// OpenAI model comparison

GPT-6 LunavsGPT-6 Sol

Q: Is GPT-6 Sol the same as GPT-6.1 Sol?

No. GPT-6 Sol and the newer [GPT-6.1 Sol](/model/openai/gpt-6.1-sol) have separate model pages and IDs on GPTProto. This comparison is specifically about gpt-6-luna versus gpt-6-sol.

Q: Are both models available through GPTProto?

Yes. The catalog includes [GPT-6 Luna](/model/openai/gpt-6-luna) and [GPT-6 Sol](/model/openai/gpt-6-sol) pages with pricing, API examples, and a Playground. The [OpenAI model catalog](/model/openai) shows the current lineup.

By GPTProtoUpdated October 14, 2026

GPT-6 Luna and GPT-6 Sol share a 1,050,000-token context window, a 128,000-token maximum output, and text-and-image input. They are not interchangeable defaults. OpenAI positions Luna for focused, high-volume work and Sol for complex coding and agentic workflows. On GPTProto, the current displayed rates are $0.08 input / $0.40 output for Luna and $1.60 input / $8.00 output for Sol per million tokens. Check the live pricing panels before budgeting.

The short answer: start with Luna for repeatable tasks with clear acceptance criteria. Trial Sol for multi-file coding, ambiguous reasoning, and agent runs where fewer failed attempts could justify the higher token rate. Choose with a test set and a cost-per-successful-task calculation, not a model-name ranking.

Explore GPT-6 Luna on GPTProto Explore GPT-6 Sol on GPTProto

GPT-6 Luna↗OpenAI · gpt-6-luna

Best forBest first test for classification, structured extraction, grounded answers, and narrow agent steps that need to run often.

Per 1M tokens · base list tier

Input~~$0.10~~$0.08/M−20%

Output~~$0.50~~$0.40/M−20%

View model page

GPT-6 Sol↗OpenAI · gpt-6-sol

Best forBest first test for complex code changes, code review, debugging across files, and multi-step agent work.

Per 1M tokens · base list tier

Input~~$2.00~~$1.60/M−20%

Output~~$10.00~~$8.00/M−20%

View model page

// Side by side

GPT-6 Luna vs GPT-6 SolAt a Glance.

GPTProto rates below are the catalog prices checked on October 13, 2026. They are the prices to use for a GPTProto comparison; review the current pages before budgeting production traffic. Release dates follow the OpenRouter canonical slugs gpt-6-luna-20260922 and gpt-6-sol-20260922.

Spec

GPT-6 Luna

GPT-6 Sol

Provider

OpenAI

Model ID

gpt-6-luna

gpt-6-sol

Availability

94.6%

Last 7 days

97.6%

Last 7 days

Release date

September 22, 2026

Input

$0.08~~$0.10~~ official listPrompts over 272K: $0.16 (list $0.20)

$1.60~~$2.00~~ official listPrompts over 272K: $3.20 (list $4.00)

Output

$0.40~~$0.50~~ official listPrompts over 272K: $0.60 (list $0.75)

$8.00~~$10.00~~ official listPrompts over 272K: $12.00 (list $15.00)

You save

−20%vs OpenAI list price

Cache read

$0.008~~$0.01~~ official list

$0.16~~$0.20~~ official list

Cache write

$0.10~~$0.125~~ official list

$2.00~~$2.50~~ official list

Caching

Supported

Listed price relationship

Baseline

20× Luna for input and output

Latency (p50)

3.33 s

3.53 s

Throughput (p50)

63 tok/s

57 tok/s

Context window

1,050,000 tokens

Max input tokens

922,000 tokens

Max output tokens

128,000 tokens

Thinking

none, low, medium, high, xhigh, max

Accepts

TEXTIMAGE

Returns

TEXT

Built-in capabilities

Web SearchFile Analysis

Tool use

Supported

Stream cancellation

Supported

No prompt training

YesGPTProto Privacy Policy §5.2

Where it earns its keep

Classification, structured extraction, grounded answers, and narrow agent steps with a direct check

Cross-file changes, code review, multi-step debugging, and agent runs that combine several tool results

Best first test

Focused, high-volume tasks

Complex coding and agentic workflows

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": "gpt-6-luna",
    "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-sol",
    "messages": [{"role": "user", "content": "Hi"}]
  }'

Prices per 1M tokens. Official list rates from the GPTProto catalog (raw price / 740). GPTProto rates apply price_multiplication 0.8. The 272K long-context tier is the official price in extend_config, converted with the same multiplier. Latency and benchmarks are OpenRouter figures as of 2026-10-14.

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

OfficialGPTProto

GPT-6 LunaOpenAI

Input$0.10

Input−20%$0.08

Output$0.50

Output−20%$0.40

GPT-6 SolOpenAI

Input$2.00

Input−20%$1.60

Output$10.00

Output−20%$8.00

// Price comparison

The 20× ListedToken-Price Gap.

