Claude Opus 4.8 Launches: Stronger Coding, More Honest, Same Price

Documentation Index

Fetch the complete documentation index at: https://docs.apiyi.com/llms.txt

Use this file to discover all available pages before exploring further.

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Key Takeaways

• Coding evolves again: Agentic coding benchmark rises from Opus 4.7’s 64.3% to 69.2%; multidisciplinary reasoning with tools climbs from 54.7% to 57.9%
• More honest & reliable: About 4x less likely to overlook code flaws than its predecessor, more likely to flag uncertainty and less likely to make unsupported claims
• Same price, no markup: API易 has launched claude-opus-4-8 at $5 input / $25 output per million tokens — identical to Opus 4.7
• Dynamic workflows (research preview): Orchestrate hundreds of parallel subagents, handling codebase-scale migrations across hundreds of thousands of lines
• Faster & cheaper: Fast mode runs 2.5x faster, regular usage costs less than before, and it works independently for longer on long tasks

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Background

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Deep Dive

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Core Features

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Performance Highlights

• Stronger agentic coding, leading contemporaneous competitors on several key benchmarks.
• About 4x less likely to overlook code flaws — well suited as the final gate before shipping.

• More likely to surface “I’m not sure” rather than offer plausible-but-baseless answers.
• Lower deception rate than Opus 4.7; alignment assessments show higher prosocial traits.

• Fast mode runs 2.5x faster, and regular usage costs less than the prior mechanism.
• Works independently for longer than its predecessors, reducing manual intervention.

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Technical Specifications

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What’s New

• Dynamic Workflows (research preview): Let Claude orchestrate hundreds of subagents in parallel, tackling large-scale jobs such as codebase migrations spanning hundreds of thousands of lines.
• Effort control: Adjust how much “effort” the model puts into a single response directly in Claude.ai and Cowork, trading off depth vs. speed.
• Mid-task instructions in Messages API: System entries can now be inserted within the message array, allowing instructions to be updated mid-task without restarting the session.

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Practical Applications

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Recommended Scenarios

1. Large repo-scale coding: cross-file refactors, complex bug fixes, architecture decisions
2. Codebase-scale migrations: parallelize hundreds of thousands of lines via dynamic workflows
3. Critical code review: 4x lower chance of missing flaws — ideal for gating important PRs
4. Long-horizon agent tasks: research/code/browser agents that run longer with sharper judgement
5. High-trust knowledge work: financial analysis and domains that need honest uncertainty flagging

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Code Examples

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OpenAI-Compatible Format

from openai import OpenAI

client = OpenAI(
    api_key="your-apiyi-key",
    base_url="https://api.apiyi.com/v1"
)

response = client.chat.completions.create(
    model="claude-opus-4-8",
    messages=[
        {
            "role": "user",
            "content": "Review this TypeScript code and point out potential bugs and design improvements."
        }
    ],
    extra_body={
        "anthropic_effort": "xhigh"  # low / medium / high / xhigh / max
    }
)

print(response.choices[0].message.content)

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Anthropic-Native Format

import anthropic

client = anthropic.Anthropic(
    api_key="your-apiyi-key",
    base_url="https://api.apiyi.com"
)

message = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=8192,
    messages=[
        {
            "role": "user",
            "content": "Walk through the root cause of this production bug step by step and propose a fix."
        }
    ]
)

print(message.content[0].text)

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Using in Claude Code

{
  "model": "claude-opus-4-8",
  "apiKey": "your-apiyi-key",
  "baseURL": "https://api.apiyi.com/v1"
}

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Best Practices

1. Pick by task:
   • Everyday code/refactor/review: claude-opus-4-8 + xhigh effort
   • Complex root-cause / multi-step planning: raise effort to max
   • Cost-sensitive batch jobs: use medium effort or the Sonnet line
2. Leverage dynamic workflows:
   • For large migrations and cross-repo bulk changes, let the model orchestrate subagents in parallel.
3. Lean on honesty:
   • In financial analysis and compliance review, let the model flag uncertainty to lower the cost of misjudgement.
4. Use the full context:
   • 200K tokens covers full project source + docs for repo-scale operations in one pass.

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Pricing & Availability

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Pricing

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Stack with Recharge Promotions

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Where to Get It

• Website: apiyi.com
• OpenAI format: https://api.apiyi.com/v1
• Anthropic-native format: https://api.apiyi.com
• Model name: claude-opus-4-8

• Anthropic official API
• AWS Bedrock
• Google Cloud Vertex AI
• Microsoft Foundry

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Summary & Recommendations

• Stronger: agentic coding 69.2%, multidisciplinary reasoning 57.9%
• Steadier: only ~1/4 the chance of missing code flaws vs. the prior model
• More honest: flags uncertainty, lower deception than 4.7
• No markup: priced identically to Opus 4.7 ($5/$25)

1. Critical coding tasks: choose claude-opus-4-8 with xhigh effort
2. Codebase-scale migrations: enable dynamic workflows for parallel processing
3. High-value PR review: use /ultrareview in Claude Code
4. Cost-sensitive batch calls: use medium effort or the Sonnet line