Best forAnthropic's small Haiku 5.5 for classification, extraction, and short agent steps you can grade.
Per 1M tokens · catalog rate
Input$0.10$0.09/M−10%
Output$0.50$0.45/M−10%
// Cross-vendor model comparisonView model page
Claude Haiku 5.5vsGPT-6 Luna
Claude Haiku 5.5 and GPT-6 Luna are the small models public writeups compared through October 2026. Artificial Analysis, DataCamp, and hands-on tests all put them side by side, and they do not agree on a winner: some favor Haiku on agent tasks, some favor Luna on cost per finished job. Vendor list cards are often quoted at the same low rate. On GPTProto the live text rates are not the same. This page uses that catalog.
The short answer: start the cost check with GPT-6 Luna. 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. Do not promote either model on a single benchmark. The public comparisons split, and a small rate gap disappears if the losing model needs a second try.
View model page
Best forOpenAI's small GPT-6 Luna for high-volume calls where the GPTProto token rate is the constraint.
Per 1M tokens · catalog rate
Input$0.10$0.08/M−20%
Output$0.50$0.40/M−20%
// Side by side
Claude Haiku 5.5 vs GPT-6 LunaAt 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 Haiku 5.5
GPT-6 Luna
Provider
Claude
OpenAI
Model ID
claude-haiku-5-5
gpt-6-luna
OpenRouter model
anthropic/claude-haiku-5.5
openai/gpt-6-luna
Availability
99.10%
Last 7 days100%
Last 7 daysRelease date
October 7, 2026
September 22, 2026
Input
$0.09$0.10 official list
$0.08$0.10 official list
Output
$0.45$0.50 official list
$0.40$0.50 official list
You save
−10%vs the catalog list rate
−20%vs the catalog list rate
Cache read
$0.009$0.01 official list
$0.008$0.01 official list
Cache write
$0.1125$0.125 official list
$0.10$0.125 official list
Latency (p50)
1.78 s
2.74 s
Throughput (p50)
149 tok/s
65 tok/s
Context window
1M tokens
1.05M tokens
Max output tokens
128K tokens
128K tokens
Accepts
TextImageFile
FileImageText
Returns
Text
Text
Where it earns its keep
Short agent and tool steps where a small model has to stay inside a checkable task
High-volume chat and tool steps that finish inside a checkable result
Best first test
A routed sub-step: classify, extract, or draft, then grade it
A repeated short call where the lower catalog rate is the point
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": "claude-haiku-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-luna",
"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
A small rate gap,on the catalog you will actually call.
On the live GPTProto text rates, GPT-6 Luna is $0.08 per million input tokens and $0.40 per million output tokens. Claude Haiku 5.5 is $0.09 and $0.45. The provider list rates stored beside them are $0.10/$0.50 for GPT-6 Luna and $0.10/$0.50 for Claude Haiku 5.5. GPT-6 Luna is 11% lower on input and 11% lower on output.
For a request with 10,000 uncached input tokens and 1,000 output tokens, those GPTProto rates come to about $0.0012 for GPT-6 Luna and $0.0014 for Claude Haiku 5.5. That illustration is input plus output only. At these rates the dollar gap on one short call is small. It matters when the call repeats, and it vanishes when a retry or a tool loop spends the difference.
Vendor price cards can step up once a prompt passes a length threshold. This comparison does not apply a second tier. It shows the one text-to-text rate in the GPTProto catalog. Open the model page before you budget a prompt long enough to leave that rate.
Check the live Claude Haiku 5.5 and GPT-6 Luna 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
$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
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
Claude Haiku 5.5
GPT-6 Luna
Latency (p50)
1.78 s
2.74 s
Throughput (p50)
149 tok/s
65 tok/s
Providers measured
5
3
Supported parameters
include_reasoningmax_completion_tokensmax_tokensreasoningreasoning_effortresponse_formatstopstructured_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 Claude Haiku 5.5 and GPT-6 Luna 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 Luna? At GPTProto pricing, that’s roughly $24.00 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
Claude Haiku 5.5
GPT-6 Luna
Intelligence Index
36.2Claude Haiku 5.5 (Max)
27.8GPT-6 Luna (Max)
Artificial Analysis scores as carried by the OpenRouter harvest, fetched November 3, 2026.
// Verdict
Which small modelshould take the step?
Use the model whose failure mode you can already describe.
Claude Haiku 5.5claude-haiku-5-5
Choose Haiku 5.5 for the graded agent step
- Use Haiku for classification, extraction, and short tool steps you can check automatically. Public writeups give it the edge on some agent tasks and not on others.
- Do not treat one intelligence index as the decision. The table shows a score only when the harvest has it for both models, and the cell note names the configuration.
- The latency figure on this page is an OpenRouter provider median, not a promise about your route. Time the call you will actually make.
GPT-6 Lunagpt-6-luna
Choose GPT-6 Luna when the call repeats
- Use Luna when the same short call runs many times and the token rate is the constraint. The listed input rate is 11% below Claude Haiku 5.5, and the output rate is lower by 11%.
- A lower token rate is not a lower cost per completed job until you count retries. Run the same set you use for Haiku.
- The context windows are in the same million-token class. Do not pick either model on window size alone, and do not assume the catalog rate still applies once a prompt crosses the vendor's long-prompt threshold. Check the model page.
Route the step, then look at the log
Send the repeated short calls to the model with the lower GPTProto rate, and keep Haiku on the agent steps your grader already prefers. Move a step across only when the log shows the other model succeeding where the first one did not. Both sit on one GPTProto balance.
// Context and output
Same class of window.Not a reason to pick.
GPT-6 Luna lists the larger context window: Claude Haiku 5.5 at 1M tokens, GPT-6 Luna at 1.05M tokens. Both sit in the million-token class. For a small model, that headroom is not a reason to fill every call. You still pay the input rate for every token you send.
Maximum output is 128K tokens for Claude Haiku 5.5 and 128K tokens for GPT-6 Luna. These models are usually called for a short result. Test the output length you will actually keep.
Claude Haiku 5.5 median latency is 1.78 s. GPT-6 Luna is 2.74 s. A user waiting on a short answer should weight latency. These figures are medians of OpenRouter provider p50s, not a GPTProto service level.
Claude Haiku 5.5 leads on throughput: Claude Haiku 5.5 at 149 tok/s, GPT-6 Luna at 65 tok/s. A long completion should weight throughput. A one-line classification usually should not.
// How to decide
Count the finished job,not the tenth of a cent.
Hands-on tests published in October 2026 found these two close on short jobs and apart on which task each one misses. Run your own mix as two sets: a high-volume classification or extraction set, and a short agent set with tools. Grade whether the result was usable, how many tries it took, and the time to the last token. Cost per successful job is the number to keep. A lower token rate 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.
// FAQ
Haiku 5.5 vs GPT-6 Luna.Straight answers.
01Is GPT-6 Luna cheaper than Claude Haiku 5.5 on GPTProto?
On the live GPTProto rates, yes. Luna is $0.08 input and $0.40 output per million tokens, against $0.09 and $0.45 for Haiku. That is 11% less on input and 11% less on output. The provider list rates in the catalog are $0.10/$0.50 and $0.10/$0.50. A retry, a tool loop, or a long-prompt tier on the model page can erase that gap.
02Which small model scores higher?
This page does not award that. October 2026 writeups, including Artificial Analysis and hands-on job sets, split by task and by effort setting. 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?
Not exactly. The table lists each model's published context and maximum output from the OpenRouter snapshot. Both are in the million-token class. 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. Claude Haiku 5.5 and GPT-6 Luna 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.
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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