> ## 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.

# Claude Opus 4.7 Launch: Better Coding, 3× Vision

> Anthropic's new flagship Claude Opus 4.7 is here — +13% on a 93-task coding benchmark, 3× production tasks on Rakuten-SWE-Bench, new xhigh effort level, ultrareview in Claude Code. APIYI now supports claude-opus-4-7 and claude-opus-4-7-thinking.

## 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 APIYI**: 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

## Background

On April 16, 2026, Anthropic released Claude Opus 4.7, the next step after Opus 4.5 and Opus 4.6. Pricing stays flat, but the model takes a clear step forward on the hardest and longest-running tasks: real-world software engineering, multi-step agents, and high-resolution vision.

This is not a single-benchmark flex. The gains show up precisely where Opus-class models are deployed in production — fixing long-tail bugs, driving agent pipelines, reviewing large PRs, and reasoning over dense screenshots.

APIYI now supports both `claude-opus-4-7` and the extended-thinking variant `claude-opus-4-7-thinking`, via OpenAI-compatible and Anthropic native endpoints. Claude Code users can switch over by simply changing the model name.

## Detailed Analysis

### Core Features

<CardGroup cols={2}>
  <Card title="+13% Coding" icon="code">
    93-task coding benchmark +13% over Opus 4.6, cracking tasks that neither Opus 4.6 nor Sonnet 4.6 could solve
  </Card>

  <Card title="Sturdier Agents" icon="wand-sparkles">
    CursorBench 70% vs 58%, +14% on multi-step workflows, tool errors down to \~1/3
  </Card>

  <Card title="3× Vision" icon="eye">
    Images up to 2,576 px on the long edge — 3× the resolution capacity of previous Claude models
  </Card>

  <Card title="xhigh Effort" icon="sliders-horizontal">
    New effort tier between high and max for finer control over reasoning depth vs latency
  </Card>
</CardGroup>

### Performance Highlights

Opus 4.7's gains concentrate on harder, longer, more realistic tasks:

| Benchmark                          | Claude Opus 4.7      | Claude Opus 4.6 | Delta     |
| ---------------------------------- | -------------------- | --------------- | --------- |
| **93-task coding benchmark**       | +13% over 4.6        | Baseline        | **+13%**  |
| **Rakuten-SWE-Bench (prod tasks)** | \~**3×** of 4.6      | Baseline        | **3×**    |
| **CursorBench**                    | **70%**              | 58%             | **+12pp** |
| **Complex multi-step workflows**   | +14% at fewer tokens | Baseline        | **+14%**  |
| **Tool-use errors**                | \~**1/3** of 4.6     | Baseline        | **-67%**  |

<Info>
  Sources: Anthropic official blog (April 16, 2026) and GitHub Changelog's Opus 4.7 GA post; independent coverage from TechBriefly, Dataconomy, and others.
</Info>

**More reliable engineering work**:

* 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.

**Cheaper, steadier agents**:

* 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.

**Stronger vision**:

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

### Technical Specifications

| Parameter          | Specification                                                          |
| ------------------ | ---------------------------------------------------------------------- |
| **Model IDs**      | `claude-opus-4-7` / `claude-opus-4-7-thinking`                         |
| **Context Length** | 200,000 tokens                                                         |
| **Image Input**    | Up to 2,576 pixels on the long edge                                    |
| **Effort Control** | effort parameter (low / medium / high / **xhigh** / max)               |
| **Thinking Mode**  | `claude-opus-4-7-thinking` runs extended thinking by default           |
| **API Formats**    | OpenAI-compatible / Anthropic native                                   |
| **Availability**   | Anthropic API, AWS Bedrock, Google Vertex AI, Microsoft Foundry, APIYI |

<Warning>
  `claude-opus-4-7` and `claude-opus-4-7-thinking` share the same per-token pricing, but the thinking variant consumes extra output tokens for its reasoning trace. Total spend is typically higher — reach for it when depth really matters.
</Warning>

### 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."

## Practical Applications

### Recommended Use Cases

Claude Opus 4.7 is especially well-suited to:

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

### Code Examples

#### OpenAI Format (standard version)

```python theme={null}
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)
```

#### Anthropic Native Format (thinking version)

```python theme={null}
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)
```

#### Using in Claude Code

Just swap the model name in your Claude Code config:

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

Use `claude-opus-4-7` for everyday coding; switch to `claude-opus-4-7-thinking` before running `/ultrareview` on important PRs or complex refactors.

### 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.

## Pricing & Availability

### Pricing

| Item       | Claude Opus 4.7  | Claude Opus 4.6  | Change |
| ---------- | ---------------- | ---------------- | ------ |
| **Input**  | \$5 / 1M tokens  | \$5 / 1M tokens  | Flat   |
| **Output** | \$25 / 1M tokens | \$25 / 1M tokens | Flat   |

<Info>
  Opus 4.7 holds Opus 4.6 pricing while delivering meaningful gains in coding, agent reliability, and vision — effectively a free performance upgrade.
</Info>

**Competitor Price Reference**:

| Model               | Input   | Output   | Positioning              |
| ------------------- | ------- | -------- | ------------------------ |
| **Claude Opus 4.7** | **\$5** | **\$25** | Top-tier coding / agents |
| Claude Sonnet 4.6   | \$3     | \$15     | Daily workhorse          |
| GPT-5.1-Codex-Max   | \$1.25  | \$10     | Coding competitor        |
| Gemini 3 Pro        | \$2     | \$12     | General-purpose flagship |

### Recharge Bonuses

Stack APIYI's recharge bonuses to lower actual spend further. Details: `docs.apiyi.com/faq/recharge-promotions`.

### Channels

**APIYI Platform**:

* 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`

**Other Channels**:

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

## Summary & Recommendations

Claude Opus 4.7 is a "same price, clearly better" upgrade: +13% on coding, 3× production tasks, 1/3 the tool errors, and 3× the vision capacity, plus new `xhigh` effort, Task Budgets, and `ultrareview`.

**Core Advantages**:

* **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

**Recommendations**:

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`

APIYI now fully supports `claude-opus-4-7` and `claude-opus-4-7-thinking` via both OpenAI-compatible and Anthropic native formats. Plug it into your coding and agent workflows and take the free upgrade.

<Info>
  Sources: Anthropic official announcement (`anthropic.com/claude/opus`), GitHub Changelog (Opus 4.7 GA, 2026-04-16), The Information, TechBriefly, Dataconomy. Data retrieved: April 17, 2026.
</Info>