Best forMoonshot's Kimi K3.1 Pro for long-horizon coding, financial research, and visual-to-code work at the lower catalog rate.
Per 1M tokens · catalog rate
Input$2.00$2.00/M−0%
Output$10.00$10.00/M−0%
// Cross-vendor model comparisonView model page
Kimi K3.1 ProvsClaude Opus 5.5
Kimi K3.1 Pro vs Claude Opus 5.5 is a choice between two models aimed at long-running coding and knowledge work. Start with Kimi when you want to evaluate repository-scale changes, financial analysis, or visual-to-code tasks at a lower GPTProto token rate. Keep Opus in the shortlist when the workflow is built around Claude's agent behavior and tool interface. Both accept text and images and produce text. This page compares kimi-k3.1-pro, not the separate high-speed SKU.
The short answer: start the cost check with Kimi K3.1 Pro. On the live GPTProto rates its input is 44% lower and its output is 44% lower. A lower token price helps when the application can check the output and stop extra retries. Keep Opus when an established Claude workflow already passes those checks. Moonshot's launch comparison is a reason to test both, not a decision by itself.
View model page
Best forAnthropic's Claude Opus 5.5 for long-running agentic coding and knowledge work on an existing Claude route.
Per 1M tokens · catalog rate
Input$4.00$3.60/M−10%
Output$20.00$18.00/M−10%
// Side by side
Kimi K3.1 Pro vs Claude Opus 5.5At 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
Kimi K3.1 Pro
Claude Opus 5.5
Provider
MoonshotAI
Claude
Model ID
kimi-k3.1-pro
claude-opus-5-5
Availability
100%
6 days of traffic since Oct 2497.7%
Last 7 daysInput
$2.00$2.00 official list
$3.60$4.00 official list
Output
$10.00$10.00 official list
$18.00$20.00 official list
You save
−0%vs the catalog list rate
−10%vs the catalog list rate
Cache read
$0.20$0.20 official list
$0.18$0.20 official list
Cache write
$2.00$2.00 official list
$4.50$5.00 official list
Context window
1,048,576 tokens
1M tokens
Where it earns its keep
Repository changes and source-backed analysis you can check automatically
Workflows that already pass your checks on Claude's tool interface
Best first test
A coding-agent trial where the token rate is part of the migration decision
A coding agent you are not ready to re-platform
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": "kimi-k3.1-pro",
"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-opus-5-5",
"messages": [{"role": "user", "content": "Hi"}]
}'Prices per 1M tokens from the GPTProto catalog. OpenRouter figures fetched October 20, 2026. Not a GPTProto SLA.
// Price comparison
The lower rateis Kimi's.
On the live GPTProto text rates, Kimi K3.1 Pro is $2.00 per million input tokens and $10.00 per million output tokens. Claude Opus 5.5 is $3.60 and $18.00. The provider list rates stored beside them are $2.00/$10.00 for Kimi K3.1 Pro and $4.00/$20.00 for Claude Opus 5.5. Kimi K3.1 Pro is 44% lower on input and 44% lower on output.
For a batch of 1 million uncached input tokens and 200,000 billed output tokens, those catalog rates come to $4.00 for Kimi K3.1 Pro and $7.20 for Claude Opus 5.5. The input portion is $2.00 versus $3.60. The output portion is $2.00 versus $3.60. Cache operations and separately billed tools are extra. For an agent that rereads a repository and revises a patch, count every attempt required to reach the same acceptance criteria.
Check the live Kimi K3.1 Pro and Claude Opus 5.5 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
$0
Monthly run rateOfficialGPTProtoSave
Input$400$400$0
Output$500$500$0
Per year$10,800$10,800$0
Input and output tokens only, at the catalog rate loaded for this page. Cache, tools, and retries are extra.
// Top-upOne balance.
One balance.
Both models.
Test Kimi K3.1 Pro and Claude Opus 5.5 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 Claude Opus 5.5? At GPTProto pricing, that’s roughly $480 saved per year vs official pricing — before bonus credits.
// Verdict
Which agentpasses your checks?
Use the model whose failure mode you can already describe.
Kimi K3.1 Prokimi-k3.1-pro
Choose Kimi K3.1 Pro for the lower-rate trial
- Use Kimi for a cost-sensitive coding agent, a financial-document pipeline, or a visual-to-code experiment. The listed input rate is 44% below Claude Opus 5.5, and the output rate is lower by 44%.
- Keep reasoning content in the assistant message when you continue a Kimi tool loop. Record the effort setting with the run.
