Gemini 4 Argon vs Opus 5.5 vs GPT-6 AstraWhich One for Coding?

Three models, one checkout bug

September twenty second. Anthropic ships Claude Opus five point five. Eight days later, Google announces Gemini four Argon. And OpenAI's G P T six Astra is already out.

Three frontier models. All three want to write your code. The prices differ. Per million input tokens, Argon costs two dollars.

Opus costs four. Astra costs ten. So which one should fix your next bug? Let's use one real looking bug.

A customer clicks pay. The app creates two orders. You need a fix. You need proof it works.

And you need to understand the bill. Gemini four Argon. Claude Opus five point five. G P T six Astra.

Coding only. Published evidence first.

What the coding scores actually say

Then a practical way to choose. Start with two separate scoreboards. Google reports seventy seven point nine percent on Deep S W E, version one point one. OpenAI reports seventy four point one for Astra on that same named benchmark.

Google describes it as testing long running software engineering work. That is one useful signal for codebase work. Keep the version attached. Next board.

Terminal Bench four point zero. Anthropic reports sixty six point four percent for Opus. OpenAI reports fifty seven point nine for Astra. This benchmark covers terminal work.

Software engineering. System configuration. Data analysis. It is broader than fixing a checkout bug.

Those are vendor reported results. I have not run these three models through a shared test. I could not verify a matching three model set of S W E bench Verified or Pro scores from the official sources checked. Those cells stay blank.

An empty cell means missing evidence here. It does not mean zero ability.

Read the small print

And the two boards stay separate. Seventy seven on one test cannot outrank sixty six on another. The model is only one part of an agent run. The harness is the software around it.

Instructions. Tool access. The loop that lets it read, edit, and try again. Change that setup and you have changed the experiment.

Anthropic's Terminal Bench notes put Opus at extra high effort. Astra at high. Opus also has a reported standard error of two point six percentage points. These are estimates with uncertainty.

There is another wrinkle. Anthropic says production safeguards were enabled. Some interventions handed tasks to earlier Claude models. That qualification belongs beside the score.

OpenAI says its scores use the best result across effort settings. It also warns that its research or API setup can differ from the production chat experience. The reports have different dates too. Opus launched September twenty second.

Google's Argon announcement came September thirtieth. A later table may compare against an earlier snapshot. So read each score as evidence about a particular setup. Before making a buying decision, ask for matching versions, tools, effort, retries, and dates.

For our checkout task, write the acceptance rule first. One payment must create one order. Sending the same callback twice must still leave one order. A patch that only hides the second confirmation message has missed the example's actual requirement.

How much code fits

All three advertise roughly a million tokens of context. The exact limits differ. Argon has one million, forty eight thousand, five hundred and seventy six. Google calls this a unified input and output limit.

Opus lists one million. Astra lists one million, fifty thousand. Output has its own limits. Opus and Astra list one hundred and twenty eight thousand.

Argon's default synchronous output limit is sixty five thousand, five hundred and thirty six. Its long decode option can go much further. Within that shared context budget. So a million token headline does not mean you can paste a million tokens and then generate another million.

For our checkout example, I would start smaller. The failing test. The order creation code. The payment callback.

Logs that matter. Then let the agent inspect what connects them. Treat the context figure as capacity.

The bill for the same token bundle

Do not use it as proof that the model found the right function. Ask it to point to the code behind its diagnosis. Now price. Standard A P I rates.

U S dollars. Per million tokens. First, input. Second, output.

Argon. Two dollars and ten dollars through November thirtieth, twenty twenty six. From December first. Four and twenty.

Opus. Four and twenty. Astra. Ten and fifty.

Take an imaginary request. One hundred thousand uncached input tokens. Ten thousand billable output tokens. No extra tool fees.

Argon costs thirty cents at the introductory rate. Opus costs sixty cents. Astra costs one dollar fifty. Argon becomes sixty cents after its promotion.

That holds the token counts fixed. A real repair may use different amounts. More attempts change the bill. So can caching and reasoning.

One important threshold. Above two hundred and seventy two thousand input tokens, Astra doubles input and cache rates.

Speed needs a stopwatch

Output becomes one point five times the base rate. For the whole request. These are A P I prices. Do not read them as a monthly coding subscription quote.

Speed has several meanings. Time until the first response. How fast text appears. Time until the fix passes review.

Anthropic says Opus generates output more than thirty percent faster than Opus five. Its fast mode claims up to two point five times the speed. At eight dollars input. Forty output.

Per million tokens. OpenAI says Astra fast mode offers up to twice standard processing speed. At twice the standard price. Those are comparisons within each vendor's system.

They do not tell us whether Opus finishes our bug before Astra. I did not verify a comparable Argon coding speed figure from the official pages checked. So there is no third stopwatch number here. My rule for this task.

Start the clock at the prompt.

Which coding tool connects

Stop it when the regression test passes and you have reviewed the change. Count your own intervention time too. Next. Where you actually use them.

Opus five point five is available in Claude Code. Astra is listed for Codex, including its command line tool and editor extension. Check your account's model picker. Google announced Argon's broader availability on October thirteenth.

The paid Gemini A P I. And Antigravity for Google A I Ultra subscribers. What about Gemini C L I? The official model selection page I checked did not name Argon.

I cannot confirm that pairing from that page. Check support before choosing your workflow around it. For our bug, give the agent a clear boundary. Reproduce the duplicate order.

Explain the cause. Make a focused fix. Run the regression test. Show the changed files.

Keep deployment as a separate decision.

Which one, when

We are evaluating the repair here. A fluent explanation alone does not complete that job. Here is how I would choose. Guidance.

No universal winner. Trying a cost sensitive A P I workflow? Start by testing Argon. Its introductory token rates make that experiment cheaper for the fixed token bundle we calculated.

Recheck the December price before committing. Already working in Claude Code? Start with Opus. You have confirmed integration and a strong reported Terminal Bench result.

Use a real task to decide whether it earns its cost. Working in Codex on a difficult repair? Put Astra on the shortlist. Test whether its result saves enough review and retry time to justify the higher token rates.

Use the same starting code. The same bug description. The same tests. Set a time limit and a spending limit before each run.

Repeat the task before drawing a conclusion. Record the accepted fix. Total bill. Time spent.

Your corrections. Choose the setup that works for your repository. Keep the failed runs in your notes too. Otherwise you might remember the impressive repair and forget the three attempts that went nowhere.

If the result changes between runs, widen the trial. A close result is a reason to collect more evidence. Next video. Build a small coding test for your own repository.

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