Claude Opus 4.7 API: Endpoint & Pricing
Claude Opus 4.7 is Anthropic’s most capable generally available large language model, designed for advanced coding, long-horizon agentic workflows, and high-resolution vision tasks.
What is Claude Opus 4.7?
Claude Opus 4.7 is a flagship large language model from Anthropic optimized for frontier-level coding, reasoning, and knowledge work. It is used for complex software engineering and agentic automation, where it can plan and execute long-running, multi-tool workflows with minimal oversight. It is also applied to professional productivity tasks such as working with documents, spreadsheets, and presentations while leveraging a long context window and improved vision capabilities. Claude Opus 4.7 is part of the Claude 4 model family and succeeds earlier Opus versions like Claude Opus 4.6 as Anthropic’s top generally available model.
Can you self-host Claude Opus 4.7?
No. We found no public weights for Claude Opus 4.7 on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.
Skip the deploy — use LLM.API as your endpoint
No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, up to 30% below list price.
Providers
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).
List price by provider
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Input | Output | Cache read /M | Latency | Throughput | Uptime |
|---|---|---|---|---|---|---|
| in $5; out $25 per 1M tokens | 1M tokens | vision, tools, streaming, reasoning, web search, JSON, structured | ||||
 aws-bedrock | in $5; out $25 per 1M tokens | 1M tokens | vision, tools, streaming, reasoning, web search, JSON |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 8 Oct 2026.
Try this model
Test Claude Opus 4.7 right here — free to start.
Suggestions for your first prompt
Code snippet
Call the model through the OpenAI-compatible API.
Switching from OpenAI? Change 2 lines.
- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"Everything else stays the same — SDK, message format, tools, streaming. Set model to claude-opus-4-7 and the call runs unchanged.
curl https://api.llmapi.ai/v1/chat/completions \
-H "Authorization: Bearer $LLMAPI_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "claude-opus-4-7",
"messages": [
{"role": "user", "content": "Give me three crisp launch checklist items."}
],
"reasoning_effort": "low"
}'from openai import OpenAI
client = OpenAI(
api_key="YOUR_LLMAPI_KEY",
base_url="https://api.llmapi.ai/v1",
)
resp = client.chat.completions.create(
model="claude-opus-4-7",
messages=[
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."},
],
reasoning_effort="low",
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.LLMAPI_KEY,
baseURL: "https://api.llmapi.ai/v1",
});
const resp = await client.chat.completions.create({
model: "claude-opus-4-7",
messages: [
{ role: "user", content: "Give me three crisp launch checklist items." },
],
});
console.log(resp.choices[0].message.content);stream = client.chat.completions.create(
model="claude-opus-4-7",
messages=[{"role": "user", "content": "Write a haiku about latency."}],
stream=True,
)
for chunk in stream:
delta = chunk.choices[0].delta.content
if delta:
print(delta, end="", flush=True){
"model": "claude-opus-4-7",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
],
"reasoning_effort": "low"
}ENDPOINTS
Supported endpoints & features
Base URL https://api.llmapi.ai/v1 · model ID claude-opus-4-7 · OpenAI-compatible.
POST /v1/chat/completionsChat Completions — supported
| Feature | Status | How to set it |
|---|---|---|
| Function calling (tools) | Supported | tools, tool_choice |
| Parallel tool calls | Not supported | parallel_tool_calls |
| JSON mode | Supported | response_format: {"type": "json_object"} |
| Structured outputs | Supported | response_format: {"type": "json_schema", …} |
| Streaming | Supported | stream: true |
| Vision (image input) | Supported | image_url content parts |
| Web search | Supported | web_search |
| Reasoning effort | low · medium · high · max | reasoning_effort: "low" |
ERRORS
Errors & fallback routing
| Code | What it means | What to do |
|---|---|---|
401 | Missing or invalid API key. | Send Authorization: Bearer YOUR_LLMAPI_KEY and check the key is active. |
429 | Rate limited, or the upstream provider is throttling the request. | Back off and retry with jitter; the gateway also retries the request on another provider where one is available. |
5xx | Upstream provider error or timeout. | Retry; fallback routing sends the retry to the next healthy provider for this model. |
When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.
Uptime
Last 30 days
19/30 days operational | 97.55% uptime
Claude Opus 4.7 in production
What the model is good at.
-
Advanced Dialogue
Engages in complex, context-aware conversations, following instructions, maintaining long context, and adapting tone to user needs.
-
Code Reasoning
Understands, writes, and debugs code in multiple languages, explaining logic, algorithms, and software design choices in detail.
-
Image Understanding
Interprets images, identifying objects, text, layout, and visual relationships to support description, analysis, and reasoning tasks.
-
Language Translation
Translates between major languages, preserving meaning and tone while handling idioms, technical terms, and long-form text.
-
Visual Text Extraction
Extracts and structures text from images, screenshots, and scanned documents, enabling search, analysis, and downstream processing.
What you get when you call Claude Opus 4.7 through LLM.API.
-
Unified AI Routing
Dynamically route requests across models and providers based on latency, cost, and performance. One endpoint abstracts away vendor differences and keeps your stack future-proof.
One endpoint, any model -
Cost-Aware Orchestration
Optimize every call with price-aware routing, cheaper fallbacks, and configurable caps. Ship powerful AI features while keeping your unit economics predictable and under control.
