Claude Opus 4.7 is an Anthropic model available through Merge Gateway. Use it with Gateway routing policies, spend controls, request logs, and a 1,000,000 token context window. It supports streaming, structured outputs, tool calling, vision through at least one Gateway vendor route.
Claude Opus 4.7 performance*
Intelligence - general reasoning and knowledge
57%
Coding - code generation and problem-solving
53%
Agentic - multi-step task completion with tools
33%
*Performance data is provided by Artificial Analysis and is subject to change.
Claude Opus 4.7 pricing
| Vendor | Input / 1M tokens | Output / 1M tokens | Zero data retention |
| --- | ---: | ---: | --- |
| Anthropic | $5.00 | $25.00 | No |
Test Claude Opus 4.7 with Merge Gateway’s Simulator
Claude Opus 4.7
Synced
Synced
Run simulation to see response
—
—
—
Ready to try it out?
Start routing requests to hundreds of large language models in your product within minutes.
Route requests to Claude Opus 4.7 with Merge Gateway
Merge Gateway is a unified LLM API that lets your product route requests to Claude Opus 4.7 and every other major model through a single endpoint. You get built-in fallback routing, per-request cost tracking, zero data retention support, and observability without changing your application architecture.
To get started in seconds, add our Gateway Implementation skill to your project, or pick your preferred SDK below. Check out our other quick start skills here.
Install the Merge Gateway SDK
Python
1$ pip install merge-gateway-sdkSend a request
Python
1from merge_gateway import MergeGateway
2
3client = MergeGateway(api_key="YOUR_API_KEY")
4
5response = client.responses.create(
6 model="openai/gpt-5.2",
7 input=[
8 {"type": "message", "role": "system", "content": "You are a helpful programming tutor. Explain the concepts clearly with practical examples."},
9 {"type": "message", "role": "user", "content": "Explain the concept of recursion in programming with a simple set of examples."},
10 ],
11)
12
13print(response.output[0].content[0].text)Try a diffrent model
Swap the model string to route to a different provider. No other code changes needed.
Anthropic
1response = client.responses.create(
2 model="anthropic/claude-sonnet-4-20250514",
3 input=[
4 {"type": "message", "role": "system", "content": "You are a helpful programming tutor. Explain the concepts clearly with practical examples."},
5 {"type": "message", "role": "user", "content": "Explain the concept of recursion in programming with a simple set of examples."},
6 ],
7)Point to Gateway
Python
1from openai import OpenAI
2
3client = OpenAI(
4 api_key="YOUR_API_KEY",
5 base_url="https://api-gateway.merge.dev/v1/openai",
6)Send a request
Use the standard chat.completions.create method. No provider prefix needed on the model name.
Python
1response = client.chat.completions.create(
2 model="gpt-5.2",
3 messages=[
4 {"role": "system", "content": "You are a helpful programming tutor. Explain the concepts clearly with practical examples."},
5 {"role": "user", "content": "Explain the concept of recursion in programming with a simple set of examples."},
6 ],
7)
8
9print(response.choices[0].message.content)Install packages
1npm install merge-gateway-ai-sdk-provider aiCreate the provider
TypeScript
1import { createMergeGateway } from "merge-gateway-ai-sdk-provider";
2
3const gateway = createMergeGateway({
4 apiKey: "YOUR_API_KEY",
5});Send a request
Use generateText to send a request. Model names use the provider/model format.
TypeScript
1import { generateText } from "ai";
2
3const { text } = await generateText({
4 model: gateway("openai/gpt-4o"),
5 prompt: "Explain the concept of recursion in programming with a simple set of examples.",
6});
7
8console.log(text);If you already have @ai-sdk/openai installed, point it at Gateway with a base URL change:
TypeScript
1import { createOpenAI } from "@ai-sdk/openai";
2
3const gateway = createOpenAI({
4 apiKey: "YOUR_API_KEY",
5 baseURL: "https://api-gateway.merge.dev/v1/ai-sdk",
6});
7
8// All generateText/streamText calls work unchangedInstall the Merge Gateway SDK
Anthropic SDK
1from anthropic import Anthropic
2
3client = Anthropic(
4 api_key="YOUR_API_KEY",
5 base_url="https://api-gateway.merge.dev/v1/anthropic",
6)
7
8message = client.messages.create(
9 model="claude-sonnet-4-20250514",
10 max_tokens=1024,
11 messages=[
12 {"role": "user", "content": "Explain the concept of recursion in programming with a simple set of examples."},
13 ],
14)
15
16print(message.content[0].text)Explore other models available in Merge Gateway
Claude Opus 4.7 FAQ
If you have any other questions about Claude Opus 4.7, we've covered a few more below. This information was written in June, 2026 and is subject to change.
