// Cross-vendor model comparison

Gemini 4 ArgonvsGPT-6.1 Terra

Q: Can I call both through GPTProto?

Yes. [Gemini 4 Argon](/model/google/gemini-4-argon) and [GPT-6.1 Terra](/model/openai/gpt-6.1-terra) each have a model page with pricing, an API example, and a Playground. Switching models is a model-name change on the same balance.

By GPTProtoUpdated October 27, 2026

GPT-6.1 Terra is the model OpenAI released at the end of October 2026, and the pricing roundups that followed put it next to Gemini 4 Argon. They are not the same product. Argon is Google's high-end model for long prompts. Terra is the new lower rate for visual checks, forms, and other computer-use steps that repeat. This page prices both from the GPTProto text catalog. It does not copy a token price from OpenRouter, and it does not treat one public index as the winner.

The short answer: start the cost check with GPT-6.1 Terra. On the live GPTProto rates its input is 67% lower and its output is 67% lower. The struck-through figures are the provider list rates from the same catalog. Public comparisons still give Argon the higher intelligence index at the settings they publish. Read a benchmark row only when both scores are present, then test the prompts you will send.

Explore Gemini 4 Argon on GPTProto Explore GPT-6.1 Terra on GPTProto

Gemini 4 Argon↗Google · gemini-4-argon

Best forGoogle's current high-end Gemini for long prompts and work where the published intelligence index matters more than the token rate.

Per 1M tokens · catalog rate

Input~~$4.00~~$2.40/M−40%

Output~~$20.00~~$12.00/M−40%

View model page

GPT-6.1 Terra↗OpenAI · gpt-6.1-terra

Best forOpenAI's GPT-6.1 Terra, released October 2026, for high-volume computer use and visual tasks at the lower catalog rate.

Per 1M tokens · catalog rate

Input~~$1.00~~$0.80/M−20%

Output~~$5.00~~$4.00/M−20%

View model page

// Side by side

Gemini 4 Argon vs GPT-6.1 TerraAt a Glance.

GPTProto prices are the live text-to-text catalog rates. Latency, context, and benchmarks appear only when the OpenRouter harvest returned them for both models.

Spec

Gemini 4 Argon

GPT-6.1 Terra

Provider

Google

OpenAI

Model ID

gemini-4-argon

gpt-6.1-terra

Availability

100%

Last 7 days

100%

1 day of traffic since Oct 27

Input

$2.40~~$4.00~~ official list

$0.80~~$1.00~~ official list

Output

$12.00~~$20.00~~ official list

$4.00~~$5.00~~ official list

You save

−40%vs the catalog list rate

−20%vs the catalog list rate

Cache read

$0.12~~$0.20~~ official list

$0.04~~$0.05~~ official list

Where it earns its keep

Longer prompts and tasks where the higher published index is worth the rate

Repeated visual and computer-use steps where the token rate is the constraint

Best first test

A long document or codebase that still has to fit in one request

A high-volume step with a screenshot, a form, or a checkable result

In the catalog since

Oct 2026

API usage

curl https://gptproto.com/v1/chat/completions \
  -H "Authorization: Bearer $GPTPROTO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-4-argon",
    "messages": [{"role": "user", "content": "Hi"}]
  }'

curl https://gptproto.com/v1/chat/completions \
  -H "Authorization: Bearer $GPTPROTO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-6.1-terra",
    "messages": [{"role": "user", "content": "Hi"}]
  }'

Prices per 1M tokens from the GPTProto catalog. OpenRouter figures fetched October 20, 2026. Not a GPTProto SLA.

Input & output price · per 1M tokens · each pair scaled to its official rate

OfficialGPTProto

Gemini 4 ArgonGoogle

Input$4.00

Input−40%$2.40

Output$20.00

Output−40%$12.00

GPT-6.1 TerraOpenAI

Input$1.00

Input−20%$0.80

Output$5.00

Output−20%$4.00

// Price comparison

Google's high endagainst the new rate.

