Best forOpenAI's GPT-6.1 Terra for product photos, technical drawings, application screens, and text revision.
Per 1M tokens · catalog rate
Input$1.00$0.80/M−20%
Output$5.00$4.00/M−20%
// Same-vendor model comparisonView model page
GPT-6.1 TerravsGPT-6.1 Sol
GPT-6.1 Terra vs GPT-6.1 Sol is a choice between efficient visual and computer-based work and a model positioned for more complex coding and professional tasks. Start with Terra for product-photo matching, technical drawings, application screens, and drafting or revising text. Test Sol when a difficult code change or an extended professional workflow justifies a larger token budget. Both accept text and images, return text, and are called through OpenAI routes on GPTProto.
The short answer: start with GPT-6.1 Terra. On the live GPTProto rates its input is 50% lower and its output is 50% lower. Keep Sol for the tasks where that extra spend buys a check Terra did not pass. The struck-through figures are the provider list rates from the same catalog.
View model page
Best forOpenAI's GPT-6.1 Sol for complex coding, computer use, and professional work that earns a larger token budget.
Per 1M tokens · catalog rate
Input$2.00$1.60/M−20%
Output$10.00$8.00/M−20%
// Side by side
GPT-6.1 Terra vs GPT-6.1 SolAt 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
GPT-6.1 Terra
GPT-6.1 Sol
Provider
OpenAI
OpenAI
Model ID
gpt-6.1-terra
gpt-6.1-sol
OpenRouter model
openai/gpt-6.1-terra
openai/gpt-6.1-sol
Availability
99.99%
Last 7 days98.73%
Last 7 daysRelease date
October 26, 2026
September 29, 2026
Input
$0.80$1.00 official list
$1.60$2.00 official list
Output
$4.00$5.00 official list
$8.00$10.00 official list
You save
−20%vs the catalog list rate
−20%vs the catalog list rate
Cache read
$0.04$0.05 official list
$0.08$0.10 official list
Cache write
$1.00$1.25 official list
$2.00$2.50 official list
Context window
1.05M tokens
1.05M tokens
Max output tokens
128K tokens
128K tokens
Accepts
FileImageText
FileImageText
Returns
Text
Text
Where it earns its keep
Repeatable visual checks, form extraction, and bounded edits
Cross-file debugging and multi-document work after a cheaper pass fails a check
Best first test
The first pass on a task you can grade before spending more
A difficult change that Terra left unresolved
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": "gpt-6.1-terra",
"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-sol",
"messages": [{"role": "user", "content": "Hi"}]
}'Prices per 1M tokens from the GPTProto catalog. OpenRouter figures fetched November 3, 2026. Not a GPTProto SLA.
// Price comparison
Lower on both sidesat the catalog 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. GPT-6.1 Sol is $1.60 and $8.00. The provider list rates stored beside them are $1.00/$5.00 for GPT-6.1 Terra and $2.00/$10.00 for GPT-6.1 Sol. GPT-6.1 Terra is 50% lower on input and 50% lower on output. The list rates show the same direction.
For a batch of 1 million uncached input tokens and 200,000 billed output tokens, those catalog rates come to $1.60 for GPT-6.1 Terra and $3.20 for GPT-6.1 Sol. The input portion is $0.80 versus $1.60. The output portion is $0.80 versus $1.60. Cache operations and separately billed tools are extra. Track every request in the task, including repeated context, before you call the upgrade worth it.
Define the extra result you need from Sol before escalating. For a code change, that might be a full test suite that Terra left failing. For a document, it might be a set of cross-references a simpler pass missed. An escalation rule tied to a failed check gives the extra spend a reason.
Check the live GPT-6.1 Terra and GPT-6.1 Sol 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
M tokens
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.
// 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
GPT-6.1 Terra
GPT-6.1 Sol
Supported parameters
include_reasoningmax_completion_tokensmax_tokensreasoningreasoning_effortresponse_formatseedstructured_outputs
include_reasoningmax_completion_tokensmax_tokensreasoningreasoning_effortresponse_formatseedstructured_outputs
Tool use
Listed
Listed
Median of OpenRouter provider p50s, fetched November 3, 2026. Not a GPTProto SLA.
