Gemini 4 Argon vs GPT-6 Astra: Same Tasks, Speed and API Cost

On October 23, 2026 we gave Gemini 4 Argon and GPT-6 Astra the same three jobs: fix a billing bug, answer questions about a 306-line log, and turn an email thread into JSON. Argon got every answer right. Astra was faster on two jobs, but cost 3.4 to 4.9 times as much per job and miscounted the log's errors twice.

Both are generally available on the API now. Argon's introductory price ends November 30. From December 1 it doubles, and Astra will still cost about twice as much per job.

List prices are per million tokens, and Argon's are its introductory prices until November 30. Both are on OmniaKey: Gemini 4 Argon and GPT-6 Astra.

Can you call Gemini 4 Argon and GPT-6 Astra today?

Yes. Argon has been generally available on the paid Gemini API since October 13, 2026, and Astra on the OpenAI API since September 3.

Some launch-week pages still say you can't use Argon. When Google announced it on September 30, it opened the model only to trusted cyber defenders in its Fairwind Program. That changed on October 13.

Neither model is on its vendor's free API tier. Argon also reaches Google AI Ultra and Pro subscribers in the Gemini app.

Argon's window is shared by input, thinking and output, and its "long decode" mode raises output to as much as 1,048,576 tokens. Argon takes text, images, video, audio and PDF. Astra takes text and images.

Astra's reasoning levels are low, medium, high, xhigh and max, with no "none". OpenAI requires its Responses API for tool calling.

Through OmniaKey you call both with one key at https://api.omniakey.com/v1, using the model IDs above. Rates are on the Gemini 4 Argon page and the GPT-6 Astra page.

How we tested

• Models: Gemini 4 Argon (gemini-4-argon) and GPT-6 Astra (gpt-6-astra).
• October 23, 2026, through OmniaKey's OpenAI-compatible Chat Completions API, streaming, with no reasoning or thinking setting, so each model used its own default. Google documents medium as Argon's default thinking level.
• The same prompt for both models. The bug fix and the log job ran twice per model, the JSON job once: 10 answers in all.
• Time is from sending the request to the last token, measured on our side, network included.
• Scored by scripts, not by taste. Each bug fix ran against 12 tests the models never saw, in an isolated Python process. The log answers were checked against an answer key from the script that generated the log, and the JSON field by field against its own key. We judged the one open question, the log's likely cause, by reading.
• Cost: the tokens of our prompt and of the answer, times each vendor's standard list price, on the first run (nothing cached yet).
• OmniaKey sells API access to both models, so we ran the tests through it. Every price here is Google's or OpenAI's own rate, not ours.
• Prices, limits and dates come from Google's and OpenAI's own pages, listed under Sources at the end and checked on October 23, 2026.

That's one or two runs per job, so read the results as examples of how each model behaves, not as a benchmark.

What happened when we gave both the same three tasks?

Both fixed the bug and filled in the JSON correctly. The long log separated them, and Astra was faster on two of the three jobs.

Test 1: Fix a billing bug in Python

Does the model fix every broken rule in a small file, not only the three tests that fail?

Prompt:

You're fixing a bug report in our billing service. Below are billing.py, its test file and the test output. Three tests fail.

Fix billing.py so it follows every rule in its docstring, not only the failing tests. Keep the function names and signatures, and use only the Python standard library.

Reply with the complete corrected billing.py in one ```python code block, then at most three short bullets on what you changed.

Below that we pasted the 34-line billing.py, its four tests and the test output: 3 failed, 1 passed. The file had four bugs:

1. round(), which rounds half to even where the rules say half up.
2. Volume discounts that started at 11 and 51 units instead of 10 and 50.
3. The coupon applied after tax.
4. No check that the coupon is between 0 and 100.

We scored each fix with 12 tests the models never saw.

• Gemini 4 Argon (10.8 s, 10.6 s): both fixes passed 12 of 12. Both used Python's decimal module with half-up rounding. The first one uses a type hint (float | Decimal) that needs Python 3.10 or newer.
• GPT-6 Astra (17.3 s, 6.4 s): both fixes passed 12 of 12. The first used exact fractions and was the only Astra answer that reported reasoning tokens (185). It was also the slowest answer in this test.

After grading, we tried three extra edge cases on all four fixes. Astra's second fix was a cent off on two of them: it rounds the price after the coupon instead of the coupon itself. So a 50% coupon on a 1,001-cent order took off 500 cents, not 501.

Verdict: Tie on our tests. Both models fixed all four bugs. Argon's two answers also handled every extra case, and each cost under a cent against 3 to 5 cents for Astra.

