Claude Opus 4.7

Name: Claude Opus 4.7
Brand: Anthropic
SKU: claude-opus-4-7
Price: 6 USD
Availability: InStock

MultimodalLegacyAvailable

by Anthropicclaude-opus-4-7

Anthropic's April 2026 flagship. 87.6% on SWE-bench Verified, 3x higher image resolution, output self-verification, vision + reasoning.

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Price · 1M in / out: $6.00 / $30.00
Context: 1,000,000 tokens
Max. output: 128,000 tokens
Input → output: Text + Image → Text
Developer: Anthropic
Updated: September 23, 2026

This is an older version of the model.

Newer version available: Claude Opus 5.5

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Try Claude Opus 4.7

Chat

$6.00/1M in

Send a message. The answer arrives in full once the model is done (no streaming).

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Max. answer length (tokens)

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This run

at most $0.0308 · 3.08 credits reserved

Billed by the tokens actually used; the unused part of the reservation is refunded.

For accounts without a purchase: runs above 2 credits need a top-up.

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10 free credits ($0.10) when you sign up with Google

Usable 24 hours after sign-up, up to 5 runs per day and at most 2 credits per run. Other sign-in methods start without credits.

About Claude Opus 4.7

TL;DRAs of September 23, 2026

Claude Opus 4.7 is a model by Anthropic in the Multimodal category. On Railwail, Claude Opus 4.7 costs $6.00 per 1M input tokens and $30.00 per 1M output tokens. The context window holds 1,000,000 tokens, and one response can be up to 128,000 tokens long. Newer version: Claude Opus 5.5.

Released April 16, 2026 as Anthropic's most capable frontier model. Claude Opus 4.7 keeps the Opus tier pricing unchanged while delivering 87.6% SWE-bench Verified, native output self-verification, and 3x higher image resolution understanding. Extended thinking, multi-hour autonomous coding sessions, native computer use, and parallel tool calling. Recommended for complex agentic engineering, regulated-industry copilots, long-horizon autonomous workflows.

Background

About Anthropic

Founded 2021 · San Francisco, USA

Anthropic was founded in 2021 by Dario Amodei (CEO) and Daniela Amodei (President) together with senior researchers Tom Brown, Sam McCandlish, Jared Kaplan and others who left OpenAI. The company is structured as a Public Benefit Corporation focused on AI safety and the deployment of reliable, interpretable, steerable AI systems. Anthropic's foundational research includes Constitutional AI (2022), Sparse Autoencoders for interpretability, and influential scaling laws work. The Claude model family launched in March 2023, followed by Claude 2 (2023), Claude 3 (March 2024), Claude 3.5 Sonnet (June 2024), Claude 3.7 Sonnet with extended thinking (February 2025), the Claude 4 family (May 2025), Claude 4.5 (late 2025), Claude Sonnet 4.6 (February 2026) and Claude Opus 4.7 (April 2026). Investors include Google ($3B+), Amazon (committed up to $8B), Spark Capital and Lightspeed; total funding exceeds $20 billion with a 2026 valuation above $150 billion. Anthropic publishes Responsible Scaling Policies and is a leading voice on ASL safety levels.

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Architecture

Decoder-only Transformer (frontier reasoning model with self-verification)

Claude Opus 4.7 is Anthropic's flagship frontier model released April 16, 2026. It builds on the Opus 4 lineage with three headline upgrades: native output self-verification (the model spends test-time compute to check its own answers before returning them), 3x higher vision input resolution, and an upgraded tokenizer (which can produce up to 35% more tokens for the same input text). Architecturally it remains a decoder-only Transformer optimized for agentic engineering and long-horizon tasks. Pretraining used a multi-trillion-token mixture of curated web text, code repositories, books, scientific papers and licensed data with a knowledge cutoff in early 2026. Post-training combined RLHF, Constitutional AI (CAI), RLAIF, and large-scale reinforcement learning against verifiable rewards on coding, math and tool-use trajectories. The model retains the two operating modes from Opus 4: standard fast responses and extended thinking mode with adjustable thinking budget. Native computer use (screenshots, mouse and keyboard control), parallel tool calling, and stable multi-hour autonomous agent loops are first-class behaviours. Vision input was jointly trained with text and now supports denser document, diagram and screen reading. The model shipped under Anthropic's Responsible Scaling Policy ASL-3 safeguards with constitutional classifiers, external evaluations and a public Bug Bounty programme.

Parameters: Undisclosed (estimated multi-hundred billion parameters dense, hybrid sparse layers)
Context: 200,000 tokens

Capabilities

87.6% on SWE-bench Verified at launch, top of the agentic coding leaderboard
Native output self-verification: the model checks its own answers before returning
3x higher vision input resolution for dense documents and screenshots
Extended thinking mode with adjustable thinking budget for hard reasoning
Sustained multi-hour autonomous coding sessions with stable tool-use loops
Native computer use: screenshots, mouse and keyboard control of a virtual desktop
200K token context window with strong needle-in-haystack recall
Multimodal input: text, images, PDFs, charts and diagrams in a single prompt
Parallel tool calling and structured JSON / XML output
Constitutional-AI alignment with low refusal rate on benign questions
Multilingual fluency across English, German, French, Spanish, Japanese, Chinese and more
Best for: complex coding agents, regulated-industry copilots, autonomous workflows, long-document analysis.

Training & license

Pretrained on a curated multi-trillion-token mixture of publicly available internet text, licensed third-party data, code from public repositories and human-generated conversations. Anthropic does not disclose exact token counts; the corpus is heavily filtered for quality, deduplication and copyright. Knowledge cutoff is early 2026. Post-training uses RLHF, Constitutional AI, RLAIF, and reinforcement learning against verifiable rewards on coding, math and agent trajectories.

