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Claude Opus 5.5
Send a message to test this OpenAI-compatible chat completions model.
Explore different use cases and parameter configurations
Transparent pricing with no hidden fees. Pay as you go.
| Rule & Modality | Channel | Credits | Price (USD) | Official / Reference Price | Daily Savings |
|---|---|---|---|---|---|
Claude Opus 5.5 regular pricing textAnthropicToken | Regular | Input440.04 Output2,200.22 Cache read22.00 Cache write 5m550.06 Cache write 1h880.09 credits / 1M tokens | Input: $2.0002 Output: $10.001 Cache read: $0.100 Cache write 5m: $2.500 Cache write 1h: $4.0004 per 1M tokens | - | - |
Complete guide to using claude-opus-5.5
Bring frontier-level intelligence to demanding coding, computer-use, and knowledge-work applications with Claude Opus 5.5 on ApiPass. The model emphasizes faster output, clearer communication, and more efficient reasoning across complex, multi-step tasks.
Claude Opus 5.5 is the first model in Anthropic's Claude 5.5 family. It is designed for agentic software engineering, computer use, and knowledge-intensive analysis. Compared with Opus 5, the supplied content describes faster output, improved computational efficiency, and clearer responses. ApiPass model metadata describes Claude Messages API-compatible requests for claude-opus-5-5-1m; check the current API documentation for the supported identifier, endpoint, and request features before integrating.
Apply the model to codebase-wide migrations, audits, debugging, and performance optimization. Its reasoning capabilities are suited to long development tasks that require consistency across multiple steps.
Build agents that interpret interface context and work through multi-step software interactions. Confirm available tools and computer-use features in the ApiPass API documentation.
Connect findings across sources for research, financial analysis, and business reporting, then turn them into structured reports and actionable summaries.
Claude Opus 5.5 is presented as a stronger agentic coding model for extensive codebases, complex debugging, migrations, and optimization. Improved reasoning efficiency can reduce unnecessary work during longer tasks.
The model is designed to handle multi-step digital workflows with better interface-context interpretation, making it useful when building more sophisticated software agents.
Use it to synthesize information across sources and produce structured analysis while staying focused on a complex research or reporting objective.
The supplied content reports output more than 30% faster than Opus 5, alongside more natural responses that surface important information earlier.
Anthropic's evaluations described in the source report improved prompt-injection resistance and a lower tendency to cross operational boundaries. Safeguards still have limitations and should be paired with application-level controls.
Create assistants for debugging, codebase reviews, migrations, and performance investigations, with human review before consequential changes.
Develop workflows that organize findings from multiple sources into analytical reports, concise summaries, and decision-ready recommendations.
Use the model's coding capabilities to prototype playable browser worlds, simulations, and interactive presentations. The supplied content highlights voxel environments and a staged architectural visualization as examples.
Explore JavaScript animation, motion-graphics concepts, and 3D scene ideation by asking the model to plan or generate the code and assets needed for a creative workflow.
The supplied content presents improvements in coding, computer use, output speed, and workload efficiency. Results and costs depend on the task and configuration.
The supplied benchmark table reports 66.4% for Opus 5.5 versus 52.3% for Opus 5 on Terminal-Bench 4.0, and 54.4% versus 48.0% on FrontierCode v1.1 (Main). These are reported evaluation results, not guarantees for individual tasks.
The source reports output more than 30% faster than Opus 5. It also attributes approximately 40% lower costs for typical workloads at default settings to lower token prices and reduced token consumption; actual ApiPass charges depend on the applicable billing rules.
The supplied table reports 81.8% partial versus 74.0% partial on OSWorld 2.0, and scores of 1846 versus 1708 on GDPval-AA v2.1, for Opus 5.5 and Opus 5 respectively.
The supplied page lists 320 credits per 1 million input tokens and 1,600 credits per 1 million output tokens, approximately $1.60 and $8.00 respectively. ApiPass model metadata states that billing uses model_credit_rules by channel_type; check your current ApiPass pricing before relying on these source-page figures.
Count both input and output tokens when planning long conversations, coding tasks, or analytical workflows. The source attributes lower typical workload costs than Opus 5 to both token pricing and reduced token use, but individual results vary.
Sign in to ApiPass, create an API key, keep it secure, and review your available balance before making requests.
Confirm the Claude Messages-compatible endpoint, supported model identifier, request parameters, available features, and channel-specific billing. The supplied page and model metadata show different model identifiers, so use the current ApiPass documentation as the integration authority.
Authenticate with your API key, submit a message using the documented request format, and process the returned response. Start with a coding or analysis prompt, then add supported features as your application requires.
All APIs require authentication via Bearer Token.
Authorization: Bearer
Submit a new Nano Banana 2 image generation or editing task
The API accepts a JSON payload with the following structure:
1{
2 "model": "string",
3 "callBackUrl": "string (optional)",
4 "channel": "auto",
5 "input": {
6 // Input parameters
7 }
8}modelRequiredstringThe model name to use for generation
"google/nano-banana-2"
callBackUrlOptionalstringCallback URL for task completion notifications. If omitted, no callback will be sent.
"https://your-domain.com/api/callback"
channelOptionalstringYou may specify the corresponding provider within APIPASS via the channel parameter; these providers handle the actual image and video generation tasks. APIPASS currently offers three provider options:
The default value for the channel parameter is auto. When enabled, APIPASS automatically allocates tasks across available providers based on real-time pricing and stability metrics to balance minimal cost and reliable performance. Retain the default auto value unless you have custom routing requirements.
Available options:
auto
The input object contains the following parameters:
input.promptRequiredstringA text description of the image you want to generate
Describe the subject, style, lighting, and composition for best results
"A serene alpine lake reflecting snow-capped mountains at golden hour, photorealistic"
input.image_inputOptionalarray(URL)Input images to transform or use as reference. Supports up to 14 images.
Accepted types: image/jpeg, image/png; Max size: 30MB per image; Max files: 14
["https://example.com/reference.jpg"]
input.aspect_ratioOptionalstringAspect ratio of the generated image. Defaults to match_input_image when image_input is provided, otherwise 1:1.
Available options:
"16:9"
input.resolutionOptionalstringResolution of the generated image. Higher resolutions produce more detail but take longer to generate. Default: 1K.
Available options:
"1K"
input.output_formatOptionalstringFormat of the output image. Default: jpg.
Available options:
"jpg"
1curl -X POST "https://api.apipass.dev/api/v1/jobs/createTask" \
2 -H "Content-Type: application/json" \
3 -H "Authorization: Bearer YOUR_API_KEY" \
4 -d '{
5 "model": "google/nano-banana-2",
6 "callBackUrl": "https://your-domain.com/api/callback",
7 "input": {
8 "prompt": "A serene alpine lake reflecting snow-capped mountains at golden hour, photorealistic",
9 "aspect_ratio": "16:9",
10 "resolution": "1K",
11 "google_search": false,
12 "image_search": false,
13 "output_format": "jpg"
14 }
15 }'1{
2 "code": 200,
3 "message": "success",
4 "data": {
5 "taskId": "task_12345678"
6 }
7}codeStatus code, 200 for success, others for failure
messageResponse message, error description when failed
data.taskIdTask ID for querying task status and results
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