GPTProto’s displayed rates put Luna at $0.08 input / $0.40 output and Sol at $1.60 input / $8.00 output per million tokens. Sol is 20 times Luna’s listed input and output rate. For an illustrative request with 10,000 uncached input tokens and 1,000 output tokens, those displayed rates imply $0.0012 for Luna or $0.024 for Sol. That calculation covers only those input and output tokens; cached input, cache writes, tools, retries, and other usage can change a complete bill.

A 20× rate gap is significant on repeated calls. But the cheaper call is not automatically the cheaper completed task. If Luna needs many retries or escalations on difficult work, compare the total tokens and accepted outcomes against a Sol run. OpenAI applies higher long-context rates above 272,000 input tokens. The catalog stores those official tiers ($0.20/$0.75 for Luna and $4/$15 for Sol per million) and converts them with the 0.8 multiplier. Confirm GPTProto billing for long prompts before extrapolating from the displayed base rates.

Check the current Luna rate and Sol rate before making a production estimate.

// Savings calculator

Run Your Own Numbers.Not Ours.

Calculate base token costs for both models using the same expected input and output volume. Then add real observations: prompt-caching behavior, retry rate, tool-call cost, and the percentage of tasks each model completes to your standard.

Pricing model

Input tokens per month (millions)

M tokens

Output tokens per month (millions)

M tokens

You keep, per year

$108.00

Monthly run rateOfficialGPTProtoSave

Input$20.00$16.00$4.00

Output$25.00$20.00$5.00

Per year$540.00$432.00$108.00

Excludes top-up bonus credits. Priced at the base tier, not the over-272K long-context tier. Confirm GPTProto billing before you extrapolate a long-prompt budget.

See top-up tiers

// Speed, uptime & access

How Fast. How Reliable.What It Takes To Call It.

OpenAI describes Luna as its efficient option for focused, high-volume work, while Sol targets more complex coding and agents. That positioning is useful, but it is not a measured latency or uptime guarantee for GPTProto. Request length, output length, reasoning effort, tool usage, and live conditions all affect completion time.

Spec

GPT-6 Luna

GPT-6 Sol

Latency (p50)

3.33 s

3.53 s

Throughput (p50)

63 tok/s

57 tok/s

Providers serving it

Usage (last 30 days)

—

Reasoning

OptionalDefault effort is medium; none turns reasoning off

Effort levels

NONELOWMEDIUMHIGHXHIGHMAX

Default effort

Medium

Supported parameters

reasoningseedmax_tokensresponse_formattoolstool_choice

OpenRouter figures as of 2026-10-14. Provider medians are not a GPTProto SLA. Chat Completions function calling is documented by OpenAI only when reasoning effort is none; confirm the live GPTProto route.

// Top-up

Pay Per Token.
One GPTProto Account for Both.

Use one GPTProto account and balance to test both models at their displayed rates. That makes it practical to start routine steps on Luna and selectively test Sol for work that needs more reasoning. Check the selected endpoint’s API Usage panel for available controls.

$15

Straight credit · no bonus

Get started. Great for testing the endpoint and trying new models.

What They Can Do.How to Judge the Results.

Luna fits tasks with a bounded answer: label a support message, extract fields, summarize a retrieved passage, or handle a small code change with a direct test. Sol is the more relevant candidate when an agent needs to plan across files, interpret several tool results, debug a difficult failure, or review a substantial patch. A published benchmark cannot decide your routing rule on its own.

Spec

GPT-6 Luna

GPT-6 Sol

Intelligence Index

38.1

47.6

Vals Index

51.2%

57.5%

Code Migration

42.6%

57.2%

IOI

55.6%

82.6%

ProgramBench

0.5%

2.0%

Design Arena · Website

—

Design Arena · UI components

—

Design Arena · Game dev

—

Artificial Analysis and Vals AI figures as listed on OpenRouter, 2026-10-14. Neither model had a Design Arena rank on that check.

// Verdict

Which Model FitsYour Workload?

Route by the acceptance test, then escalate when that test fails.

GPT-6 Lunagpt-6-luna

Choose Luna for repeatable, well-scoped work

Start with Luna for classification, structured extraction, high-volume chat, and agent subtasks with clear inputs and expected outputs.
Give it explicit categories or a schema, validate its output in your application, and escalate cases that fail a check.
Its lower listed rate has the most leverage when a task repeats at scale.

Test GPT-6 Luna on GPTProto

GPT-6 Solgpt-6-sol

Choose Sol for harder coding and agent runs

Trial Sol on cross-file changes, code review, multi-step debugging, and workflows that combine several tool results before a decision.
Give it relevant files rather than a whole repository by default.
Use your own tests or review rubric to determine whether the extra spend produces a better completed result.