License: Proprietary commercial license via Anthropic API, Amazon Bedrock and Google Vertex AI. Commercial use permitted under Anthropic's Usage Policy.

Safety testing: Deployed under Responsible Scaling Policy ASL-3 safeguards with constitutional classifiers, automated red-teaming, third-party evaluations by METR and Apollo Research, and a public Bug Bounty programme.

Known limitations

No native audio or video input (vision images only)
New tokenizer produces up to 35% more tokens, so real bills per request rose despite unchanged rate card
Higher latency and cost than Sonnet/Haiku tiers
Will refuse some legitimate red-team and security research prompts
No fine-tuning available to external customers

Research papers

Pricing

Prices in US dollars. Usage is charged from prepaid credits.

Input	$6.00 / 1M tokens
Output	$30.00 / 1M tokens

Billed by the tokens each request actually uses.
1 credit = $0.01

Cost calculator

Price calculator

Input tokens

/ req.

Output tokens

/ req.

Requests

Total

$2.10

210 credits

Per request

$0.021 · 2.1 credits

Each request is rounded up to 0.01 credits.

API

Call Claude Opus 4.7 with your Railwail API key. Use this model ID in the request:

claude-opus-4-7API documentation Get an API key

curl https://railwail.com/api/v1/chat/completions \
  -H "Authorization: Bearer $RAILWAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-4-7",
    "messages": [
      {
        "role": "user",
        "content": "Explain what a vector database is in two sentences."
      }
    ],
    "max_tokens": 1024
  }'

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["RAILWAIL_API_KEY"],
    base_url="https://railwail.com/api/v1",
)

completion = client.chat.completions.create(
    model="claude-opus-4-7",
    messages=[
        {
            "role": "user",
            "content": "Explain what a vector database is in two sentences.",
        },
    ],
    max_tokens=1024,
)

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

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.RAILWAIL_API_KEY,
  baseURL: "https://railwail.com/api/v1",
});

const completion = await client.chat.completions.create({
  model: "claude-opus-4-7",
  messages: [
    {
      role: "user",
      content: "Explain what a vector database is in two sentences."
    }
  ],
  max_tokens: 1024
});

console.log(completion.choices[0].message.content);

// npm install railwail
import railwail from "railwail";

const rw = railwail(process.env.RAILWAIL_API_KEY);

const res = await rw.chat("claude-opus-4-7", [
  { role: "user", content: "Explain what a vector database is in two sentences." },
], { max_tokens: 1024 });

console.log(res.choices[0].message.content);

Set your key as RAILWAIL_API_KEYCreate API key

Specifications

Model ID: claude-opus-4-7
Developer: Anthropic
Category: Multimodal
Input: Text, Image
Output: Text
Context window: 1,000,000 tokens
Max. output: 128,000 tokens
Billing: By usage (tokens or GPU time)
Released: April 16, 2026
Lifecycle: Legacy
Model size: Undisclosed (estimated multi-hundred billion parameters dense, hybrid sparse layers)
License: Proprietary commercial license via Anthropic API, Amazon Bedrock and Google Vertex AI. Commercial use permitted under Anthropic's Usage Policy.
Catalog entry updated: September 23, 2026

Input parameters

Inputs and settings from the model's input schema. The example in the API section shows which of them the API accepts.

ParameterTypeDefaultAllowed values

promptrequired
User message
Type: Text
Default: –
Allowed values: up to 32,000 characters
top_p
Type: Number
Default: 1
Allowed values: 0 to 1
stream
Type: Yes/no
Default: false
Allowed values: –
image_url
Optional image URL to analyze
Type: Text
Default: –
Allowed values: –
max_tokens
Type: Integer
Default: 4096
Allowed values: 1 to 32,000
temperature
Type: Number
Default: 1
Allowed values: 0 to 1
system_prompt
Optional system instruction
Type: Text
Default: –
Allowed values: up to 8,000 characters

Use cases

What it is used for

Autonomous software engineering agents
Long-document legal and financial analysis
Scientific research synthesis
Regulated-industry customer support copilots
Computer-use automation
Complex multi-step planning
Dense document and screenshot understanding

All models in Multimodal

Frequently asked questions

What is Claude Opus 4.7?

Claude Opus 4.7 is a model by Anthropic in the Multimodal category. On Railwail you can call it with an API key through the Railwail API.

How much does Claude Opus 4.7 cost on Railwail?

On Railwail, Claude Opus 4.7 costs $6.00 per 1M input tokens and $30.00 per 1M output tokens. You are charged for what each request actually uses. Usage is paid from prepaid credits; 1 credit equals $0.01.

What is the context window of Claude Opus 4.7?

The context window of Claude Opus 4.7 holds 1,000,000 tokens. One response can be up to 128,000 tokens long.

How fast is Claude Opus 4.7?

There are not enough measured runs of Claude Opus 4.7 on Railwail yet to state a run time. It depends on the input, the settings and the load at the provider.

Is Claude Opus 4.7 better than Claude Opus 5.5?

That depends on the task. Claude Opus 4.7 (Anthropic) and Claude Opus 5.5 (Anthropic) are both models in the Multimodal category. The comparison page shows their prices and specifications side by side.

Compare Claude Opus 4.7 and Claude Opus 5.5

Can Claude Opus 4.7 process images?

Yes. Claude Opus 4.7 accepts images as input in addition to text.

How do I use Claude Opus 4.7 through the API?

Create a Railwail API key and send your request with the model ID claude-opus-4-7. Code examples for curl, Python and JavaScript are in the API section of this page.

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Compare Claude Opus 4.7 vs. Claude Sonnet 4.6
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Use Claude Opus 4.7 via the API

One API key for every model on Railwail. Usage is charged from prepaid credits, 1 credit = $0.01.

Get API key Browse all models