AnthropicMediumUltra

Claude Opus 4.8

Anthropic's most capable Opus-tier model, with a 1M-token context window (200K on some surfaces), 128K max output tokens, and knowledge cutoff to January 2026. Builds on Opus 4.7 with stronger long-horizon agentic coding, better tool triggering, and adaptive thinking that reasons only when a turn needs it. Compatible with the Anthropic Messages and OpenAI Chat Completions formats.

9,290/46,450credits

input / output · per 1M tokens

Most capable Anthropic model

1M token context window

128K max output tokens

Knowledge cutoff: January 2026

Adaptive thinking (effort-controlled)

Vision and tool use

Streaming support

Run it right now

Test this model instantly in the Console Playground — no code required

Use with AI Assistant

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Model Specifications

Context Window

tokens

Max Output

128K

tokens

Training Cutoff

2026-01

Compatible SDK

Anthropic, OpenAI

Capabilities

Vision

Function Calling

Streaming

JSON Mode

System Prompt

Token Pricing (per 1M tokens)

Token Type	Credits	USD Equivalent
Input Tokens	9,290	$6.19
Output Tokens	46,450	$30.97

* 1,500 credits ≈ $1 (actual charges may vary based on usage)

Quick Start

curl -X POST "https://api.core.today/llm/anthropic/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer cdt_your_api_key" \
  -d '{
  "model": "claude-opus-4-8",
  "messages": [
    {
      "role": "system",
      "content": "You are a principal research analyst. Provide rigorous, source-aware analysis."
    },
    {
      "role": "user",
      "content": "Compare the safety and capability trade-offs of frontier closed-weight models versus open-weight alternatives for enterprise deployment in 2026, with mitigations."
    }
  ],
  "max_tokens": 8000
}'

Parameters

Parameter	Type	Required	Default	Description
`messages`	array	Yes	-	Array of message objects (OpenAI format)
`max_tokens`	integer	No	4096	Maximum tokens in response (up to 128000)
`stream`	boolean	No	false	Enable Server-Sent Events streaming

Examples

Long-context Analysis

Deep multi-step analysis over large documents with Claude Opus 4.8

curl -X POST "https://api.core.today/llm/anthropic/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer cdt_your_api_key" \
  -d '{
  "model": "claude-opus-4-8",
  "messages": [
    {
      "role": "system",
      "content": "You are a principal research analyst. Provide rigorous, source-aware analysis."
    },
    {
      "role": "user",
      "content": "Compare the safety and capability trade-offs of frontier closed-weight models versus open-weight alternatives for enterprise deployment in 2026, with mitigations."
    }
  ],
  "max_tokens": 8000
}'

Production Code Generation

Generate production-ready code with full reasoning

curl -X POST "https://api.core.today/llm/anthropic/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer cdt_your_api_key" \
  -d '{
  "model": "claude-opus-4-8",
  "messages": [
    {
      "role": "system",
      "content": "You are a senior software engineer. Write clean, well-tested, production code."
    },
    {
      "role": "user",
      "content": "Implement a distributed rate limiter in Go using Redis Cluster with the leaky bucket algorithm. Include unit tests."
    }
  ],
  "max_tokens": 6000
}'

Tips & Best Practices

1Most capable Anthropic model — pick this for the hardest reasoning and long-horizon agentic tasks

21M context window handles very large codebases and document sets in a single request

3128K output enables long-form drafts in a single response

4Sampling params (`temperature`, `top_p`, `top_k`) are unsupported — omit them; a non-default value returns a 400

5Thinking is adaptive-only: use `thinking: {type: "adaptive"}` and control depth with the `effort` parameter (defaults to `high`); fixed `budget_tokens` returns a 400

6Streaming recommended for long responses

7Compatible with both Anthropic Messages and OpenAI SDK formats