On the live GPTProto text rates, GPT-6.1 Terra is $0.80 per million input tokens and $4.00 per million output tokens. Gemini 4 Argon is $2.40 and $12.00. The provider list rates stored beside them are $1.00/$5.00 for GPT-6.1 Terra and $4.00/$20.00 for Gemini 4 Argon. GPT-6.1 Terra is 67% lower on input and 67% lower on output, so the cheaper model does not flip when the reply is long.

For a request with 10,000 uncached input tokens and 1,000 output tokens, those GPTProto rates come to about $0.012 for GPT-6.1 Terra and $0.036 for Gemini 4 Argon. That illustration is input plus output only. Cache reads, cache writes, tool calls, and retries are extra. Vendor price cards can step up once a prompt passes a length threshold. This page shows the one text-to-text catalog rate, not a second tier.

Check the live Gemini 4 Argon and GPT-6.1 Terra pages before you budget a production route. This comparison reads the same catalog those pages use. It does not copy a price from OpenRouter.

// Savings calculator

Run your own numbers.Not ours.

Multiply the input and output tokens you expect by the live GPTProto rates. Then run a real set. A useful budget includes cache, tools, retries, and the share of requests each model finishes correctly.

Pricing model

Input tokens per month (millions)

M tokens

Output tokens per month (millions)

M tokens

You keep, per year

$1,080

Monthly run rateOfficialGPTProtoSave

Input$200$160$40

Output$250$200$50

Per year$5,400$4,320$1,080

Input and output tokens only, at the catalog rate loaded for this page. Cache, tools, and retries are extra.

See top-up tiers

// Top-up

One balance.
Both models.

Test Gemini 4 Argon and GPT-6.1 Terra from the same GPTProto balance, at the rates on their model pages.

$15

Straight credit · no bonus

Get started. Great for testing the endpoint and trying new models.

Which model fitsthe call you make most?

Use the model whose failure mode you can already describe.

Gemini 4 Argongemini-4-argon

Choose Gemini 4 Argon for the long prompt

Use Argon when the prompt is long and you already grade Gemini on that work, especially when a wrong read costs more than the token gap.
Do not treat a single intelligence index as the decision. Public comparisons of this pair change with the effort setting, and the table shows only the configuration the harvest returned for both models.
Measure your own prompt. The latency figure on this page is an OpenRouter provider median, not a promise about your route.

Test Gemini 4 Argon on GPTProto

GPT-6.1 Terragpt-6.1-terra

Choose GPT-6.1 Terra when the call repeats

Use Terra for visual checks, form filling, and other computer-use steps with a clear finish line, especially when the call repeats. The listed input rate is 67% below Gemini 4 Argon, and the output rate is lower by 67%.
A lower token rate is not a lower cost per completed task until you count retries. Run the same set you use for Argon.
Do not pick Terra, or reject it, on window size alone. Use the context row when both lengths are on the table.

Test GPT-6.1 Terra on GPTProto

Route by the step, not by the brand

Send the repeated visual and computer-use steps to the model with the lower GPTProto rate, and keep Argon on the long prompts your grader already prefers. Write down when the switch changes the result. Both models are on one GPTProto balance, so the split is a model-name change.

// Context and output

Show the windowonly when both sides have one.

OpenAI's launch note describes Terra as a million-token-class model. This table uses a harvested length only, and it does not round another model's window into that label. A larger window is room for a long prompt, not a lower input rate.

// How to decide

Split the long promptfrom the repeated visual step.

October 2026 writeups describe Terra as the cheaper model for screenshots, forms, and computer use, and Argon as the model that still leads the published intelligence index at the settings they show. Those indexes move with the effort setting. Build two sets from work you already run: a visual or computer-use set with a checkable result, and a long prompt you already send to Gemini. Send the same instructions, tools, and grader to both. Record task success, total tokens, retries, and time to the last token. Keep cost per successful task next to the per-token rate.

// Get started

Try both on one balancebefore you pick a default.

