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Claude Opus 4.8 API: Endpoint & Pricing

Claude Opus 4.8 is a large language model from Anthropic’s Claude family, designed for high-level reasoning, detailed writing assistance, and complex problem solving.

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What is Claude Opus 4.8?

Claude Opus 4.8 is an Anthropic large language model focused on advanced natural language understanding and generation. It is used for tasks such as drafting and editing complex documents, answering technical or domain-specific questions, and supporting research and analysis workflows. It also assists with creative writing, brainstorming, and conversational applications where nuanced, context-aware responses are important. Claude Opus 4.8 belongs to Anthropic’s Claude model family, succeeding earlier Claude generations and related Opus variants.

Can you self-host Claude Opus 4.8?

No. We found no public weights for Claude Opus 4.8 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, web search, JSON

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

Try this model

Test Claude Opus 4.8 right here — free to start.

Claude Opus 4.8
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-4-8 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-4-8",
    "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-4-8",
    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-4-8",
  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-4-8",
    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-4-8",
  "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-4-8 · 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 · xhigh · 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 4.8 in production

What the model is good at.

  • Advanced Chat

    Engages in complex, multi-turn conversations, following nuanced instructions, maintaining context, and adapting tone to different user needs.

  • Code Reasoning

    Understands, writes, and explains code across multiple languages, assisting with debugging, refactoring, and algorithmic problem-solving tasks.

  • Image Understanding

    Interprets images to identify objects, text, and visual relationships, supporting tasks like description, analysis, and high-level reasoning.

  • Document OCR

    Extracts and structures text from images or scanned documents, supporting downstream search, analysis, and content transformation workflows.

  • Multilingual Translation

    Translates between many languages, preserving meaning and tone while handling idioms, technical terminology, and longer documents.

What you get when you call Claude Opus 4.8 through LLM.API.

  • Unified AI Routing

    Automatically route each request to the optimal model across providers based on latency, capability, and constraints—no client changes or new SDKs required.

    One endpoint, any model

  • Cost-Optimized Scaling

    Balance price and performance with dynamic model selection, per-call controls, and usage limits so you can keep bills predictable even as traffic spikes.

    Lower spend, same quality

  • Resilient Fallback Logic

    Define automatic failover rules so if a model or provider degrades, requests are retried on healthy alternatives without errors leaking into your app.

    No more AI 500s

  • End-to-End Observability

    Get unified logs, traces, and metrics across all providers so you can debug prompts, compare models, and monitor latency from a single dashboard.

    See every token

  • Task-Aware Orchestration

    Express higher-level tasks—chat, generation, tools, RAG—once and let LLM.API pick the right models, parameters, and flows for each request.

    Describe tasks, not models

  • High-Throughput Batch

    Send large batches of prompts in a single call with automatic parallelization, rate-limit smoothing, and retries to maximize throughput across providers.

    Millions of calls, one API

When to Use — When NOT to Use

Use it if...

  • You need a frontier-level general-purpose model for complex reasoning and problem solving.
  • You need high-quality long-form writing, such as reports, documentation, or technical briefs.
  • Your use case involves nuanced analysis of long documents, contracts, or research papers.
  • Your use case involves multi-step coding tasks, refactoring, and explaining non-trivial codebases.
  • You need strong instruction-following and safe, aligned behavior for consumer-facing assistants.
  • Your use case involves detailed brainstorming, ideation, and refining product or design concepts.

Avoid if...

  • You need the absolute lowest-cost model for simple classification or routing tasks.
  • You need ultra-low-latency responses for real-time interaction on constrained devices.
  • Your workload requires extremely high request throughput where per-token cost dominates value.
  • You need heavy vision, audio, or multimodal processing that this text-focused model lacks.
  • You need an on-premise or fully self-hosted solution rather than a cloud API.
  • Your workload requires strict model determinism and reproducibility across many rapid deployments.

LLM API rating for Claude Opus 4.8

★ 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
4.0Benchmark qualityArtificial Analysis Intelligence Index 41.8 · LMArena text rank #44, Elo 1453
3.1Developer ecosystemContext window, tool and JSON support, price against the category median
Reasoning & chat 4.3Coding 4.3Vision & multimodal 4.3Tool & function calling 4.6Long context 4.6
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 4.8

LMArena#44 · Elo 1453Arena coding#37 · Elo 1488Speed—Sources: LMArena, Artificial Analysis

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

  • Developers on Reddit and GitHub report that Opus 4.8 suffers from serious language calibration issues, describing the output as verbose, jargon-heavy, and "toxic" to read compared to previous versions.
  • Engineers on Hacker News and specialized blogs describe the model as a reliable "workhorse" for coding, praising its surgical edits and predictability in long-horizon agentic workflows despite modest intelligence gains.
  • Developers note that while Opus 4.8 excels at generating functional greenfield prototypes, it can become less reliable and prone to drift during complex, iterative tasks requiring conflict resolution.

SOURCES

Frequently Asked Questions

  • What is Claude Opus 4.8?

    Claude Opus 4.8 is a flagship Anthropic large language model focused on high reasoning ability, complex analysis, and high-quality natural language generation.

  • What is Claude Opus 4.8 best suited for?

    Claude Opus 4.8 is best for complex multi-step reasoning, advanced coding, data analysis, long-form writing, and high-stakes assistant-style interactions.

  • How is Claude Opus 4.8 priced when accessed via LLM.API?

    Claude Opus 4.8 pricing on LLM.API follows LLM.API’s own per-token rates, which may differ from Anthropic’s direct pricing; check LLM.API’s pricing page.

  • What context window does Claude Opus 4.8 support on LLM.API?

    Claude Opus 4.8 supports long-context interactions on LLM.API; refer to the model’s context window specification in the LLM.API documentation for exact token limits.

  • How fast is Claude Opus 4.8 in terms of latency?

    Claude Opus 4.8 generally has higher latency than smaller Anthropic models due to its size, especially for very long prompts or outputs.

  • Which modalities does Claude Opus 4.8 support through LLM.API?

    Through LLM.API, Claude Opus 4.8 supports text input and output; multimodal capabilities depend on LLM.API’s enabled features for this model.

  • How do I call Claude Opus 4.8 via the LLM.API endpoint?

    Specify the model name "Claude Opus 4.8" in your LLM.API request payload and authenticate with your LLM.API key to start generating responses.

  • How does Claude Opus 4.8 compare to smaller Anthropic models on LLM.API?

    Claude Opus 4.8 provides stronger reasoning, coding, and analysis capabilities but is slower and more expensive than smaller Anthropic models exposed on LLM.API.

  • Can Claude Opus 4.8 browse the web or access external tools?

    Claude Opus 4.8 itself cannot browse; any tool use or web access must be implemented via LLM.API or your own middleware.

  • What are the main limitations of Claude Opus 4.8?

    Claude Opus 4.8 can still hallucinate, produce incorrect code or facts, and may struggle with very domain-specific or unseen proprietary data.

  • Is Claude Opus 4.8 suitable for real-time applications?

    Claude Opus 4.8 can power interactive apps, but its higher latency makes it less ideal for strict real-time or ultra-low-latency constraints.

  • Does Claude Opus 4.8 preserve conversational state across requests on LLM.API?

    Claude Opus 4.8 is stateless; you must send prior messages in each request to maintain conversation context via LLM.API.

COMPARE

Competitive Models

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  • Claude Opus 4.7

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    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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