Best forBest first test for classification, structured extraction, support, routing, summaries, and narrow subagent steps.
Per 1M tokens · base list tier
Input$0.10$0.09/M−10%
Output$0.50$0.45/M−10%
// Claude model comparisonView model page
Claude Haiku 5.5vsClaude Sonnet 5.5
Claude Haiku 5.5 and Claude Sonnet 5.5 both offer a 1-million-token context window, up to 128,000 output tokens, text-and-image input, and adaptive thinking. Their roles differ. Anthropic positions Haiku for high-volume, latency-sensitive work such as classification, extraction, and routing. Sonnet is the stronger starting candidate for coding, analysis, and agent workflows that need more sustained judgment. You can test both through the GPTProto API or Playground.
The short answer: start with Haiku 5.5 for frequent, checkable requests. Trial Sonnet 5.5 when coding or multi-step work needs a higher success rate. Pay special attention to prompt length: Anthropic applies a higher direct-API price tier above 100,000 input tokens. Confirm GPTProto billing for long requests rather than assuming the displayed base rate applies at every prompt length.
View model page
Best forBest first test for defined coding tasks, code review, document analysis, and agent steps that need more reasoning.
Per 1M tokens · base list tier
Input$2.00$1.80/M−10%
Output$10.00$9.00/M−10%
// Side by side
Haiku 5.5 vs Sonnet 5.5At a Glance.
The rates below are the base input and output prices displayed from the GPTProto catalog, checked on October 13, 2026. Anthropic publishes a separate prompt-length tier for Haiku above 100,000 input tokens. Confirm the applicable GPTProto billing for long requests; the displayed base price alone does not establish every tier.
Spec
Claude Haiku 5.5
Claude Sonnet 5.5
Provider
Anthropic
Anthropic
Model ID
claude-haiku-5-5
claude-sonnet-5-5
Availability
97.7%
Last 7 days97.7%
Last 7 daysRelease date
October 7, 2026
September 28, 2026
Input
$0.09$0.10 official listPrompts over 100K: $0.45 (list $0.50)
$1.80$2.00 official list
Output
$0.45$0.50 official listPrompts over 100K: $2.25 (list $2.50)
$9.00$10.00 official list
You save
−10%vs Anthropic list price
−10%vs Anthropic list price
Cache read
$0.009$0.01 official list
$0.18$0.20 official list
Cache write
$0.1125$0.125 official list
$2.25$2.50 official list
Caching
Supported
Supported
Prompts over 100K input tokens
Higher upstream tier; confirm GPTProto billing
No equivalent 100K tier in Anthropic’s listed Sonnet pricing
Latency (p50)
1.49 s
1.91 s
Throughput (p50)
130 tok/s
100 tok/s
Comparative latency
Fastest
Fast
Context window
1M tokens
1M tokens
Max output tokens
128K tokens
128K tokens
Thinking
Adaptive, with an adjustable effort setting
Adaptive, with an adjustable effort setting
Accepts
TEXTIMAGE
TEXTIMAGE
Returns
TEXT
TEXT
Built-in capabilities
Web SearchFile Analysis
Web SearchFile Analysis
Tool use
Supported
Supported
Stream cancellation
Supported
Supported
No prompt training
YesGPTProto Privacy Policy §5.2
YesGPTProto Privacy Policy §5.2
Where it earns its keep
High-volume classification, extraction, routing, summaries, support, and subagent tasks
Defined coding tasks, code review, document analysis, and agent steps that need more sustained judgment
Best first test
High-volume, bounded tasks
Coding, analysis, and agent workflows
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": "claude-sonnet-5-5",
"messages": [{"role": "user", "content": "Hi"}]
}'Prices per 1M tokens. Official list rates from Anthropic via the GPTProto catalog (raw price / 740). GPTProto rates apply price_multiplication 0.9. Latency and benchmarks are OpenRouter figures as of 2026-10-14.
// Price comparison
Watch the100,000-Token Threshold.
GPTProto’s catalog lists Haiku 5.5 at $0.09 input and $0.45 output per million tokens, versus Sonnet 5.5 at $1.80 input and $9.00 output. These are displayed base rates; confirm the applicable tier for the prompt size you plan to send. On those displayed rates, Sonnet costs 20 times as much per input or output token. A 10,000-input-token, 1,000-output-token request would cost about $0.00135 on Haiku or $0.027 on Sonnet at those rates, before cache and other charges.
