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Claude Opus 4.6 API: Endpoint & Pricing

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.

Compare Claude Opus 4.6 alternatives (30) →
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What is Claude Opus 4.6?

Claude Opus 4.6 is a state-of-the-art large language model developed by Anthropic in the Claude Opus family. It is primarily used for sophisticated natural language understanding and generation tasks such as writing, analysis, and complex instruction following across many domains. It is also used for advanced reasoning workflows, including multi-step problem solving, code assistance, and in-depth research support. It follows and extends earlier Claude Opus releases within Anthropic’s Claude model family.

Can you self-host Claude Opus 4.6?

No. We found no public weights for Claude Opus 4.6 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.

Get API keySee API code ↓

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

InputOutput
anthropic$5 in
$25 out
aws-bedrock$5 in
$25 out

Provider list prices; the LLM.API discount applies on top.

Provider Input Output Cache read /M Latency Throughput Uptime
anthropicin $5; out $25 per 1M tokens1M tokensvision, tools, streaming, reasoning, web search, JSON, structured
aws-bedrockin $5; out $25 per 1M tokens1M tokensvision, tools, streaming, reasoning, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 2026.

Try this model

Test Claude Opus 4.6 right here — free to start.

Claude Opus 4.6
Hi! Want to test the model?

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-4-6 and the call runs unchanged.

bash
curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-4-6",
    "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-4-6",
    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-4-6",
  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-4-6",
    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-4-6",
  "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-4-6 · OpenAI-compatible.

  • POST /v1/chat/completionsChat Completions — supported
FeatureStatusHow to set it
Function calling (tools)Supportedtools, tool_choice
Parallel tool callsNot supportedparallel_tool_calls
JSON modeSupportedresponse_format: {"type": "json_object"}
Structured outputsSupportedresponse_format: {"type": "json_schema", …}
StreamingSupportedstream: true
Vision (image input)Supportedimage_url content parts
Web searchSupportedweb_search
Reasoning effortlow · medium · high · maxreasoning_effort: "low"

ERRORS

Errors & fallback routing

CodeWhat it meansWhat to do
401Missing or invalid API key.Send Authorization: Bearer YOUR_LLMAPI_KEY and check the key is active.
429Rate 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.
5xxUpstream 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

30-Day Uptime
97.55%
Past Incidents (30d)
13
Error rate (24h)
8.20%

Last 30 days

19/30 days operational | 97.55% uptime

30 days ago Today
Operational Degraded Outage Maintenance
See All Incidents

Claude Opus 4.6 in production

What the model is good at.

  • Advanced Chatting

    Engages in multi-turn, context-aware conversations, following complex instructions and adapting tone while maintaining coherence over long dialogues.

  • Code and Tools

    Understands and generates code, reasons about software behavior, and coordinates external tools or APIs through structured text instructions.

  • Multilingual Translation

    Translates between major languages, preserving meaning and tone, and handling domain-specific terminology in technical, business, or casual content.

  • Vision Understanding

    Interprets images to identify objects, scenes, text, and relationships, supporting reasoning over visual content alongside text prompts.

  • Text Extraction

    Extracts and structures textual information from images or documents, enabling search, summarization, and downstream analysis workflows.

What you get when you call Claude Opus 4.6 through LLM.API.

  • Smart Model Routing

    Automatically route each request to the optimal model across providers based on cost, latency, and quality, so you ship faster without wiring every vendor by hand.

    One API, every model

  • Cost-Aware Orchestration

    Dynamically balance premium and budget models with per-call controls and global policies, cutting spend while keeping performance high across environments and teams.

    Control cost per call

  • Resilient Fallback Logic

    Define automatic failover chains so requests transparently retry on alternate models or providers, eliminating brittle single-vendor dependencies and avoiding downtime.

    Never lose a request

  • Full-Stack Observability

    Track latency, errors, token usage, and model performance in one place, with request-level traces that make debugging and optimization straightforward.

    See every token spent

  • Task-Level Abstractions

    Describe tasks like chat, tools, or classification once and let LLM.API choose and format the right model calls, decoupling your code from provider quirks.

    Code to tasks, not models

  • High-Throughput Batch

    Submit massive batches of prompts through a single endpoint with automatic chunking, rate handling, and retries, maximizing throughput without custom queueing infrastructure.

    Scale prompts by the million

When to Use — When NOT to Use

Use it if...

  • You need a general-purpose frontier model for complex reasoning, coding, and analysis.
  • You need strong instruction-following and safe outputs for sensitive or regulated domains.
  • Your use case involves multi-step reasoning over long documents or large codebases.
  • You need high-quality natural language generation for drafting reports, briefs, or documentation.
  • Your use case involves iterative problem solving where the model can revise and refine.
  • You need good built-in safety tooling to reduce harmful or policy-violating content.