Test 2: Find the cause in a 306-line log

Can the model count, look up and reason across a long text without slipping?

Prompt:

Below is a 20-minute slice of our API gateway's access log. Answer five questions using only this log.

Q1. When did the first 503 happen? Give the timestamp, the request ID and the upstream.

Q2. How many requests returned 503 in total, and which upstream or upstreams served them?

Q3. Which request was the slowest? Give its request ID, path and rt_ms.

Q4. List every request made by user u_4471, in order, as time method path status.

Q5. In one or two sentences: what most likely caused the 503s, and what would you check first?

Start each answer with Q1: to Q5:. No preamble.

Below the questions we pasted 306 lines (43 KB) of a made-up gateway log covering 20 minutes. It had traps: a user ID with two digits swapped (u_4417), a scanner hitting missing pages, and a slow search request (9,214 ms) just short of the slowest one (9,871 ms). A script generated the log and the answer key.

• Gemini 4 Argon (9.5 s, 10.0 s): 5 of 5 in both runs, including the count of 14 errors, all from payments-1. Its cause: that upstream failed fast (2–9 ms, identical 162-byte replies) from 14:07:12 to 14:09:55 while payments-2 stayed healthy.
• GPT-6 Astra (5.7 s, 5.4 s): 4 of 5 in both runs. It counted 16 errors, then 15. There were 14. Everything else was right, including the same cause.

Verdict: Argon. A wrong count in a log is the kind of slip you only catch if you check, and Astra made it in both runs. Astra answered in about half the time.

If you use either model on logs, ask for the matching lines along with any count, so you can check the number in seconds.

Test 3: Turn an email thread into JSON

Can the model pull exact fields out of messy text and follow a strict format?

Prompt:

Turn this support email thread into one JSON object for our ticketing system.

Schema (every key is required):

• "order_id": string
• "customer_email": string, the From address of the customer's most recent message
• "items": array of objects with "sku" (string) and "quantity" (integer), only the items in the customer's latest request
• "requested_action": one of "refund", "replacement", "other"
• "refund_amount_cents": integer, or null if no refund is requested
• "deadline": "YYYY-MM-DD", or null if none is given; resolve relative dates against the Date header of the message they appear in
• "escalation_risk": boolean, true if the customer threatens a chargeback, a bank dispute or legal action

If the customer changed their mind, use the latest request. Return only the JSON object: no code fences, no comments, no extra keys.

The thread had two emails from the same customer. On Monday, from a work address, they asked for two replacement lamps.

On Wednesday, from a personal address, they changed their mind: refund two lamps and a charger, $157.50 in all, by "this Friday", or they would dispute the charge with their bank. The thread also mentions an older order that had no problem.

• Gemini 4 Argon (6.8 s): all seven fields right, in valid JSON with nothing extra: a refund of 15750 cents, the personal address, both items, and "this Friday" resolved to 2026-10-16.
• GPT-6 Astra (3.9 s): the same, field for field.

Verdict: Tie on accuracy. Astra answered in about half the time. Argon cost $0.003 against $0.013.

On speed, Astra started answering sooner. Its first token came in 3.1 to 3.8 seconds on every answer except the one that reasoned (9.4 s). Argon's first token took 5.1 to 6.8 seconds, even for the short JSON prompt, and it reported 0 thinking tokens on all five answers.

So if replies must start fast, Astra has the edge. If a count or a lookup must be right, Argon did better here.

What do the published benchmarks say?

On Google's own table, Argon leads six of ten rows and Astra three, with one tie. Artificial Analysis, an independent benchmarking site, scores them level.

These are Google's numbers from the Gemini 4 Argon model card, results as of October 2026.

Astra's wins are the agent rows: FrontierSWE v2 (65.5% vs 55.0%), Terminal-bench 4.0 and OSWorld-2.0, which test coding in a terminal and using a computer. Argon's biggest leads are in knowledge work: Harvey's legal agent test (19.6% vs 5.4%), AutomationBench and the Vals Index.

Artificial Analysis gives both 53 on its Intelligence Index, but it ran Argon at high reasoning and Astra at max. At those settings Argon wrote about twice as many output tokens per task (36k vs 17k) and still cost less per task ($1.99 vs $3.26). It also measured Argon generating faster once it starts: 82 vs 56 tokens per second.

OpenAI reports 98% on FrontierMath Tier 4 and 99.9% on ARC-AGI-3 for Astra. Google's table doesn't include those tests, so there is no Argon number to put beside them.

So for terminal agents and computer use, even Google's numbers favor Astra. For document-heavy knowledge work they favor Argon.

How much does each one cost per task?

