Best forAnthropic's Sonnet 5.5 for coding and agent work that has to stay fast without stepping up to the flagship rate.
Per 1M tokens · catalog rate
GPT-6.1 Terra is the model OpenAI released at the end of October 2026 for fast computer use and visual work, and the writeups that followed put it next to Claude Sonnet 5.5. They are not the same product. Sonnet is the model teams already use for graded coding and tool loops. Terra is the new lower rate. 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 56% lower and its output is 56% lower. The struck-through figures are the provider list rates from the same catalog. Public comparisons still give Sonnet the higher intelligence index at the settings they publish, and they give Terra the lower bill. Read the benchmark row only when both scores are present, then test the prompts you will send.
Best forAnthropic's Sonnet 5.5 for coding and agent work that has to stay fast without stepping up to the flagship rate.
Per 1M tokens · catalog rate
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
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.
curl https://gptproto.com/v1/chat/completions \
-H "Authorization: Bearer $GPTPROTO_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "claude-sonnet-5-5",
"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.
On the live GPTProto text rates, GPT-6.1 Terra is $0.80 per million input tokens and $4.00 per million output tokens. Claude Sonnet 5.5 is $1.80 and $9.00. The provider list rates stored beside them are $1.00/$5.00 for GPT-6.1 Terra and $2.00/$10.00 for Claude Sonnet 5.5. GPT-6.1 Terra is 56% lower on input and 56% 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 $0.027 for Claude Sonnet 5.5. That illustration is input plus output only. Cache reads, cache writes, tool calls, and retries are extra, and a second attempt can erase a small gap. This gap is large enough that one retry does not automatically erase it. Count the retries anyway.
Check the live Claude Sonnet 5.5 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.
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.
Input and output tokens only, at the catalog rate loaded for this page. Cache, tools, and retries are extra.
Test Claude Sonnet 5.5 and GPT-6.1 Terra from the same GPTProto balance, at the rates on their model pages.
Get started. Great for testing the endpoint and trying new models.
The sweet spot for individual developers and small projects shipping to production.
Built for teams running production workloads. Bonus credits stack on already-discounted model pricing.
Use the model whose failure mode you can already describe.
Send the repeated visual and computer-use steps to the model with the lower GPTProto rate, and keep Sonnet on the coding steps 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.
OpenAI's launch note describes Terra as a million-token-class model. This table uses a harvested length only. A larger window is room for a long prompt, not a lower input rate.
October 2026 writeups describe Terra as the cheaper model for screenshots, forms, and computer use, and Sonnet 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 bounded code change. 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.
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.
On the live GPTProto rates, yes. Terra is $0.80 input and $4.00 output per million tokens, against $1.80 and $9.00 for Sonnet. That is 56% less on input and 56% less on output. The provider list rates in the catalog are $2.00/$10.00 for Claude Sonnet 5.5 and $1.00/$5.00 for GPT-6.1 Terra. Cache and retries can change a full bill.
This page does not award that. October 2026 writeups, including Artificial Analysis, publish a higher intelligence index for Sonnet 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.
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.
Read the context row. It appears only when a length is available for both models. Treat a larger window as room for a long request, and test quality at the lengths you will send. Do not pick either model on window size alone.
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.
Yes. Claude Sonnet 5.5 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.
Creators and developers share the models and workflows they built with.
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.

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.

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

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.

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

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.
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.

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.

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.
Text, images, and voice are configured separately. You can keep OpenRouter for text and use GPTProto for visuals.
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."
Compare models, learn API setup, and explore image and video workflows for real projects.
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