Gemini 4 is about as good as Claude Opus 5.5 and GPT-6 Astra. Google’s new model, officially called Gemini 4 Argon, ranks first on knowledge work, long-context reasoning and long video understanding. It trails both competitors on agentic coding, which is arguably the most important benchmark right now.
Gemini 4 also isn’t publicly available yet. My honest take is that Google has caught up to OpenAI and Anthropic, and I don’t think that’s enough to put Google back 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 Vals AI and early-access demos.
Is Gemini 4 Better Than Claude Opus 5.5 and GPT-6 Astra?
Gemini 4 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. Google hasn’t said when the increase will happen.
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Available Today? |
| Gemini 4 Argon (introductory) | $2 | $10 | Early access only |
| Gemini 4 Argon (after increase) | $4 | $20 | Early access 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?
Google hasn’t announced a public release date for Gemini 4. Right now, Argon is only available to a group of trusted cyber defenders through Google’s Fairwind Program and to Google’s internal teams. Paid API customers and Google AI Ultra subscribers are next in line, according to Google’s Argon announcement.
As of October 13, 2026, Google’s public Gemini API still tops out at Gemini 3.8 Flash. It sounds like Google just started post-training Argon, so the model may get a little better before the official release. We’ll have to get access and test it ourselves before we can say for sure.
Why Is Argon Only Available to Security Teams?
Google says Argon can find, validate and patch software vulnerabilities on its own, so it’s starting 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, Google hasn’t done enough 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. Gemini 4 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 Gemini 4 is officially released.
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’m just not super excited about Gemini 4 yet.
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 for knowledge work like finance and legal analysis, plus very long documents, codebases or video, once you can get access
- 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 or Sol for anything you need to deploy today, since Argon isn’t in the public API 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.