Test GPT-6 Sol on GPTProto

Use both with a measurable escalation rule

Route routine requests to Luna and move a request to Sol after a failed validation, repeated retries, or a clear complexity signal. Compare this mixed workflow with an all-Luna and an all-Sol baseline. Count the cost and latency of the full task, including every escalated attempt.

// Context & multimodal

Context, Output,and Multimodal Support.

Both models publish a 1,050,000-token context window, a 922,000-token maximum input, and a 128,000-token maximum output. They take text and images as input and return text. Matching limits do not mean equal performance on long documents or complex visual references. Retrieve relevant material, preserve room for the answer, and test quality at the prompt lengths you actually use. These are OpenAI model limits, not a guarantee that every GPTProto route accepts a request at the maximum size.

GPTProto also has model-specific Luna file-analysis and Sol file-analysis pages, plus Luna web-search and Sol web-search routes. Check each route’s live request schema before relying on a capability in production.

// How to decide

How to Make a FairLuna vs Sol Decision.

Create a small evaluation set that mirrors production: routine classification, a longer document task, a multi-file code change, and a task your current system often fails. Run both models with comparable prompts, tools, reasoning settings, and acceptance tests. Separate cheap successes from expensive failures. Report cost per accepted result, not just price per million tokens; then decide which tasks stay on Luna and which earn escalation to Sol.

// Get started

Try GPT-6 Luna and Solon GPTProto.

Both model pages include current pricing, API examples, and a Playground. Copy the model ID and route from the relevant page, test the same real workload, and select the default or escalation rule that meets your quality, speed, and budget requirements. GPT-6 Sol is not GPT-6.1 Sol. Browse OpenAI models on GPTProto.

View GPT-6 Luna API and pricing View GPT-6 Sol API and pricing

// FAQ

Luna vs Sol.Straight Answers.

01Is GPT-6 Luna cheaper than GPT-6 Sol on GPTProto?+

Yes. On October 13, 2026, GPTProto displays $0.08 input / $0.40 output per million tokens for Luna and $1.60 input / $8.00 output for Sol. Sol’s listed input and output rates are 20 times Luna’s. Review each live model page for current pricing and any other applicable charges.

02Which model should I use for coding agents?+

Start Luna on narrow steps with direct tests. Trial Sol on cross-file changes, difficult debugging, and multi-step agent work. Use the same tools and acceptance criteria, and compare successful completions, retries, elapsed time, and total spend.

03Do Luna and Sol have different context windows?+

No. OpenAI lists a 1,050,000-token context window, a 922,000-token maximum input, and a 128,000-token maximum output for both. Those limits do not guarantee equal accuracy on long inputs, or that every GPTProto route accepts the maximum size.

04Can both GPT-6 models read images?+

Yes. OpenAI lists text and image inputs and text output for both models. For image creation, choose a dedicated image-generation model instead.

05Can I use tool calling with reasoning on either model?+

OpenAI’s model documentation supports tool calling through its Responses API. For Chat Completions, OpenAI documents function calling only when reasoning effort is set to none. Verify the route and supported controls in GPTProto’s API Usage panel before deploying a tool-using agent.

06Is GPT-6 Sol the same as GPT-6.1 Sol?+

No. GPT-6 Sol and the newer GPT-6.1 Sol have separate model pages and IDs on GPTProto. This comparison is specifically about gpt-6-luna versus gpt-6-sol.

07Are both models available through GPTProto?+

Yes. The catalog includes GPT-6 Luna and GPT-6 Sol pages with pricing, API examples, and a Playground. The OpenAI model catalog shows the current lineup.

// 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

GPT-6 LunavsGPT-6 Sol

GPT-6 Luna vs GPT-6 SolAt a Glance.

The 20× ListedToken-Price Gap.

Run Your Own Numbers.Not Ours.

How Fast. How Reliable.What It Takes To Call It.

Pay Per Token.One GPTProto Account for Both.

What They Can Do.How to Judge the Results.

Which Model FitsYour Workload?

Use both with a measurable escalation rule

Context, Output,and Multimodal Support.

How to Make a FairLuna vs Sol Decision.

Try GPT-6 Luna and Solon GPTProto.

Luna vs Sol.Straight Answers.

See how others use GPTProto.

AI Model Guides.Practical API Tutorials.

GLM 5.3 Flash vs Claude Haiku 5.5: Which Is Better for Coding and Agents?

5 Best AI Image Generation APIs for Developers in 2026: 18 Outputs Tested

ChatGPT Free vs Paid: Which Plan Is Actually Worth It in 2026?

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

Pay Per Token.
One GPTProto Account for Both.