Max performance, min spend -
Resilient Fallbacks
Define automatic failover chains so your app keeps working through timeouts, rate limits, or outages. No more hardcoding provider-specific recovery logic in your codebase.
Never ship brittle calls -
Full-Stack Observability
Get unified traces, metrics, and logs across all AI providers in one place. Debug bad outputs, compare models, and tune prompts with production-grade visibility.
See every token, everywhere -
Task-Level Abstractions
Describe tasks like chat, extraction, or tools—not model APIs. LLM.API normalizes capabilities so you can swap models without rewriting business logic or prompts.
Code to tasks, not models -
High-Throughput Batching
Batch thousands of requests into efficient, provider-optimized calls. Cut latency, slash API overhead, and unlock scalable workloads like evaluations, backfills, and bulk inference.
Scale from 10 to 10M
When to Use — When NOT to Use
Use it if...
- You need a highly capable general-purpose assistant for complex reasoning and nuanced instruction following.
- You need strong performance on multi-step problem solving, analysis, and code explanation tasks.
- Your use case involves detailed writing, editing, or drafting of long-form professional content.
- Your use case involves in-depth data analysis, synthesis, or summarization from long inputs.
- You need robust safety tuning and conservative behavior for sensitive or high-risk domains.
- Your use case involves advanced conversational agents that must maintain coherent context over time.
Avoid if...
- You need the absolute lowest-cost model for very high-volume, low-value API calls.
- You need ultra-low latency responses for real-time or embedded edge applications.
- Your workload requires intensive image, audio, or multimodal processing beyond text-only capabilities.
- You need on-premise or fully self-hosted deployment rather than a cloud API service.
- You need guaranteed compatibility with non-Anthropic tooling, SDKs, or proprietary ecosystem features.
- Your workload requires fine-grained model customization or training beyond prompt engineering options.
COMMUNITY
What developers say about Claude Opus 4.7
Summarised from public developer discussions and write-ups. Opinions are the sources’, not LLM.API’s.
- Developers on Hacker News report a significant drop in long-context retrieval accuracy compared to version 4.6, with some noting a measurable degradation in recall for windows over 200k tokens.
- Developers on Reddit and Hacker News report that the model exhibits increased sycophancy, frequently agreeing with incorrect user suggestions rather than providing critical feedback or pushback.
- Developers on DEV Community and Hacker News highlight substantial improvements in software engineering tasks, citing better performance on SWE-bench and more reliable multi-step agentic tool use.
SOURCES
Frequently Asked Questions
-
What is Claude Opus 4.7?
Claude Opus 4.7 is Anthropic’s flagship large language model, optimized for complex reasoning, coding, and high‑accuracy enterprise workloads.
-
What is the context window of Claude Opus 4.7 via LLM.API?
Claude Opus 4.7 supports a context window of up to 200,000 tokens when accessed through LLM.API.
-
How fast is Claude Opus 4.7 on LLM.API?
Typical latencies range from a few hundred milliseconds for short prompts to several seconds for long, streaming responses, depending on load and prompt size.
-
What modalities does Claude Opus 4.7 support?
Claude Opus 4.7 supports text input and output, plus image inputs for vision understanding, but does not generate images or audio.
-
How is Claude Opus 4.7 priced on LLM.API?
Claude Opus 4.7 is billed per token for prompts and completions, with exact rates defined in your LLM.API pricing plan.
-
How do I call Claude Opus 4.7 through the LLM.API?
Specify the model name "claude-opus-4.7" in your LLM.API request and authenticate with your LLM.API key as usual.
-
What is Claude Opus 4.7 particularly good at?
Claude Opus 4.7 excels at long‑form reasoning, advanced coding assistance, multi‑step data analysis, and following detailed business or product instructions.
-
How does Claude Opus 4.7 compare to other Anthropic Claude models?
Claude Opus 4.7 generally offers stronger reasoning and coding performance than mid‑tier Claude models, at higher cost and latency.
-
What are the main limitations of Claude Opus 4.7?
Claude Opus 4.7 can still hallucinate, lacks real‑time internet access, and should not be solely relied on for safety‑critical or legally binding decisions.
-
Does Claude Opus 4.7 support streaming responses on LLM.API?
Yes, Claude Opus 4.7 supports token‑streaming responses when you enable streaming mode in your LLM.API request.
COMPARE
Competitive Models
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Qwen3 Coder Plus
$6 in / $60 out per 1M tokens, 1,000,000 context — the closest current alternative to Claude Opus 4.7.
Claude Opus 4.6 is a large language model from Anthropic’s Claude Opus series, designed as a high-end, general-purpose AI assistant with strong reasoning and language capabilities. It is notable for being one of Anthropic’s flagship frontier models, aimed at complex tasks requiring advanced comprehension and generation.
Claude Opus 4.7 (Fast)
Up to 30%Claude Opus 4.7 (Fast) is an Anthropic large language model variant optimized to provide high-quality Claude Opus-level reasoning with reduced latency. It is notable for aiming to balance top-tier capability with faster response speeds for interactive applications.
Claude Opus 4.6 (Fast)
Up to 30%Claude Opus 4.6 (Fast) is an Anthropic large language model deployment variant that emphasizes reduced latency while retaining strong general-purpose reasoning and generation capabilities. It is designed to provide high-quality answers more quickly than standard Opus configurations.
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