// Top-upOne balance.
One balance.
Both models.
Test GPT-6.1 Terra and GPT-6.1 Sol 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.
Most Popular
$100
+4% bonus credits
The sweet spot for individual developers and small projects shipping to production.
Max Savings · +6%
$1,000
+6% bonus credits
Built for teams running production workloads. Bonus credits stack on already-discounted model pricing.
Running 100M tokens/month on GPT-6.1 Sol? At GPTProto pricing, that’s roughly $480 saved per year vs official pricing — before bonus credits.
// Quality and benchmarks
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
GPT-6.1 Terra
GPT-6.1 Sol
Intelligence Index
51GPT-6.1 Terra (Max)
54.5GPT-6.1 Sol (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.
GPT-6.1 Terragpt-6.1-terra
Choose GPT-6.1 Terra for the first pass
- Use Terra for image matching, form extraction, concise writing, and bounded edits. The listed input rate is 50% below GPT-6.1 Sol, and the output rate is lower by 50%.
- Terra's none effort setting is available for the lightest configuration. Both models default to medium.
- A lower token rate is not a lower cost per completed task until you count the retries that follow a failed check.
GPT-6.1 Solgpt-6.1-sol
Choose GPT-6.1 Sol when the extra cost is earned
- Use Sol for cross-file debugging, multi-document analysis, and computer-use flows with branching recovery.
- Escalate when Terra fails a named check that Sol passes. Keep the trigger and the total task cost measurable.
- Do not choose Sol for a larger context window. Read the context row. The published limits match.
Terra first, Sol when the check fails
Send the repeatable visual, writing, and bounded steps to Terra. Move a request to Sol when Terra fails a specific acceptance check that Sol passes in your evaluations. Both routes share one GPTProto key and balance.
// Context and output
Same published window.Different effort controls.
GPT-6.1 Terra lists 1.05M tokens of context. GPT-6.1 Sol lists 1.05M tokens of context and 128K tokens of output. OpenAI publishes the same context class for both, plus a maximum input and an output ceiling that this page shows when the harvest returns them. Do not pick Sol because you expect a larger window.
Terra supports none, low, medium, high, xhigh, and max reasoning effort. Sol supports low, medium, high, xhigh, and max. Both default to medium. Terra's none setting is the extra option for a request where you want the lightest configuration. Record the setting with the result.
OpenAI requires the Responses API for tool calling on both versions. Keep the identifiers explicit: gpt-6.1-terra and gpt-6.1-sol. OpenAI's direct API applies higher full-request rates above 272,000 input tokens for both models. This page shows the one GPTProto text-to-text catalog rate, not that second direct-API tier.
// How to decide
Start on Terra.Escalate on a failed check.
OpenAI lists matching product photos to catalog entries and interpreting technical drawings among Terra's use cases. Ask for the matching identifier plus the evidence, or for dimensions, labels, and units in a structured response. Score the fields against verified answers. Both models are positioned for computer use. Start Terra on repeatable steps such as reading a form or checking a displayed value. Include Sol when the workflow has more branching or a harder recovery. Use the same environment and record whether the requested state was reached.
Sol's published positioning includes complex coding and professional work. Include it when a change crosses modules, needs a difficult investigation, or takes several rounds of tool-based verification. Terra is the first evaluation for a small edit, a field extracted from a screenshot, or a draft from supplied material. For a professional deliverable with several source documents, evaluate Sol on the complete workflow and separate factual corrections from tone.
Keep image matching, form extraction, concise writing, and bounded edits on Terra. Put cross-file debugging and multi-document analysis in the group that can spend more. Run both on the same labeled examples. Validate a product identifier before saving it, run tests before accepting a patch, and check the application state after a computer action.
// 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. Compare current GPTProto pricing.
// FAQ
Terra vs Sol.Straight answers.
