// Cross-vendor model comparison

Gemini 3.8 FlashvsGPT-6.1 Terra

By GPTProtoUpdated October 28, 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 3.8 Flash. They are not the same product. Flash is the high-volume Gemini route. Terra is the new computer-use and visual model. Public comparisons of this pair mix effort settings, so this page does not award an index. It prices both from the GPTProto text catalog, and it does not copy a token price from OpenRouter.

The short answer: start the cost check with GPT-6.1 Terra. On the live GPTProto rates its input is 11% lower and its output is 11% lower. The struck-through figures are the provider list rates from the same catalog. The gap is small enough that retries can erase it. Keep both models in the test, and read a benchmark row only when both scores are present.

Explore Gemini 3.8 Flash on GPTProto Explore GPT-6.1 Terra on GPTProto

Gemini 3.8 Flash↗Google · gemini-3.8-flash

Best forGoogle's Gemini 3.8 Flash for high-volume prompts where a Flash-class model is the one you already grade.

Per 1M tokens · catalog rate

Input~~$1.50~~$0.90/M−40%

Output~~$7.50~~$4.50/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 computer use and visual tasks at the catalog's newer 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 3.8 Flash 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 3.8 Flash

GPT-6.1 Terra

Provider

Google

OpenAI

Model ID

gemini-3.8-flash

gpt-6.1-terra

OpenRouter model

google/gemini-3.8-flash

openai/gpt-6.1-terra

Availability

100%

Last 7 days

99.99%

Last 7 days

Release date

September 2, 2026

October 26, 2026

Input

$0.90~~$1.50~~ official list

$0.80~~$1.00~~ official list

Output

$4.50~~$7.50~~ official list

$4.00~~$5.00~~ official list

You save

−40%vs the catalog list rate

−20%vs the catalog list rate

Cache read

$0.09~~$0.15~~ official list

$0.04~~$0.05~~ official list

Cache write

$0.60~~$1.00~~ official list

$1.00~~$1.25~~ official list

Context window

1,048,576 tokens

1.05M tokens

Max output tokens

65,536 tokens

128K tokens

Accepts

TextImageVideoFileAudio

FileImageText

Returns

Text

Where it earns its keep

Repeated prompts that already run on a Gemini Flash route

Visual checks and computer-use steps where the catalog rate still has to clear the bar

Best first test

A high-volume step you can grade against Terra

A screenshot, a form, or a desktop step with a 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": "gemini-3.8-flash",
    "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 November 3, 2026. Not a GPTProto SLA.

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

OfficialGPTProto

Gemini 3.8 FlashGoogle

Input$1.50

Input−40%$0.90

Output$7.50

Output−40%$4.50

GPT-6.1 TerraOpenAI

Input$1.00

Input−20%$0.80

Output$5.00

Output−20%$4.00

// Price comparison

A small gapon both sides.

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 3.8 Flash is $0.90 and $4.50. The provider list rates stored beside them are $1.00/$5.00 for GPT-6.1 Terra and $1.50/$7.50 for Gemini 3.8 Flash. GPT-6.1 Terra is 11% lower on input and 11% 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 $0.0135 for Gemini 3.8 Flash. That illustration is input plus output only. Cache reads, cache writes, tool calls, and retries are extra, and a second attempt can erase the gap. 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 3.8 Flash 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

// Speed and access

How fast. How it is called.What the snapshot shows.

Latency and throughput are OpenRouter provider medians. A missing row was not in the fetch, and it is left out rather than guessed.

Spec

Gemini 3.8 Flash

GPT-6.1 Terra

Supported parameters

include_reasoningmax_tokensreasoningreasoning_effortresponse_formatseedstopstructured_outputs

include_reasoningmax_completion_tokensmax_tokensreasoningreasoning_effortresponse_formatseedstructured_outputs

Tool use

Listed

Median of OpenRouter provider p50s, fetched November 3, 2026. Not a GPTProto SLA.

// Top-up

One balance.
Both models.

Test Gemini 3.8 Flash 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.

What the index shows.What it cannot decide.

