// Cross-vendor model comparison

Claude Opus 5.5vsGPT-6.1 Sol

Q: Can I call both through GPTProto?

Yes. [Claude Opus 5.5](/model/claude/claude-opus-5-5) and [GPT-6.1 Sol](/model/openai/gpt-6.1-sol) 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 21, 2026

Claude Opus 5.5 and GPT-6.1 Sol are the models teams shortlist when a coding agent has to do more than answer one question. They are not the same product. Opus is Anthropic's flagship for long-running, open-ended work. Sol is OpenAI's newer high-end model, and on GPTProto it lists a lower rate for both input and output tokens.

Claude Opus 5.5 is $3.60 input and $18.00 output per million tokens on GPTProto. GPT-6.1 Sol is $1.60 input and $8.00 output. Those are the live catalog rates.

Explore Claude Opus 5.5 on GPTProto Explore GPT-6.1 Sol on GPTProto

Claude Opus 5.5↗Claude · claude-opus-5-5

Best forAnthropic's flagship for long-running coding agents and work that has to hold a judgment across many steps.

Per 1M tokens · catalog rate

Input~~$4.00~~$3.60/M−10%

Output~~$20.00~~$18.00/M−10%

View model page

GPT-6.1 Sol↗OpenAI · gpt-6.1-sol

Best forOpenAI's high-end general model for coding and agent work when the token bill is part of the decision.

Per 1M tokens · catalog rate

Input~~$2.00~~$1.60/M−20%

Output~~$10.00~~$8.00/M−20%

View model page

// Side by side

Claude Opus 5.5 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

Claude Opus 5.5

GPT-6.1 Sol

Provider

Claude

OpenAI

Model ID

claude-opus-5-5

gpt-6.1-sol

Availability

99.9%

Last 7 days

93.3%

Last 7 days

Input

$3.60~~$4.00~~ official list

$1.60~~$2.00~~ official list

Output

$18.00~~$20.00~~ official list

$8.00~~$10.00~~ official list

You save

−10%vs the catalog list rate

−20%vs the catalog list rate

Cache read

$0.18~~$0.20~~ official list

$0.08~~$0.10~~ official list

Cache write

$4.50~~$5.00~~ official list

$2.00~~$2.50~~ official list

Context window

1M tokens

1.05M tokens

Where it earns its keep

Long-running agentic coding, hard review, and open-ended knowledge work

Coding and agent work where a lower token rate still clears the quality bar

Best first test

A coding agent that stays with one task across a large codebase

High-volume agent steps that have a checkable result

In the catalog since

Sep 2026

API usage

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

Claude Opus 5.5Claude

Input$4.00

Input−10%$3.60

Output$20.00

Output−10%$18.00

GPT-6.1 SolOpenAI

Input$2.00

Input−20%$1.60

Output$10.00

Output−20%$8.00

// Price comparison

The token-price gapis the whole rate, not just input.

On the live GPTProto text rates, GPT-6.1 Sol is $1.60 per million input tokens and $8.00 per million output tokens. Claude Opus 5.5 is $3.60 and $18.00. The catalog's provider list rates, shown struck through in the table, are $2.00/$10.00 and $4.00/$20.00. Sol 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 $0.024 for GPT-6.1 Sol and $0.054 for Claude Opus 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 the gap.

Check the live Claude Opus 5.5 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

Input tokens per month (millions)

M tokens

Output tokens per month (millions)

M tokens

You keep, per year

$2,160

Monthly run rateOfficialGPTProtoSave

Input$400$320$80

Output$500$400$100

Per year$10,800$8,640$2,160

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 Claude Opus 5.5 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.

Which model fitsthe call you make most?

Use the model whose failure mode you can already describe.

Claude Opus 5.5claude-opus-5-5

Choose Opus 5.5 when the attempt is expensive

Use Opus when an agent has to stay inside a large codebase, review a difficult change, or carry a decision across many tool calls.
Measure your own prompt. The latency figure on this page is an OpenRouter provider median, not a promise about your route.

Test Claude Opus 5.5 on GPTProto

GPT-6.1 Solgpt-6.1-sol

Choose GPT-6.1 Sol when the rate is the constraint

Use Sol for coding and agent steps with a clear finish line, especially when the call repeats. The listed input rate is 56% below Opus, and the output rate is lower by 56%.
A lower token rate is not a lower cost per completed task until you count retries. Run the same set you use for Opus.
Sol's context window is in the same million-token class. Do not pick it, or reject it, on window size alone.

Test GPT-6.1 Sol on GPTProto

Route by the step, not by the brand

A practical split is to send the bounded, high-volume steps to Sol and escalate the open-ended or high-stakes steps to Opus. Keep the same grader at the handoff, and write down when the escalation actually changes the result. Both models are on one GPTProto balance, so the split is a model-name change.

// Context and output

The windows are close.The stream is not.

// How to decide

Price the successful task,not the token.

Build the set from work you already run: a bounded code change, a long-context read, and the failure that is expensive when it ships. Send the same instructions, tools, and grader to both models. Record task success, total tokens, retries, and time to the last token. Report cost per successful task next to the per-token rate. A single average will hide the slice where Opus earns the higher rate, and the slice where Sol's lower rate is the whole story.

// 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 Claude Opus 5.5 API and pricing View GPT-6.1 Sol API and pricing

// FAQ

Opus 5.5 vs GPT-6.1 Sol.Straight answers.

01Is GPT-6.1 Sol cheaper than Claude Opus 5.5?+

On the live GPTProto rates, yes. Sol is $1.60 input and $8.00 output per million tokens, against $3.60 and $18.00 for Opus. That is 56% less on input and 56% less on output. Cache and other charges can change a full bill.

02Which 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.

03Do they accept the same context window?+

Not exactly. The table lists each model's published context and maximum output from the OpenRouter snapshot. Treat a larger window as room for a long request, and test quality at the lengths you will send.

04Are 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.

05Can I call both through GPTProto?+

Yes. Claude Opus 5.5 and GPT-6.1 Sol 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