- Keep K3.1 Pro results separate from older Kimi K3 results, and separate from the high-speed SKU.
Claude Opus 5.5claude-opus-5-5
Choose Claude Opus 5.5 when the Claude route already works
- Use Opus when the existing Claude agent passes your acceptance tests, or when its behavior on your long-running tasks justifies the rate.
- Count the migration, not only the tokens: request shape, prompts, tool handling, and maintenance.
- The Terminal-Bench figures above are Moonshot's published comparison. Run your repository tests before you switch the default.
Split the task groups
A document analysis and a multi-file code change can use different defaults on the same GPTProto balance. Increase traffic on the new route only after it meets the acceptance checks you already use.
// Context and output
Check thinkingbefore you migrate.
Kimi K3.1 Pro lists 1,048,576 tokens of context. Moonshot describes that window as shared by input and output, with a configurable output budget inside it. Claude Opus 5.5 lists 1M tokens of context and 128K tokens of output. This page shows Kimi's output limit when the harvest returns one. A large window helps when the prompt is an organized set of requirements, evidence, and recent tool results.
Kimi's reasoning stays enabled, with low, high, and max effort. Opus 5.5 uses always-on adaptive thinking. For Kimi multi-turn tool use, retain the complete assistant message, including reasoning content. For Claude, use the request and tool-response format on the chosen GPTProto route. Do not carry an older request across unchanged.
Moonshot's K3.1 Pro launch comparison reports 65.7% for Kimi and 66.4% for Claude Opus 5.5 on Terminal-Bench 4.0. Keep that benchmark version and the Moonshot attribution attached to those figures. The intelligence row on this page appears only when the harvest has a score for both models.
// How to decide
Same repository.Same tests.
Moonshot positions K3.1 Pro for long-horizon coding and visual implementation. Anthropic positions Opus 5.5 for long-running agentic coding and knowledge work. Give each candidate the same issue, repository snapshot, tool access, and definition of completion. Ask for the implementation and the verification results. Review whether the change preserves interfaces, handles error paths, and avoids unrelated edits. Use the same shell timeout, test command, and retry allowance.
Kimi's release emphasizes financial research and modeling. Use the same filings, spreadsheet extracts, calculation rules, and question for both models. Require traceable sources and reproducible formulas. Opus's knowledge-work scope makes it part of the same trial. Compare the memo and the unresolved questions, and keep a numerical checker on material calculations.
Both models accept images. Kimi's launch demonstrates frontend, CAD, and 3D work through software tools. Identifying a control, producing interface code, and operating an application are different tasks. Run the generated component or script, then inspect layout, geometry, and behavior. Start with Kimi for a cost-sensitive agent you can check automatically. Start with Opus when the Claude workflow already passes, and count request changes, prompt revisions, and tool handling in the migration cost.
// 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. Compare current GPTProto pricing.
// FAQ
Kimi K3.1 Pro vs Opus 5.5.Straight answers.
01Which is cheaper, Kimi K3.1 Pro or Claude Opus 5.5?
On the live GPTProto rates, Kimi K3.1 Pro is $2.00 input and $10.00 output per million tokens, against $3.60 and $18.00 for Claude Opus 5.5. That is 44% less on input and 44% less on output. The provider list rate for Opus is $4.00/$20.00. Track total usage across attempts to compare a completed task.
02What does Kimi K3.1 Pro vs Claude Opus 5.5 show for coding?
Both are positioned for long-running coding agents. Moonshot reports 65.7% for Kimi and 66.4% for Opus on Terminal-Bench 4.0 in its K3.1 Pro launch comparison. Run your repository tests to select a default for your own tasks.
03Do both models support images?
Yes. Both accept text and image inputs and return text. Use the selected route's documented input format, then execute and inspect generated code when the task involves an interface or a 3D asset.
04Can I disable thinking to save tokens?
Kimi K3.1 Pro reasoning stays enabled and can be set to low, high, or max effort. Anthropic lists always-on adaptive thinking for Opus 5.5. Select a supported effort and measure the resulting token usage.
05Is Kimi K3.1 Pro already available for self-hosting?
As of October 27, API access is available and Moonshot schedules the full model weights for release by November 4. Claude Opus 5.5 is a proprietary hosted model. GPTProto provides API access to both.
06Can I keep both models in one application?
Yes. Kimi K3.1 Pro and Claude Opus 5.5 each have a model page with pricing, an API example, and a Playground. Use one GPTProto key and balance, and keep each route's message format, thinking configuration, and tool handling aligned with its API Usage example.
07Are 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.
// 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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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
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