LANGUAGE MODEL Feb 2026 Anthropic Last updated: Apr 29, 2026

Claude Opus 4.7

Claude Opus 4.7 is an API model from Anthropic. It’s positioned for tool use and multi-step workflows and vision + text tasks—work that benefits from iteration, not just one-shot answers. Opus 4.7 是 Anthropic 在 2026 年初发布的旗舰级模型之一，主打长上下文与“能推进任务”的智能体式工作流：把目标拆成步骤、调用工具、反复检查再继续。它更像是给工程与知识工作者用的执行引擎，而不是只做一次性问答的聊天模型。

Try Claude API Docs ↗

Intelligence

Top 5%

Speed

Slow

55 tok/s output

Cost

High

$6.25 in / $25.00 out

Context

Up to 1,000,000 tokens

How are Intelligence, Speed & Cost bucketed?

Intelligence and Speed buckets are percentile ranks on Artificial Analysis. Cost buckets are fixed dollar thresholds keyed off output-token price ($/M out).

Intelligence

Top 1%≤ 1%
Top 5%≤ 5%
Top 10%≤ 10%
Good≤ 25%
Medium≤ 50%
Below avg> 50%

Speed

Top 1%≥ 345 tok/s
Top 5%≥ 237 tok/s
Top 10%≥ 196 tok/s
Good≥ 146 tok/s
Medium≥ 90 tok/s
Slow< 90 tok/s

Cost

Freeopen weights · self-host
Low< $1 / M out
Moderate$1–5 / M out
High≥ $5 / M out

Official ↗ Artificial Analysis ↗

Why it matters

Opus 4.7 is the current state of the art for sustained autonomous AI — and the model whose deployment in coding agents is reshaping engineering team economics most visibly in 2026. Q1 2026 BLS data shows year-over-year contraction in entry-level US software engineering hiring that maps onto the Opus 4.x deployment timeline.

Opus 4.7 是当下持续自主 AI 的 state of the art，也是 2026 年最明显在重塑工程团队经济学的那款模型。美国劳工统计局 2026 Q1 数据显示入门级软件工程岗位同比在萎缩，时间线刚好对上 Opus 4.x 的规模部署——有人叫好，也有 36氪那种「Anthropic 把模型越带越笨」的反面报道，争议在继续。

Core Capabilities

Long Documents

Handles entire codebases, books, and multi-doc RAG.

Multimodal

Combines text, vision, and audio in one model.

Generative

Produces images, video, audio, or other media.

Agent Workflows

Built for tool use and autonomous tasks.

Context Window

1M tokens

≈ entire codebase

4k Chat 聊天

32k Long docs 长文档

128k Books 整本书

400k Multi-doc 多文档

1M This model 本模型

10M

Availability

API

Available

Product / App

Available

Open Source

Not released

Enterprise

Available

Pricing Model

Pay per token

Input and output billed separately.

Pay-per-token

Capability / Performance

Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).

Lower 20% Upper 80% This model

Reasoning

AA Intelligence Index · scaled to 10

1.7

5.6

8.2

Coding

SciCode · scaled to 10

1.8

4.3

5.5

Agentic tasks

Terminal-Bench Hard · scaled to 10

0.2

3.6

5.5

Context / memory

Context window size · log-scaled

6.0

9.0

Cost efficiency

Input price ($/M tokens) · cheaper scores higher

6.2

10.0

3.2

Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.

What it feels like

Successor refresh of Opus 4.5; same agentic-coding strengths.
Best-in-class on real-world coding — 80.9% on SWE-bench Verified, ahead of Gemini 3 Pro and GPT-5.1
Reasoning index of 70 on Artificial Analysis — biggest single-jump from any prior Anthropic model
Testers say it 'just gets it' on multi-system bugs without hand-holding