// Cross-vendor model comparison

Gemini 4 ArgonvsGrok 4.7

Q: Can I use both on GPTProto?

Yes. [Gemini 4 Argon](/model/google/gemini-4-argon) and [Grok 4.7](/model/grok/grok-4.7) each have pricing, an API example, and a Playground. One balance covers both. You change the model name to switch.

By GPTProtoUpdated October 21, 2026

Gemini 4 Argon and Grok 4.7 answer different constraints. Argon is the newer Google model, with a million-token-class window and the higher published intelligence index of the two. Grok 4.7 is the one that costs less per token on GPTProto and starts the reply sooner in the OpenRouter measurements.

Gemini 4 Argon is $2.40 input and $12.00 output per million tokens on GPTProto. Grok 4.7 is $1.20 input and $3.60 output. Those are the live catalog rates.

Explore Gemini 4 Argon on GPTProto Explore Grok 4.7 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 first token.

Per 1M tokens · catalog rate

Input~~$4.00~~$2.40/M−40%

Output~~$20.00~~$12.00/M−40%

View model page

Grok 4.7↗Grok · grok-4.7

Best forxAI's Grok 4.7 for interactive chat and agent loops where the first token and the output rate dominate the bill.

Per 1M tokens · catalog rate

Input~~$2.00~~$1.20/M−40%

Output~~$6.00~~$3.60/M−40%

View model page

// Side by side

Gemini 4 Argon vs Grok 4.7At 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

Grok 4.7

Provider

Google

Grok

Model ID

gemini-4-argon

grok-4.7

Availability

100%

Last 7 days

99.8%

Last 7 days

Input

$2.40~~$4.00~~ official list

$1.20~~$2.00~~ official list

Output

$12.00~~$20.00~~ official list

$3.60~~$6.00~~ official list

You save

−40%vs the catalog list rate

Cache read

$0.12~~$0.20~~ official list

$0.30~~$0.50~~ official list

Context window

1.05M tokens

500K tokens

Where it earns its keep

Longer prompts and tasks where the higher intelligence index is worth the wait

Latency-sensitive chat and high-volume generation inside a 500K-class window

Best first test

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

A user-facing chat or agent loop that should start speaking quickly

In the catalog since

Oct 2026

Sep 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": "grok-4.7",
    "messages": [{"role": "user", "content": "Hi"}]
  }'

Prices per 1M tokens from the GPTProto catalog. OpenRouter figures fetched the latest fetch. 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

Grok 4.7Grok

Input$2.00

Input−40%$1.20

Output$6.00

Output−40%$3.60

// Price comparison

Grok wins both sidesof the token rate.

GPTProto lists Grok 4.7 at $1.20 per million input tokens and $3.60 per million output tokens. Gemini 4 Argon is $2.40 and $12.00. Against the catalog's provider list rates ($2.00/$6.00 and $4.00/$20.00), Grok is 50% lower on input and 70% lower on output. Output is usually the larger line on a long reply, and it points the same way as input.

A request of 10,000 uncached input tokens and 1,000 output tokens is about $0.0156 on Grok 4.7 and $0.036 on Gemini 4 Argon at these rates. Cache, tools, and retries are not in that figure. If Argon finishes a hard prompt that Grok has to run twice, the cheaper rate can still lose.

The prices are the GPTProto text-to-text catalog rates, the same numbers as the Gemini 4 Argon and Grok 4.7 pages. OpenRouter's own token prices are not used here.

// Savings calculator

Run your own numbers.Not ours.

Multiply your expected input and output tokens by the live GPTProto rates. A chat workload and a long-document workload will not pick the same winner, so run both mixes.

Pricing model

Input tokens per month (millions)

M tokens

Output tokens per month (millions)

M tokens

You keep, per year

$3,360

Monthly run rateOfficialGPTProtoSave

Input$400$240$160

Output$300$180$120

Per year$8,400$5,040$3,360

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 Grok 4.7 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.

Pick the constraintbefore you pick the model.

The cheaper model and the higher-index model are not the same one.

Gemini 4 Argongemini-4-argon

Choose Gemini 4 Argon for the long request

Use Argon when the prompt needs the larger window. Argon is 1.05M tokens; Grok is 500K tokens.
The higher token rate is the cost of that headroom. It is only justified where the extra quality or the extra context changes the outcome.

Test Gemini 4 Argon on GPTProto

Grok 4.7grok-4.7

Choose Grok 4.7 for latency and rate

The listed rate is $1.20 input and $3.60 output per million tokens, 50% and 70% below Argon.
Stay inside the context window the table lists. A prompt that no longer fits is not a reason to truncate silently; move that call to Argon.

Test Grok 4.7 on GPTProto

Keep both behind one model name switch

Send the interactive, high-volume calls to Grok. Send the long-context and harder reasoning calls to Argon. Use the same grader, and move a step across only when the log shows the more expensive call succeeding where the cheaper one did not. Both sit on one GPTProto balance.

// Context and speed

Argon holds more.Grok starts sooner.

// How to decide

Split the testby the constraint you actually have.

Do not average a latency-sensitive chat with a long-document task and then pick one winner. Run them as two sets. In the chat set, grade first-token time, completion time, and whether the answer was usable. In the long-context set, grade whether the model used the material at the end of the prompt. Cost per successful answer, not the token rate alone, is the number to keep.

// Get started

Run the same prompton both model pages.

Open either model for the live rate, an API example, and a Playground. If the catalog has moved a rate since you read this sentence, the table on this page and the model page move together, because they read the same catalog. Browse other text models when neither constraint matches the work.

View Gemini 4 Argon API and pricing View Grok 4.7 API and pricing

// FAQ

Gemini 4 Argon vs Grok 4.7.Straight answers.

01Is Grok 4.7 cheaper than Gemini 4 Argon?+

On the live GPTProto rates, yes. Grok is $1.20 input and $3.60 output per million tokens. Argon is $2.40 and $12.00. That is 50% less on input and 70% less on output.

02Which has the larger context window?+

Gemini 4 Argon does. The table lists 1.05M tokens for Argon and 500K tokens for Grok, from the OpenRouter model record. A larger window is capacity, not a lower price.

03Where do the prices come from?+

From the GPTProto catalog for the text-to-text scene, including the provider list rate stored next to the platform rate. Latency, throughput, context, maximum output, and benchmark scores come from the OpenRouter harvest and are labeled with the fetch time. OpenRouter token prices are not shown.

04Can I use both on GPTProto?+

Yes. Gemini 4 Argon and Grok 4.7 each have pricing, an API example, and a Playground. One balance covers both. You change the model name to switch.

// 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