Heading
What other models does Anthropic offer?
Anthropic's lineup spans three tiers designed to address different tradeoffs between cost, speed, and intelligence. Here are some other models Anthropic supports:
- Claude Haiku 4.5: Claude Haiku 4.5 is Anthropic's fastest and most affordable model at $1.00 input and $5.00 output per million tokens. It is built for high-throughput tasks where intelligence depth is not the primary requirement
- Claude Sonnet 4.6: Claude Sonnet 4.6 is Anthropic's mid-tier model at $3.00 input and $15.00 output per million tokens. It delivers strong benchmark performance with a 1M token context window and is the recommended default for most production use cases
- Claude Opus 4.6: Claude Opus 4.6 is a non-reasoning Opus variant ranked #2 out of 71 evaluated non-reasoning models. It provides near-flagship intelligence without adaptive thinking traces and at a faster time to first token than Opus 4.7
- Claude Opus 4.8: Claude Opus 4.8 is Anthropic's current flagship model, ranked #1 on the Artificial Analysis Intelligence Index across all 150+ evaluated models. It uses extended thinking with adaptive reasoning at max effort, producing the highest benchmark scores available from Anthropic at $5.00 input and $25.00 output per million tokens
How does Claude Opus 4.7 differ from Anthropic's other models?
Claude Opus 4.7 occupies the high-capability tier of Anthropic's active lineup, positioned between Claude Opus 4.6 and Claude Opus 4.8.
- Intelligence ranking: Claude Opus 4.7 scores 57 on the Artificial Analysis Intelligence Index, placing it #4 out of 150 evaluated models. This is above Claude Sonnet 4.6 at score 44 and below Claude Opus 4.8 at score 61
- Pricing: Claude Opus 4.7 is priced at $5.00 per million input tokens and $25.00 per million output tokens, matching Claude Opus 4.8. This is 67% higher input cost than Claude Sonnet 4.6 and five times the input cost of Claude Haiku 4.5
- Reasoning type: Claude Opus 4.7 uses adaptive thinking without extended thinking traces. Claude Opus 4.8 adds extended thinking with max effort reasoning, which contributes to its higher intelligence score but also to longer latency. Opus 4.7 is faster to first token than Opus 4.8 as a result
- Speed and latency: Output speed is 55.4 tokens per second with a time to first token of 17.74 seconds. The high TTFT reflects the model's adaptive reasoning step, making it unsuitable for interactive or streaming interfaces
- Output verbosity: Claude Opus 4.7 generated 110 million output tokens during Artificial Analysis evaluation, compared to a median of 35 million. At $25.00 per million output tokens, this verbosity has significant cost implications at scale
- Context window: Supports 1,000,000 input tokens and up to 128,000 output tokens, with extended output available via the Message Batches API
Claude Opus 4.7 is the right choice when you need high-capability adaptive reasoning with faster time to first token than Opus 4.8, and when the task does not specifically require Opus 4.8's extended thinking traces or maximum-effort reasoning.
What models should I consider using alongside Claude Opus 4.7?
