Claude Opus 4.7 Launch: Better Coding, 3× Vision

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Key Highlights

• Coding Leveled Up: +13% over Opus 4.6 on a 93-task coding benchmark, solving 4 tasks neither Opus 4.6 nor Sonnet 4.6 could crack
• 3× Production Tasks: On Rakuten-SWE-Bench, Opus 4.7 resolves ~3× more production tasks than Opus 4.6, with double-digit gains in code and test quality
• Agentic Workflows: CursorBench 70% (vs Opus 4.6 at 58%), +14% on complex multi-step tasks, and tool-use errors cut to ~1/3
• 3× Vision Capacity: Accepts images up to 2,576 pixels on the long edge — more than 3× the resolution of previous Claude models
• Live on API易: Both claude-opus-4-7 and claude-opus-4-7-thinking available now, priced at $5 / $25 per 1M tokens — same as Opus 4.6

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Background

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Detailed Analysis

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Core Features

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Performance Highlights

• On real repositories and production issues, Opus 4.7 doesn’t just “do more” — it does it more consistently, with double-digit improvements in code quality and test quality.
• It solves hard tasks that previously stumped both Opus 4.6 and Sonnet 4.6.

• Same multi-step workflow, fewer tokens, and roughly 1/3 the tool errors — meaningfully cutting the cost of “retry on failure” loops in production agent systems.

• Long-edge images up to 2,576 pixels fit high-density inputs — architecture diagrams, full UI screenshots, long scrolling captures, dense charts.

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Technical Specifications

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What’s New

• xhigh effort tier: Slots between high and max, ideal for “deeper than high, cheaper than max” work.
• Task Budgets (public beta): API-level budgets for tokens and tool calls, letting you hard-cap long agent runs before they spiral.
• ultrareview (Claude Code): A deep code-review command that flags latent bugs and design issues — designed as the “last gate before shipping.”

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Practical Applications

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Recommended Use Cases

1. Repo-scale coding: cross-file refactors, complex bug hunts, architectural decisions
2. Long-horizon agents: research, coding, and browser agents with many tool calls
3. High-information vision: architecture diagrams, full-screen UIs, long screenshots
4. Critical code review: combine with Claude Code’s ultrareview for important PRs
5. Production workloads that must be stable: services that can’t afford flaky tool calls

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Code Examples

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OpenAI Format (standard version)

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-4-7",
    messages=[
        {
            "role": "user",
            "content": "Review this TypeScript code, flag latent bugs and design issues."
        }
    ],
    extra_body={
        "anthropic_effort": "xhigh"  # low / medium / high / xhigh / max
    }
)

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

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Anthropic Native Format (thinking version)

import anthropic

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

message = client.messages.create(
    model="claude-opus-4-7-thinking",
    max_tokens=8192,
    messages=[
        {
            "role": "user",
            "content": "Here is a production bug in a Rakuten order system — walk through root cause and propose a fix."
        }
    ]
)

print(message.content[0].text)

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Using in Claude Code

{
  "model": "claude-opus-4-7",
  "apiKey": "your-apiyi-key",
  "baseURL": "https://api.apiyi.com/v1"
}

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Best Practices

1. Pick the variant per task:
   • Daily code/refactor/review: claude-opus-4-7 + xhigh effort
   • Hard root-cause analysis / multi-step planning: claude-opus-4-7-thinking
   • Cost-sensitive bulk work: stay on Sonnet 4.6 or use Opus 4.7 at medium
2. Use Task Budgets:
   • Cap tokens and tool calls on long agent runs.
   • Combined with Opus 4.7’s lower tool-error rate, this yields much more stable production agents.
3. Lean on vision:
   • Send high-resolution screenshots and architecture diagrams directly.
   • Long scrolling captures and dense tables are understood more completely than before.
4. Fill the context window:
   • 200K tokens holds full project source + docs for one-shot repo-scale work.

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Pricing & Availability

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Pricing

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Recharge Bonuses

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Channels

• Website: apiyi.com
• OpenAI format: https://api.apiyi.com/v1
• Anthropic native: https://api.apiyi.com
• Model IDs: claude-opus-4-7, claude-opus-4-7-thinking

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

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Summary & Recommendations

• Stronger: +13% on the 93-task coding benchmark, 3× on Rakuten-SWE-Bench
• Steadier: Tool-use errors cut to ~1/3, long agent runs use fewer tokens
• Deeper: xhigh effort plus a thinking variant for high-stakes problems
• Wider: Long-edge image input up to 2,576 pixels

1. Critical coding work: go straight to claude-opus-4-7 + xhigh
2. Production agents: migrate from Opus 4.6 and enable Task Budgets
3. High-value PR reviews: use ultrareview with the thinking variant in Claude Code
4. Bulk / latency-sensitive work: stay on Sonnet 4.6 or Opus 4.7 at medium