Each model page has the current rate, an API example, and a Playground. Run your own prompts, then keep the routing rule that wins on quality, latency, and budget. Browse the model catalog if you want a third candidate in the same test.

View Gemini 4 Argon API and pricing View GPT-6.1 Terra API and pricing

// FAQ

Argon vs GPT-6.1 Terra.Straight answers.

01Is GPT-6.1 Terra cheaper than Gemini 4 Argon on GPTProto?+

On the live GPTProto rates, yes. Terra is $0.80 input and $4.00 output per million tokens, against $2.40 and $12.00 for Argon. That is 67% less on input and 67% less on output. The provider list rates in the catalog are $4.00/$20.00 for Gemini 4 Argon and $1.00/$5.00 for GPT-6.1 Terra. Cache and retries can change a full bill.

02Which model is more intelligent?+

This page does not award that. October 2026 writeups, including Artificial Analysis, publish a higher intelligence index for Argon than for Terra at the settings they show, and those settings are not the same effort level. The intelligence row appears only when the OpenRouter harvest has a score for both models. Use it to shortlist, then test your own tasks.

03Which one responds faster?+

Read the latency and throughput rows, which are medians of OpenRouter provider p50s and carry the fetch date in the table note. They are not a GPTProto latency target. A short chat cares about latency. A long completion cares about throughput.

04Do they accept the same context window?+

Read the context row. It appears only when a length is available for both models. Argon's harvested length is the integer the harvest returned, which is not a rounded million label. Treat a larger window as room for a long request, and test quality at the lengths you will send.

05Are these OpenRouter prices?+

No. The dollar amounts are GPTProto's catalog rates for the text-to-text scene, including the provider list rate the catalog stores beside them. OpenRouter is the source for latency, throughput, context, output limit, and the Artificial Analysis scores, each marked with its fetch time.

06Can I call both through GPTProto?+

Yes. Gemini 4 Argon and GPT-6.1 Terra each have a model page with pricing, an API example, and a Playground. Switching models is a model-name change on the same balance.

// Social proof

See how others use GPTProto.

Creators and developers share the models and workflows they built with.

Blogstra

Reddit · r/Bloggers

#Routing

For a team already juggling DeepSeek, Kimi, Qwen, or other providers, a shared routing layer can be reasonable if it removes duplicated work and the fallback behavior is tested. GPTProto is one implementation of this approach.

X (Twitter) · @ChillaiKalan__

#Cost

I've been testing GPTProto recently, and it's honestly made my creative workflow much simpler. Instead of paying for multiple subscriptions, I can access several leading AI models from one place.

Sanskriti Naruka

X (Twitter) · @snskritinaruka

#Video

I turned this single prompt into a cinematic fantasy video using GPTProto. "Continuous 15-second cinematic shot, 4K resolution, hyper-realistic dark fantasy photorealism…"

Caden Flux

X (Twitter) · @Caden_Flux

#Video

I challenged myself to create a cinematic AI cooking short in just 15 seconds. I used GPTProto to bring the entire workflow together, from image generation to video, all in one place.

Zara

X (Twitter) · @ZaraIrahh

#Creative

Made with Seedance 2.0 + GPT Image 2 on GPTProto. A Pixar-style commercial with the perfect glow.

Many-Operation2625

Reddit · r/LLMDevs

#Integration

What worked for me was pointing the cloud connections at GPTProto so the frontend only sees one endpoint and I just change the model name to swap. I am not rebuilding a connection from scratch every time.

// From the blog

AI Model Guides.Practical API Tutorials.

Compare models, learn API setup, and explore image and video workflows for real projects.

View all posts →

// Get started

Your next 100,000,000 tokensshouldn't cost full price.

Create images and videos online, or bring text, image, and video models into your app with one API key. Explore discounted rates on selected models.

✓OpenAI-compatible
✓200+ models
✓10–30% lower than official
✓High uptime, auto-failover

✓Copied to clipboard