Long prompts need a separate cost check. Anthropic’s direct API changes Haiku’s input/output rates from $0.10/$0.50 to $0.50/$2.50 per million tokens when a request exceeds 100,000 input tokens; the higher tier applies to the full request. The GPTProto catalog stores that official tier and a 0.9 multiplier, which converts to $0.45 input and $2.25 output. Those catalog figures are not a promise about every long-prompt invoice. The threshold concerns each request’s prompt length, not the monthly token total.
Review the current Haiku 5.5 model price, Sonnet 5.5 model price, and GPTProto’s Haiku long-prompt comparison.
// Savings calculator
Run Your Own Numbers.Not Ours.
Estimate costs from your expected input and output tokens, separating requests at or below 100K input tokens from those above it and confirming the applicable GPTProto rates for each group. Add cache use, retries, tool calls, and the number of attempts each model needs to pass your acceptance test.
Pricing model
M tokens
M tokens
You keep, per year
$54.00
Monthly run rateOfficialGPTProtoSave
Input$20.00$18.00$2.00
Output$25.00$22.50$2.50
Per year$540.00$486.00$54.00
Excludes top-up bonus credits. Haiku 5.5 is priced here at its ≤100K-token prompt tier. Assumes flat usage at the published per-1M-token rate.
// Speed, uptime & access
How Fast. How Reliable.What It Takes To Call It.
Anthropic labels Haiku 5.5 Fastest and Sonnet 5.5 Fast relative to its model lineup. Neither label promises a specific GPTProto latency. Prompt length, thinking effort, tool calls, and output size can change the end-to-end time. Test both through the live model pages with the same request and settings.
Spec
Claude Haiku 5.5
Claude Sonnet 5.5
Latency (p50)
1.49 s
1.91 s
Throughput (p50)
130 tok/s
100 tok/s
Providers serving it
5
5
Usage (last 30 days)
—
—
Reasoning
OptionalCan be switched off per request
Always onEffort is adjustable; OpenRouter lists reasoning as mandatory
Effort levels
LOWMEDIUMHIGHXHIGHMAX
LOWMEDIUMHIGHXHIGHMAX
Default effort
Medium
High
Supported parameters
max_tokenstop_ptemperaturestopreasoningtoolstool_choicetop_k
reasoningmax_tokensstoptool_choicetoolsresponse_format
OpenRouter figures as of 2026-10-14. “Fastest” and “Fast” are Anthropic’s relative classifications. Not a GPTProto SLA.
// Top-upPay Per Token.
Pay Per Token.
One Balance for Both Models.
Use one GPTProto account and balance to test Haiku and Sonnet at their displayed rates. A practical application can put repeatable requests on Haiku and reserve Sonnet for tasks that fail validation or require more reasoning. Keep the routing rule visible in your usage data so a cheaper first call does not hide the cost of repeated escalations.
$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 Claude Sonnet 5.5? At GPTProto pricing, that’s roughly $240 saved per year vs official pricing — before bonus credits.
// Quality & benchmarks
What They Can Do.How to Judge the Results.
Haiku 5.5 fits labeling tickets, extracting fields, routing requests, concise summaries, and focused coding subagent work. Sonnet 5.5 is the more relevant candidate for repository changes, code review, longer document analysis, and multi-step agent work. A published score does not mean one model wins every prompt.
Spec
Claude Haiku 5.5
Claude Sonnet 5.5
Intelligence Index
43.4
56
Vals Index
54.4%
67.0%
Code Migration
44.6%
69.8%
IOI
47.3%
83.1%
ProgramBench
2.5%
6.5%
Design Arena · Website
1267 Elo#32 in category
1332 Elo#5 in category
Design Arena · UI components
1308 Elo#15 in category
1379 Elo#3 in category
Design Arena · Game dev
1310 Elo#15 in category
1456 Elo#2 in category
Artificial Analysis, Vals AI, and Design Arena figures as listed on OpenRouter, 2026-10-14.
// Verdict
Which Model FitsYour Workload?
Pick by the check you can run — not by which release is newer.
Claude Haiku 5.5claude-haiku-5-5
Choose Haiku 5.5 for high-volume, checkable steps
- Use Haiku for customer-support triage, extraction into a defined schema, retrieval-grounded short answers, and small subagent jobs with direct checks.