At list prices, Astra cost 3.4 to 4.9 times as much per job as Argon in our runs. When Argon's price doubles on December 1, Astra will still cost 1.7 to 2.4 times as much.

These prices come from Google's Gemini API pricing page and OpenAI's GPT-6 Astra model page, checked October 23, 2026.

Batch and Flex cost half on both. Astra prompts over 272K input tokens cost 2x for input and cache and 1.5x for output on the whole request. Google's pricing page lists no long-prompt surcharge for Argon.

Scaled up, 1,000 log jobs like ours come to about $60 on Argon until November 30, about $120 from December 1, and about $205 on Astra.

The gap is smaller than the 5x gap in list prices for two reasons. Argon wrote longer answers (401 vs 218 output tokens on the log job), and Google counted the same 43 KB log as more input tokens than OpenAI did.

Ask about the same log again two minutes later and both bill most of it at the cached rate. Our second log run cost about $0.011 on Argon and about $0.033 on Astra, roughly 80% less than the first.

One OpenAI rule to know: on GPT-6 models the default caching mode bills prompt tokens written to the cache at 1.25 times the input rate. So the first log call can cost about $0.25 instead of $0.205.

Which one should you choose?

Pick Argon for long documents, logs and volume. Pick Astra when replies must start fast or your agents work in a terminal or on a computer.

On December 1 Argon's price doubles to $4 and $20 per million tokens, which is still 40% of Astra's list price, so the choice above holds. Mixing them is easy, because both run on the same API with one key.

A simple split: use Argon where a wrong number is expensive, and Astra where a slow reply is. Start from Gemini 4 Argon on OmniaKey and GPT-6 Astra on OmniaKey.

How do you run the same test with one API key?

Use the OpenAI Python SDK pointed at OmniaKey, and change one string to switch models. We ran this code on October 23, 2026.

import os
import time

from openai import OpenAI

client = OpenAI(
    base_url="https://api.omniakey.com/v1",
    api_key=os.environ["OMNIAKEY_API_KEY"],
)

model = "gemini-4-argon"  # change to "gpt-6-astra" to compare
prompt = open("ticket-prompt.md").read()  # the support-ticket task from Test 3

start = time.perf_counter()
response = client.chat.completions.create(
    model=model,
    messages=[{"role": "user", "content": prompt}],
)
print(response.choices[0].message.content)
print(f"{time.perf_counter() - start:.1f} s", response.usage)

1. Create a key on OmniaKey and set it as OMNIAKEY_API_KEY.
2. Install the SDK: pip install openai.
3. Save your prompt as ticket-prompt.md, for example the Test 3 instructions followed by one of your own email threads.
4. Run the script. It prints the answer, the time and the token usage.
5. Change model to "gpt-6-astra" and run it again.

When we ran this script on Argon with the Test 3 prompt, it returned the correct JSON in 7.1 seconds, with 634 input and 152 output tokens.

Common questions

Is Gemini 4 Argon better than GPT-6 Astra?

In our three jobs Argon was right every time and cost less, and Astra was faster on two jobs. Google's table has Argon ahead on six of ten rows, and Artificial Analysis scores them level at 53.

Is Gemini 4 Argon available to everyone?

It has been on the paid Gemini API since October 13, 2026, but not on the free tier. Google AI Ultra and Pro subscribers also get it in the Gemini app.

How much cheaper is Gemini 4 Argon than GPT-6 Astra?

Until November 30 its list price is a fifth of Astra's: $2 and $10 vs $10 and $50 per million tokens. In our jobs Astra cost 3.4 to 4.9 times as much. From December 1 Argon's list price is 40% of Astra's ($4 and $20 vs $10 and $50).

Which is better for coding?

Both fixed our bug and passed all 12 hidden tests. On Google's table Argon leads DeepSWE v1.1 (77.9% vs 74.1%), while Astra leads FrontierSWE v2 (65.5% vs 55.0%) and Terminal-bench 4.0 (58.2% vs 57.4%).

Does Gemini 4 Argon think by default?

Yes, at medium. It reported 0 thinking tokens on our three jobs, but spent 471 on a harder counting puzzle and got it right, so it thinks when a task needs it.

Sources

• Google: Gemini 4 Argon announcement (September 30, 2026), general availability post (October 13, 2026), Gemini API changelog, model page, pricing, thinking levels, model card
• OpenAI: GPT-6 Astra announcement, model page, pricing, prompt caching, changelog
• Artificial Analysis: comparison page

All checked on October 23, 2026.

Update log

• October 23, 2026: First published. Tests run on October 23 through the OmniaKey API.