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Claude Opus 4.7

anthropic/claude-opus-4-7

Opus 4.7 — sharper agentic runs than 4.6, same price

Claude Opus 4.7 improves on 4.6 where long tool-using runs tend to drift: it keeps its plan straighter over many steps. Same 12 credits per 1K input and the same 1M context, so there is little reason to stay on 4.6 for new work.

Modalities
-25%
Input / Output
8.25 / 42cr / 1K11 / 56
Context window
1M
Max output
128K
Capabilities
  • Tools
  • Vision
  • PDF
  • Streaming
  • Prompt caching· read 1.2 cr / 1K

What this model does well

  • 📚

    1M token context

    5× larger than Opus 4.6 — swallow whole codebases or quarterly reports in one prompt

  • 🧠

    Deep reasoning

    Multi-step problems, ambiguous specs, contested trade-offs

  • 🤖

    Agentic by design

    Plans, calls tools, recovers from errors across long horizons

  • 💻

    Elite coding

    Refactors, debugs and reviews at senior-engineer quality

  • 👁️

    Vision + documents

    Reads images and PDFs natively in the same call

  • 🗓️

    Fresh knowledge

    Knowledge current to January 2026

Example prompts

  • Review this 800-line service file and propose three refactors that would simplify it without changing behavior

  • I'm pasting our entire codebase below. Find every place we mutate user state outside a transaction and rank by severity.

Technical specifications

Author
anthropic
API identifier
claude-opus-4-7
Category
Chat & Text
Context window
1,000,000 tokens
Max output
128,000 tokens
Rate limit
150 RPM
Billing unit
token
Streaming
Yes

Ideal for

  • Agents that run unattended
  • Whole-codebase migrations
  • Multi-document due diligence
  • Building and checking a plan before acting
  • Reviews where an error is costly

Ready to try it?

Test this model in the playground, no setup needed.

Frequently asked questions

Can I pay with mobile money, without a bank card?

Yes. Top up your Tchavi account with Orange Money, MTN MoMo, M-Pesa or another mobile money operator. No bank card is needed. Credits land as soon as the payment is confirmed and are deducted as you use them.

Is the API compatible with OpenAI's?

Yes. Keep your current OpenAI SDK, just change the base URL to Tchavi's (shown in the API tab) and use your Tchavi key. The rest of your code stays the same.

API

import Tchavi from '@tchavi/sdk';

const client = new Tchavi({ apiKey: 'API_KEY' });

const response = await client.chat.completions.create({
  model: 'claude-opus-4-7',
  messages: [
  {
    "role": "user",
    "content": "Hello!"
  }
],
});

console.log(response.choices[0].message.content);
console.log('Credits used:', response.tchavi.credits_used);

API parameters

Every parameter this model accepts on the API. Check the SDK for full typings.

modelRequired
string

ID of the chat model (e.g. `claude-opus-4-7`).

messagesRequired
Array<{ role, content }>

Conversation messages. Each item has a `role` (system/user/assistant) and a `content` that's either a plain string or an array of content blocks (`text`, `image_url`, `document_url`).

temperature
number
Default: 1

Sampling temperature between 0 and 2. Higher values = more random.

max_tokens
integer

Maximum number of tokens to generate in the response.

stream
boolean
Default: false

If true, partial message deltas are streamed as Server-Sent Events.

tools
Array<{ type, function }>

Function-calling toolbox. Pass an array of `{ type: "function", function: { name, description, parameters } }`. Returned tool calls land in `choices[].message.tool_calls`. Only models with `supportsTools: true` accept this.

tool_choice
"auto" | "none" | { type, function }

How the model picks among tools: `"auto"` (default), `"none"` to force a plain text reply, or `{ type: "function", function: { name } }` to force a specific tool. Only with `supportsTools: true`.

Show all parameters (14)
top_p
number
Default: 1

Nucleus sampling: only tokens in the top `top_p` probability mass are considered.

stop
string | string[]

Up to 4 sequences where the API will stop generating further tokens.

frequency_penalty
number
Default: 0

Between -2.0 and 2.0. Positive values penalize tokens that have appeared frequently.

presence_penalty
number
Default: 0

Between -2.0 and 2.0. Positive values encourage the model to talk about new topics.

messages[].content[].image_url
string

Attach an image on a user message. Pass a content block `{ "type": "image_url", "image_url": { "url": ... } }`. PNG / JPEG / WebP, up to 4 per request. Only models with `supportsImageInput: true` accept this block (e.g. `gpt-4o`, `claude-opus-4-7`).

messages[].content[].document_url
string

Attach a PDF on a user message. Pass a content block `{ "type": "document_url", "document_url": { "url": ... } }`. Up to 5 PDFs per request. Only models with `supportsDocuments: true` accept this block (e.g. `claude-sonnet-4-6`, `gpt-4.1`).

effort
"low" | "medium" | "high"
Default: "high"

Reasoning depth on Anthropic adaptive-thinking models (Opus 4.7+ / Sonnet 4.6+). Values `"low"`, `"medium"`, `"high"`. Higher = more deliberate, slower. Defaults to `"high"` on Opus 4.8.

Pricing

-25%The discount applies to every price for this model.
per 1,000 tokens
8.25 credits
Input 11 · Output 56 cr/1K

How billing works

  • Billed per token used, down to a thousandth of a credit.
  • A typical message (500 in, 150 out) costs about —, or 10.425 credits.
  • Prompt cache reads: — per 1K tokens (0.9 credits).
Real-world scenarios
Per day
Per week
Per month

Estimated over 30 days, for a typical message: ~500 input + ~150 output tokens, billed to the thousandth of a credit.

A 10 000 F CFA top-up gives you about ~2 877 questions. Pay with Orange Money, MTN MoMo or M-Pesa — no Visa card needed.

Ready to use Claude Opus 4.7?

Create your accountAPI Documentation

Bonus credits on your first top-up · No card to sign up