- Its listed lower-tier price and Fastest relative latency make it a strong first candidate when a request repeats many times.
- Track how often work is retried or escalated, and watch for prompts crossing 100K input tokens.
Claude Sonnet 5.5claude-sonnet-5-5
Choose Sonnet 5.5 for defined but harder work
- Use Sonnet for focused code changes, bug fixes, code review, and document analysis that requires more judgment across several pieces of evidence.
- Its higher listed token rate is easier to justify if it produces a correct result with fewer retries or less review time.
- Measure that advantage on your own tasks rather than assuming it.
Use both in an agentic workflow
Give narrow, verifiable subtasks to Haiku and escalate ambiguous or failed steps to Sonnet. Preserve the instructions, evidence, and acceptance test when handing off. Count both calls in the final cost per completed task; a routing strategy only helps if it improves the outcome or the bill.
// Context & multimodal
Context, Output,and Multimodal Support.
Anthropic specifies text and image input, text output, a 1M-token context window, and a standard 128K-token output limit for both models. Thinking tokens count toward the output budget, and the selected GPTProto route may impose its own request limits. Haiku’s upstream 100K pricing threshold means matching context capacity does not imply a flat rate at every prompt length. Split documents into useful evidence only when doing so preserves the facts needed to answer; do not truncate a task solely to fit a cheap tier.
For document workflows, GPTProto has Haiku file-analysis and Sonnet file-analysis pages. It also has model-specific Haiku web-search and Sonnet web-search routes; check the live request schema before deploying.
// How to decide
How to Make a FairHaiku 5.5 vs Sonnet 5.5 Decision.
Build an evaluation set from your actual workload: routine cases, difficult failures, and long prompts on both sides of the 100K threshold. Run both models with comparable instructions, tools, and reasoning settings. Score accepted outcomes, retries, latency, and total tokens. Report the share of requests that need escalation and the resulting cost per completed task. A single average can hide both Haiku’s high-volume advantage and Sonnet’s value on harder work.
// Get started
Test Haiku 5.5 and Sonnet 5.5on GPTProto.
Both model pages include a Playground, API examples, and current pricing. Check the rate for your prompt length, run representative requests, and choose the default—or escalation rule—that meets your quality, speed, and budget requirements. Browse Claude models on GPTProto.
// FAQ
Haiku 5.5 vs Sonnet 5.5.Straight Answers.
01Is Haiku 5.5 cheaper than Sonnet 5.5 on GPTProto?
Yes at the displayed base rates checked on October 13, 2026: Haiku is $0.09 input / $0.45 output and Sonnet is $1.80 / $9.00 per million tokens. Anthropic has a higher direct-API Haiku tier above 100K input tokens. The catalog converts that official tier with the same 0.9 multiplier, but confirm the applicable GPTProto rate and prompt length before budgeting.
02Is Haiku 5.5 faster than Sonnet 5.5?
Anthropic classifies Haiku as Fastest and Sonnet as Fast relative to its other models. Those are not GPTProto latency measurements. On OpenRouter’s provider medians checked October 14, 2026, Haiku’s p50 latency was 1.49 s and Sonnet’s was 1.91 s. Benchmark both with your own prompt lengths, thinking settings, and output sizes.
03Which model is better for coding?
Try Haiku on narrow, checkable coding substeps. Trial Sonnet for defined bug fixes, code review, and changes requiring more reasoning across files. Use tests, retry counts, and cost per accepted patch to decide.
04Do Haiku and Sonnet have different context or output limits?
No. Anthropic lists a 1M-token context window and a standard 128K-token maximum output for both. Haiku’s prompt-length price threshold is a billing distinction, not a smaller context limit.
05Do both support adaptive thinking and image input?
Yes. Anthropic lists adaptive thinking and text-and-image input with text output for both. OpenRouter lists Haiku reasoning as optional and Sonnet reasoning as mandatory, with adjustable effort on both. Image input is for understanding images, not generating them.
06Can I use both through GPTProto?
Yes. The catalog includes Claude Haiku 5.5 and Claude Sonnet 5.5 model pages with pricing, API examples, and a Playground. The Claude catalog shows the current lineup.
// 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
View all posts →AI Model Guides.Practical API Tutorials.
Compare models, learn API setup, and explore image and video workflows for real projects.
// Get started
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