Claude Opus 4.7

The April 2026 Opus flagship - top-tier coding and vision, now superseded by Opus 4.8.

Context window

Input / 1M tokens

$5.00

Output / 1M tokens

$25.00

Provider

Anthropic

Rate card unchanged from Opus 4.6 at $5/$25 per 1M input/output tokens, but Opus 4.7 shipped a new tokenizer that can produce roughly 1.0-1.35x more tokens for the same text, so real per-request cost can rise. Up to 90% savings with prompt caching, 50% with batch. Now a legacy model - migrate to Opus 4.8. · Data verified 2026-07-02

Claude Opus 4.7 (April 2026) advanced agentic coding and added substantially better vision, processing images up to ~3.75 megapixels (over 3x prior Claude models). It scored 87.6% on SWE-bench Verified and introduced adaptive thinking only (no manual thinking budgets) plus a new tokenizer. It is now a legacy model, with Opus 4.8 as the direct successor.

Capability index

Relative estimates (0-100) to place this model against its peers, grounded in published benchmarks.

Coding

Reasoning

Math

Multimodal

Long context

Speed

Cost efficiency

How to access it

Available across all Claude products and the Claude API, Amazon Bedrock, Google Cloud (Vertex AI), and Microsoft Foundry. Now categorized as a legacy model; Opus 4.8 is the recommended upgrade.

Get access →Documentation →

Strengths

✓Top-tier agentic coding: 87.6% SWE-bench Verified
✓Much-improved high-resolution vision (up to ~3.75 MP images)
✓1M-token context with 128k max output
✓Solved coding tasks neither Opus 4.6 nor Sonnet 4.6 could
✓Adaptive thinking with effort control

Best for developers who...

Vision-heavy agentic and document-understanding workloadsTop-tier coding on established Opus 4.7 pipelinesLarge-codebase engineering with 1M contextComplex reasoning and professional knowledge workMulti-step agent tasks needing strong tool use

When to choose it (and when not to)

Reach for Claude Opus 4.7 when...

→You have a pipeline pinned to Opus 4.7 and need stability
→Your workload is vision-heavy and benefits from higher-resolution image input
→You need top-tier coding with 1M context but haven't migrated to 4.8 yet
→You want documented, broadly available Opus-class behavior

Look elsewhere if...

✕You want the best current Opus - 4.8 improves coding, honesty, and long-context handling
✕You are cost-sensitive - note the tokenizer can inflate real per-request cost
✕You need manual extended-thinking budgets or custom sampling params (400 error)

How to use it

›Recount token budgets - the tokenizer can yield 1.0-1.35x more tokens than earlier Opus models
›Use adaptive thinking with the effort parameter; manual budgets return 400
›Feed higher-resolution images to exploit the improved vision pipeline
›Remove non-default temperature/top_p/top_k (returns 400)

Quickstart

Python

import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=8000,
    thinking={"type": "adaptive"},
    output_config={"effort": "high"},
    messages=[
        {"role": "user", "content": "Review this diagram and generate the API spec."}
    ],
)
print(response.content[0].text)

Legacy model - for new work migrate to claude-opus-4-8. Manual thinking budgets and non-default sampling params return 400.

API model id: claude-opus-4-7