// Cross-vendor model comparison

Grok 4.7vsGPT-6.1 Terra

Q: Can I call both through GPTProto?

Yes. [Grok 4.7](/model/grok/grok-4.7) 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 Grok 4.7. A provider list card and the GPTProto text rate are not the same number. On this catalog, the cheaper model on input is not automatically the cheaper model on output. This page prices both from that catalog. It does not copy a token price from OpenRouter, and it does not award an intelligence winner.

The short answer: GPT-6.1 Terra is 33% lower on input, and Grok 4.7 is 10% lower on output. The struck-through figures are the provider list rates from the same catalog. A short, checkable result favors the input rate. A long reply can spend the saving. Run the mix you will actually send before you pick a default.

Explore Grok 4.7 on GPTProto Explore GPT-6.1 Terra on GPTProto

Grok 4.7↗Grok · grok-4.7

Best forxAI's Grok 4.7 for interactive chat and agent loops where the output rate does most of the work.

Per 1M tokens · catalog rate

Input~~$2.00~~$1.20/M−40%

Output~~$6.00~~$3.60/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 input 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

Grok 4.7 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

Grok 4.7

GPT-6.1 Terra

Provider

Grok

OpenAI

Model ID

grok-4.7

gpt-6.1-terra

Availability

99.7%

Last 7 days

100%

1 day of traffic since Oct 27

Input

$1.20~~$2.00~~ official list

$0.80~~$1.00~~ official list

Output

$3.60~~$6.00~~ official list

$4.00~~$5.00~~ official list

You save

−40%vs the catalog list rate

−20%vs the catalog list rate

Cache read

$0.30~~$0.50~~ official list

$0.04~~$0.05~~ official list

Where it earns its keep

Chat and agent loops where the output rate matters more than the input rate

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

Best first test

A user-facing reply that is long enough for the output price to dominate

A high-volume step with a long prompt and a short, checkable result

In the catalog since

Sep 2026

Oct 2026

API usage

curl https://gptproto.com/v1/chat/completions \
  -H "Authorization: Bearer $GPTPROTO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "grok-4.7",
    "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

Grok 4.7Grok

Input$2.00

Input−40%$1.20

Output$6.00

Output−40%$3.60

GPT-6.1 TerraOpenAI

Input$1.00

Input−20%$0.80

Output$5.00

Output−20%$4.00

// Price comparison

The cheaper sideflips with the reply.

On the live GPTProto text rates, Grok 4.7 is $1.20 per million input tokens and $3.60 per million output tokens. GPT-6.1 Terra is $0.80 and $4.00. The provider list rates stored beside them are $2.00/$6.00 for Grok 4.7 and $1.00/$5.00 for GPT-6.1 Terra. GPT-6.1 Terra is 33% lower on input. Grok 4.7 is 10% lower on output. The model that wins a short prompt is not the model that wins a long answer.

For a request with 10,000 uncached input tokens and 1,000 output tokens, those GPTProto rates come to about $0.0156 for Grok 4.7 and $0.012 for GPT-6.1 Terra. That illustration is input plus output only, and this particular mix is mostly input. Cache reads, cache writes, tool calls, and retries are extra. A reply of several thousand tokens can reverse the order. 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 Grok 4.7 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 Grok 4.7 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.

Grok 4.7grok-4.7

Choose Grok 4.7 when the reply is long

Use Grok when the answer itself is the expensive part. The listed output rate is 10% below GPT-6.1 Terra.
A lower output rate is not a lower cost per completed task until you count the input tokens and the retries. Run the same set you use for Terra.
Do not treat a missing intelligence row as a tie or as a win. The table shows a score only when the harvest has it for both models.

Test Grok 4.7 on GPTProto

GPT-6.1 Terragpt-6.1-terra

Choose GPT-6.1 Terra when the prompt is the cost

Use Terra for visual checks, form filling, and other computer-use steps with a short result. The listed input rate is 33% below Grok 4.7.
A lower input rate loses when the reply runs long. Price that mix before you move a chat route.
Do not pick Terra on window size alone. Its context row appears only when the harvest returns a length.

Test GPT-6.1 Terra on GPTProto

Route by the shape of the call

Send the long-prompt, short-result steps to the lower input rate, and the long replies to the lower output rate. 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

Read Grok's window.Wait for Terra's.

Grok 4.7 lists 500K tokens of context and 450K tokens of output. A larger window is room for a long prompt, not a lower input rate. OpenAI's launch note describes Terra as a million-token-class model. This table shows Terra's length only when the harvest returns one, and it does not borrow the rounded label from the catalog.

// How to decide

Price the mixyou will actually send.

Do not pick Terra because a list card is lower on both sides, and do not pick Grok because one GPTProto output rate is lower. Split the calls you already run. One set is a long prompt with a short, checkable result, such as a visual check or a form. The other is a chat or agent step whose reply is long. Send the same instructions, tools, and grader to both. Record task success, total tokens, retries, and time to the last token. Cost per successful task is the number to keep. The per-token winner is only a hint until that mix is real.

// 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 Grok 4.7 API and pricing View GPT-6.1 Terra API and pricing

// FAQ

Grok 4.7 vs GPT-6.1 Terra.Straight answers.

01Which one is cheaper on GPTProto?+

It depends on the side. GPT-6.1 Terra is $0.80 input and $4.00 output per million tokens. Grok 4.7 is $1.20 and $3.60. GPT-6.1 Terra is 33% lower on input, and Grok 4.7 is 10% lower on output. The provider list rates in the catalog are $2.00/$6.00 for Grok 4.7 and $1.00/$5.00 for GPT-6.1 Terra. A long reply can erase the input saving.

02Which model scores higher?+

This page does not award that. The October 2026 pricing roundups put the two models on the same shopping list, and they do not share one published benchmark configuration. The intelligence row appears only when the OpenRouter harvest has a score for both models. Use it to shortlist, then test the jobs you will run.

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. Grok's harvested length is on the table when the harvest returned it. Terra's length appears only when the harvest returns one too. 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. Grok 4.7 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