPT-6 Astra and one point ahead of Opus 5.5. Astra leads on reverse engineering, scoring 56.87% on Vals’ SRE Bench compared to 44.27% for Argon.
Is Argon Better Than GPT-6.1 Sol?
There isn’t enough head-to-head data to say yet, because Google’s comparison doesn’t include GPT-6.1 Sol. On Vals’ SRE Bench for reverse engineering, Sol scored 50.76% against 44.27% for Argon. Sol will also cost half as much once Argon’s introductory pricing ends.
How Good Is Argon at Computer Use?
GPT-6 Astra is better at general computer use. Argon scored 69.2% on OSWorld 2.0, compared to 72.6% for Astra. Vals’ CUA-bench placed Argon seventh out of eight models.
How Do You Get Your Business Recommended by Gemini and ChatGPT?
Every new AI model still has to decide which businesses to recommend, and it decides based on what it finds about you online. At TJ Digital, we help small to medium-sized businesses show up in ChatGPT, Google AI Mode and Gemini so they get more leads. Book a Two-Week Strategic Assessment and we’ll show you exactly where you stand in AI search today. You can also email me at tj@tjrobertson.com.
