Claude Opus 4.8

Claude Opus 4. Ready to experience the power of AI? Start your journey here!

Platform: Replicate

Reasoning AICoding AssistantAdaptive ThinkingAgent Workflows

0 runs

Input: $5 per million tokens; Output: $25 per million tokens

Commercial

🚀Function Overview

A high-capability Opus model upgrade with adaptive effort control, longer-context operational stability, and improved long-horizon coding behavior.

Key Features

Same Opus-tier model positioning as Anthropic flagship with 1M context and 128k max output on most surfaces
Adaptive thinking with effort control (high default) for demand-sensitive reasoning
Fewer wasted thinking tokens on simple tasks plus stronger tool triggering behavior
Long-horizon agentic coding support, including better compaction handling
Fast mode on Claude API is available as research preview for higher throughput

Use Cases

•Complex engineering tasks with high autonomy requirements
•Long-horizon code refactors and repository modernization
•Tool-heavy coding workflows where missed tool calls are costly
•High-stakes production assistant loops requiring better compaction stability
•Comparative frontier-model routing where speed/quality tradeoffs are controlled by effort

⚙️Input Parameters

prompt

string

The prompt to send to the model

image

string

Optional input image; images are priced as (width px * height px)/750 input tokens

system_prompt

string

System prompt for policy, constraints, and instruction hierarchy

max_tokens

integer

Maximum number of output tokens to generate

effort

string

Controls reasoning effort for adaptive thinking. The Opus 4.8 default is high.

speed

string

Optional output mode such as `fast` on supported Claude API surfaces.

max_image_resolution

number

Optional image-resolution cap (megapixels) to reduce image token consumption

💡Usage Examples

Example 1

Input Parameters

{
  "prompt": "Refactor this authentication module to support multi-tenant role delegation and add defensive validation paths.",
  "system_prompt": "You are a senior engineer. Preserve backward compatibility and provide migration-safe changes.",
  "effort": "high",
  "speed": "fast",
  "max_tokens": 4096,
  "max_image_resolution": 0.6
}

Output Results

I’d treat this as a staged migration across five surfaces: type modeling, authorization guardrail, session cache, error handling, and test coverage.

Step 1: create a role resolution adapter that keeps existing tokens valid while adding tenant-scoped assertions.

Step 2: normalize auth claims so downstream service contracts do not break during rollout.

Step 3: add explicit audit events for authorization failures and scope escalation.

Step 4: update regression tests for denied access, admin override, and delegated access edge cases.

Step 5: deploy behind a feature flag with observability checks and rollback triggers tied to auth latency and authorization error-rate.

Quick Actions

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Technical Specifications

Hardware Type
Run Count: 0
Commercial Use: Supported
Pricing: Input: $5 per million tokens; Output: $25 per million tokens
Platform: Replicate

Related Keywords

Coding AgentClaude OpusAdaptive ThinkingLong-horizon CodingEffort ControlsPrompt CachingAI Model API

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