No single model is optimal for every task. Here are models worth pairing with Claude Opus 4.7 depending on what your product needs:
- Claude Sonnet 4.6: Route the majority of your traffic to Sonnet 4.6 at $3.00 input per million tokens and escalate to Opus 4.7 only for requests that demonstrably require it. On mixed workloads, this split can reduce Anthropic costs by 40% or more
- Claude Opus 4.8: When a task requires Anthropic's maximum intelligence tier with extended reasoning traces, route to Opus 4.8. Its score of 61 versus Opus 4.7's 57 is meaningful for ceiling-limited tasks. Accept the additional latency in exchange for peak output quality
- Claude Haiku 4.5: For high-volume classification, extraction, or simple generation tasks, Haiku 4.5 at $1.00 input handles those workloads at one-fifth the cost of Opus 4.7
- GPT-5.4: For cross-provider diversity at a similar capability tier, GPT-5.4 provides an OpenAI alternative with comparable intelligence scores and a 1M+ token context window
- Gemini 3.1 Pro: For workloads where Google's reasoning model is preferred or where Anthropic availability is a concern, Gemini 3.1 Pro competes at the frontier intelligence tier and provides a strong failover option
What are the challenges of using Claude Opus 4.7 in my product?
Like any production LLM, Claude Opus 4.7 comes with tradeoffs worth planning for:
- High latency: A time to first token of 17.74 seconds makes Claude Opus 4.7 incompatible with any interactive, streaming, or real-time interface. This is a hard architectural constraint driven by the model's adaptive thinking step
- Extreme output verbosity: Generating 110 million output tokens during evaluation, versus a median of 35 million, means that at $25.00 per million output tokens, production costs can significantly exceed what input-only pricing suggests
- Cost at the same tier as Opus 4.8: Claude Opus 4.7 is priced identically to Claude Opus 4.8 at $5.00 input and $25.00 output per million tokens, but scores lower on the Intelligence Index. Teams evaluating Opus 4.7 should validate whether Opus 4.8 delivers better output for their specific tasks at the same price
- Provider dependency: Running exclusively on Anthropic creates fragility when the provider experiences outages, rate limits, or model deprecations. Anthropic has iterated quickly across Claude 4 versions
- Cost at scale: At $5.00 input and $25.00 output per million tokens, combined with high output verbosity, Opus 4.7 is one of the more expensive models to run at volume. Active routing controls and budget caps are essential
Why should I use Merge Gateway to route LLM requests with Claude Opus 4.7 and every other model?
Using Claude Opus 4.7 through Merge Gateway gives you access to the model itself and the infrastructure layer around it:
- One API, every provider: Access Claude Opus 4.7 and every other major LLM through a single endpoint and API key. Change providers by swapping the model string, with no application code changes required
- Intelligent routing and automatic failover: Merge routes around Anthropic outages automatically. Given Opus 4.7's identical pricing to Opus 4.8, routing policies can dynamically select between them based on task complexity and latency requirements without code changes
- Cost governance: Set hard or soft project budgets so Claude Opus 4.7 spend stays within plan. Given its output verbosity, hard limits are especially important. Every request is attributed to a model, project, and tag in a unified billing dashboard
- Build Your Own Router: Define the threshold at which Opus 4.7's adaptive thinking is worth the cost versus Sonnet 4.6 by scoring models against your own benchmark weights or eval data. Every routing decision includes a plain-language explanation of why a model was chosen
- Security and compliance controls: Apply DLP rules and prompt injection protection before every request reaches Anthropic. Enforce per-project model and region policies without adding that logic to your application
How can I start using Merge Gateway to route requests with Claude Opus 4.7?
Getting Claude Opus 4.7 running through Merge Gateway takes a few minutes:
1. Create an account and get your API key from the dashboard.
2. Install the Merge Gateway SDK: run pip install merge-gateway-sdk (Python) or npm install merge-gateway-sdk (Node). Alternatively, if you're already using the OpenAI SDK, set base_url = "https://api-gateway.merge.dev/v1/openai" and your existing code works as-is.
3. Make your first request using the provider/model format. For Claude Opus 4.7, the model string is anthropic/claude-opus-4-7. Swap the model string to route to any other provider without changing anything else.
4. Configure a routing policy in the dashboard to set failover behavior, cost limits, and optimization strategy. A recommended starting point: use Claude Sonnet 4.6 as the default and escalate to Opus 4.7 for tasks that exceed a complexity threshold you define, with Opus 4.8 as a further escalation tier.
Full setup instructions and SDK references are in the Merge Gateway docs.
Try Claude Opus 4.7 through Merge Gateway
Route, observe, and control AI requests across providers from one API.