Avoid if...

  • You need the absolute lowest-cost model for massive high-volume, low-value workloads.
  • Your workload requires extremely low latency responses on constrained edge or mobile devices.
  • You need heavy multimodal capabilities like image, video, or audio understanding and generation.
  • Your workload requires on-premise deployment with strict data residency and offline operation.
  • You need a tiny specialized model fine-tuned for a single narrow task only.
  • You need guaranteed compatibility with proprietary tools or plugins from non-Anthropic ecosystems.

LLM API rating for Claude Opus 4.6

★ 4.3/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · LMArena + Artificial Analysis
5.0Production adoptionTop 1% of production API volume
3.9Benchmark qualityArtificial Analysis Intelligence Index 31.9 · LMArena text rank #6, Elo 1498
3.3Developer ecosystemContext window, tool and JSON support, price against the category median
Reasoning & chat 4.3Coding 4.4Vision & multimodal 4.3Tool & function calling 4.7Long context 4.7
How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

  1. Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
  2. Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
  3. Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

COMMUNITY

What developers say about Claude Opus 4.6

LMArena#6 · Elo 1498Arena coding#3 · Elo 1535Speed—Sources: LMArena, Artificial Analysis

Summarised from public developer discussions and write-ups. Opinions are the sources’, not LLM.API’s.

  • Developers on Reddit report that Opus 4.6 excels at complex reasoning and multi-step architecture planning, significantly surpassing the previous version for autonomous and agentic coding tasks.
  • A significant portion of users report that Opus 4.6 produces flatter and more generic prose than its predecessor, causing controversy among developers who primarily use the model for creative writing.
  • Developers note that the new 1M token context window allows for loading entire codebases, effectively maintaining coherence and outperforming competitors in large-scale retrieval tasks.

SOURCES

Frequently Asked Questions

  • What is Claude Opus 4.6?

    Claude Opus 4.6 is a flagship Anthropic large language model focused on high reasoning quality, complex instruction following, and enterprise-grade reliability.

  • What is Claude Opus 4.6 best suited for?

    Claude Opus 4.6 excels at complex multi-step reasoning, long-form writing, code generation and review, data analysis, and sophisticated agentic workflows.

  • What context window does Claude Opus 4.6 support via LLM.API?

    Claude Opus 4.6 currently supports up to a 200K token context window when accessed through LLM.API.

  • Which modalities does Claude Opus 4.6 support?

    Claude Opus 4.6 supports text input and output only when accessed via LLM.API.

  • How does the pricing for Claude Opus 4.6 work on LLM.API?

    Claude Opus 4.6 is billed per 1,000 tokens for input and output, with exact rates defined in your LLM.API pricing plan.

  • How fast is Claude Opus 4.6 in terms of latency?

    Claude Opus 4.6 generally has higher latency than smaller models but remains suitable for interactive applications with streaming responses enabled.

  • How do I call Claude Opus 4.6 through the LLM.API?

    You select the Claude Opus 4.6 model name in your LLM.API request parameters, using the same unified API schema as other models.

  • How does Claude Opus 4.6 compare to smaller Anthropic models?

    Claude Opus 4.6 typically provides better reasoning and instruction-following quality but is more expensive and slower than smaller Anthropic models.

  • Does Claude Opus 4.6 support tools, function calling, or structured outputs via LLM.API?

    Yes, Claude Opus 4.6 can be used with LLM.API’s structured output and tool-calling mechanisms where supported by your integration.

  • What are the main limitations of Claude Opus 4.6?

    Claude Opus 4.6 can hallucinate, reflect training data biases, and should not be relied on for authoritative legal, medical, or financial advice.

  • Is Claude Opus 4.6 suitable for very large documents and multi-turn workflows?

    Yes, its large context window and strong reasoning make it suitable for long documents and multi-step chains, within token and rate limits.

  • Can I fine-tune Claude Opus 4.6 through LLM.API?

    Direct fine-tuning of Claude Opus 4.6 is not available via LLM.API; use system prompts, examples, and retrieval for customization instead.

COMPARE

Competitive Models

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.

  • Claude Opus 4.7 (Fast)

    Up to 30%
    • Instruction Following

    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 4.7

    Up to 30%
    • Text Generation

    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. It emphasizes stronger multi-step reasoning, reliability on complex work, and improved instruction following compared to earlier Opus releases.

    • Get one key to every model

    Swap your API key. Keep your code.

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