01What is the main difference between GPT-6.1 Terra and Sol?
Terra is positioned for efficient visual reasoning, precise computer use, and natural writing. Sol is positioned for complex coding, computer use, and professional work. They are priced differently, and Terra adds a none effort setting. Read the context row rather than assuming a window-size difference.
02Which is cheaper in GPT-6.1 Terra vs GPT-6.1 Sol?
On the live GPTProto rates, GPT-6.1 Terra is $0.80 input and $4.00 output per million tokens, against $1.60 and $8.00 for GPT-6.1 Sol. That is 50% less on input and 50% less on output. The provider list rates are $1.00/$5.00 and $2.00/$10.00.
03Does Sol have a larger context window than Terra?
GPT-6.1 Terra shows 1.05M tokens of context. GPT-6.1 Sol shows 1.05M tokens of context and 128K tokens of output. OpenAI publishes the same context class and output ceiling for both. Choose on task fit, effort controls, and measured completion cost.
04Can Terra and Sol process audio or video natively?
OpenAI documents text and image inputs with text output for both models. Audio and video input are unsupported. Use an appropriate media model, or supply extracted frames and transcripts.
05Can I call tools through Chat Completions?
OpenAI requires the Responses API for tool calling on these two versions. Use the route and request format shown in the selected GPTProto API Usage panel, then test the tool round-trip and the continuation.
06When should I move a request from Terra to Sol?
Escalate when the request needs a documented capability, or when Terra fails a specific acceptance check that Sol passes in your evaluations. Examples include an unresolved multi-file bug or a complex professional analysis. Keep the trigger and the total task cost measurable.
07Are 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.
// Social proof
See how others use GPTProto.
Creators and developers share the models and workflows they built with.
BL#Routing
Blogstra
Reddit · r/Bloggers
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.
K
#Cost
K
X (Twitter) · @ChillaiKalan__
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.
SN
#Video
Sanskriti Naruka
X (Twitter) · @snskritinaruka
I turned this single prompt into a cinematic fantasy video using GPTProto. "Continuous 15-second cinematic shot, 4K resolution, hyper-realistic dark fantasy photorealism…"
CF
#Video
Caden Flux
X (Twitter) · @Caden_Flux
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.
Z
#Creative
Zara
X (Twitter) · @ZaraIrahh
Made with Seedance 2.0 + GPT Image 2 on GPTProto. A Pixar-style commercial with the perfect glow.
MO#Integration
Many-Operation2625
Reddit · r/LLMDevs
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.
OB#Resilience
obxsurfer06
Reddit · r/singularity
I route the calls through GPTProto so a fallback is a config switch instead of a weekend rewrite when a model disappears. It turns "my default model just got export controlled" from an incident into a config change.
AL
#Models
Alice The Ai Expert
X (Twitter) · @AliceInfoAi
I used to switch between different AI tools just to compare results. Now I just use GPTProto. GPT-5, Claude, Gemini, Kimi, and more — all in one workspace.
EB#DevOps
EntireBig7258
Reddit · r/LLMDevs
I route the calls through GPTProto so the model id and latency land in one place regardless of which provider is behind it. The win is having the log schema consistent across providers.
AL
#Video
Alamin
X (Twitter) · @iam_chonchol
I created this 15-second cinematic product video with GPTProto using Seedance 2.0, and I was really impressed by how smooth the workflow was.
F
#Workflow
Fokki
X (Twitter) · @0x_fokki
GPTProto routes the character prompt and shot list to a top model on one API key: 20 minutes … voices it and burns in captions from the same key: 20 minutes.
FA#Apps
FormalAd4696
Reddit · r/SillyTavernAI
Text, images, and voice are configured separately. You can keep OpenRouter for text and use GPTProto for visuals.
AF#Teams
Any-Farm-1033
Reddit · r/EngineeringManagers
The call layer underneath the router is GPTProto, so swapping a model does not require provisioning a new provider integration. Changing models becomes cheap enough that the question stops being "should we change."
// From the blog
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// Get started
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