Only scores present for both models are shown. A higher index is a shortlist signal. It does not decide your tasks.

Spec

Gemini 3.8 Flash

GPT-6.1 Terra

Intelligence Index

30.5Gemini 3.8 Flash (High)

51GPT-6.1 Terra (Max)

Artificial Analysis scores as carried by the OpenRouter harvest, fetched November 3, 2026.

// Verdict

Which model fitsthe call you make most?

Use the model whose failure mode you can already describe.

Gemini 3.8 Flashgemini-3.8-flash

Choose Gemini 3.8 Flash for the route you already grade

Use Flash when the prompt already runs on Gemini and you can grade the result against Terra.
Do not treat one intelligence index as the decision. Published comparisons of this pair mix effort settings, and the table shows a score only when the harvest has it for both models.
The latency figure on this page is an OpenRouter provider median, not a promise about your route. Time the call you will actually make.

Test Gemini 3.8 Flash on GPTProto

GPT-6.1 Terragpt-6.1-terra

Choose GPT-6.1 Terra when the rate is the constraint

Use Terra for visual checks, form filling, and other computer-use steps with a clear finish line. The listed input rate is 11% below Gemini 3.8 Flash, and the output rate is lower by 11%.
A lower token rate is not a lower cost per completed task until you count retries. Run the same set you use for Flash.
Do not pick Terra on window size alone. If its context cell is a catalog label, the table note says so.

Test GPT-6.1 Terra on GPTProto

Route by the step, not by the brand

Send the repeated, checkable steps to the model with the lower GPTProto rate, and keep Flash on the prompts your Gemini 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

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

Gemini 3.8 Flash lists 1,048,576 tokens of context and 65,536 tokens of output. That length is the harvested integer, not a rounded million label. 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. If Terra's context cell is filled from the catalog label, the table note says so, and that label is rounded.

// How to decide

Grade the new modelon the work Flash already runs.

Do not pick Terra because it is new, or Flash because the route is already Gemini, until the same set has run on both. Use work you already trust: a high-volume prompt, a visual check, and a case that must reject a bad input. Send the same instructions, tools, and grader. Record task success, total tokens, retries, and time to the last token. Cost per successful task is the number to keep. A small token gap loses when it needs the extra try.

// 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 3.8 Flash API and pricing View GPT-6.1 Terra API and pricing

// FAQ

Gemini 3.8 Flash vs GPT-6.1 Terra.Straight answers.

01Is GPT-6.1 Terra cheaper than Gemini 3.8 Flash on GPTProto?+

On the live GPTProto rates, yes. Terra is $0.80 input and $4.00 output per million tokens, against $0.90 and $4.50 for Flash. That is 11% less on input and 11% less on output. The provider list rates in the catalog are $1.50/$7.50 for Gemini 3.8 Flash and $1.00/$5.00 for GPT-6.1 Terra. Cache and retries can change a full bill.

02Which model scores higher?+

This page does not award that. A published comparison of these names uses a non-reasoning Terra setting against a high-effort Flash setting, so the indexes are not the same test. 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. Flash's harvested length is the integer the harvest returned, which is not a rounded million label. If Terra's cell is filled from the catalog label, the table note says so. 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 3.8 Flash 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

Gemini 3.8 FlashvsGPT-6.1 Terra

Gemini 3.8 Flash vs GPT-6.1 TerraAt a Glance.

A small gapon both sides.

Run your own numbers.Not ours.

How fast. How it is called.What the snapshot shows.

One balance.Both models.

What the index shows.What it cannot decide.

Which model fitsthe call you make most?

Route by the step, not by the brand

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

Grade the new modelon the work Flash already runs.

Try both on one balancebefore you pick a default.

Gemini 3.8 Flash vs GPT-6.1 Terra.Straight answers.

See how others use GPTProto.

AI Model Guides.Practical API Tutorials.

SillyTavern API Integration Guide: Connect GPTProto and Start Roleplaying

Best LLMs for Tool Calling in 2026: 9 Models Compared

Kimi K3.1 Pro vs Claude Opus 5.5: Which Is Better for Building Apps?

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

One balance.
Both models.