Bonus: Top up now and we'll double your first deposit — get x2 credits instantly.
Get Started
Sign Up
Sign Up

Claude Fable 5 API: Endpoint & Pricing

Call Claude Fable 5 through LLM.API when you want Anthropic-family text generation with unified auth, provider choice, and production-friendly defaults.

Compare Claude Fable 5 alternatives (30) →
Start Using API

What is Claude Fable 5?

Claude Fable 5 is an Anthropic chat model exposed on LLM.API under id `claude-fable-5`. claude-fable-5 provided by anthropic, aws-bedrock. It is wired for API access through LLM.API with OpenAI-compatible patterns. Teams use it when they need reliable text generation with text, image inputs and text outputs over an OpenAI-compatible API.

Can you self-host Claude Fable 5?

No. We found no public weights for Claude Fable 5 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, with custom volume discounts on request.

Get API keySee API code ↓

Providers

LLM.API routes Claude Fable 5 to the providers below, with discounted effective rates versus list price.

List price by provider

InputOutput
anthropic$10 in
$50 out
aws-bedrock$10 in
$50 out
aws-mantle$10 in
$50 out
computebase$10 in
$50 out

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

ProviderPricingContextCapabilities
anthropicin $10; out $50 per 1M tokens1M tokensvision, tools, streaming, reasoning, web search, JSON, structured
aws-bedrockin $10; out $50 per 1M tokens1M tokensvision, tools, streaming, reasoning, JSON
aws-mantlein $10; out $50 per 1M tokens1M tokensvision, tools, streaming, reasoning, JSON
computebasein $10; out $50 per 1M tokens1M tokensvision, tools, streaming, reasoning, web search, JSON, structured

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

Try this model

Test Claude Fable 5 right here — free to start.

Claude Fable 5
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Claude Fable 5 through the OpenAI-compatible API — POST /v1/chat/completions.

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-fable-5 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-fable-5",
    "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-fable-5",
    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-fable-5",
  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-fable-5",
    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-fable-5",
  "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-fable-5 · 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.

Claude Fable 5 in production

What the model is good at.

  • Multi-step reasoning

    Breaks down complex problems into intermediate steps before answering. Grounded in the model's chat role rather than generic chat claims.

  • Instruction following

    Follows detailed system and user instructions with strong adherence to format and tone. Relevant for `claude-fable-5` workloads on LLM.API.

  • Structured outputs

    Can produce JSON-friendly or schema-oriented responses when prompted carefully.

  • Multilingual drafting

    Drafts and translates professional content across major business languages. Tuned to how teams typically call Claude Fable 5.

  • Code assistance

    Helps write, explain, refactor, and debug application code across common languages. Grounded in the model's chat role rather than generic chat claims.

What you get when you call Claude Fable 5 through LLM.API.

  • Unified AI Routing

    Reach Claude Fable 5 and sibling models through one OpenAI-compatible endpoint.

    One endpoint, every model.

  • Cost Control

    Compare provider price points and keep spend visible as you scale Claude Fable 5.

    More performance, less spend.

  • Reliability Layer

    Retry and route across configured providers when a single upstream blips.

    Never go dark on users.

  • Observability

    Production: Trace prompts, tokens, and errors for Claude Fable 5 alongside the rest of your stack.

    See every token, everywhere.

  • Drop-in SDKs

    Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

    Think tasks, not models.

  • Model Breadth

    Swap Claude Fable 5 for chat, media, or embedding alternatives without rewriting auth.

    Scale from 1 to millions.

When to Use — When NOT to Use

Use it if...

  • You need provider failover options exposed for this model id (Claude Fable 5)
  • You want OpenAI-compatible chat completions through a single LLM.API key (Claude Fable 5)
  • You need a general-purpose text model for assistants, agents, or content workflows (Claude Fable 5)
  • Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (Claude Fable 5)

Avoid if...

  • Your use case depends on unpublished proprietary benchmarks not listed here
  • You need guaranteed real-time hard latency SLAs without benchmarking the provider
  • You need pure embedding, OCR, or media generation instead of chat
  • You require on-prem only deployment with no cloud inference

LLM API rating for Claude Fable 5

★ 3.7/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · LMArena + Artificial Analysis
3.4Production adoptionRanked #8 on the public LMArena leaderboard
4.5Benchmark qualityArtificial Analysis Intelligence Index 49.6 · LMArena text rank #8, Elo 1491
3.1Developer ecosystemContext window, tool and JSON support, price against the category median
Reasoning & chat 3.7Coding 3.7Vision & multimodal 3.5Tool & function calling 3.8Long context 3.8
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.

BENCHMARKS

Claude Fable 5 benchmark scores

Intelligence index

This model50.
Tracked median24.

Scale: 0-100 index points

Output speed

This model66 t/s

Scale: 0-400 tokens per second

Reference price per 1M tokens

Input$10.00
Output$50.00

Bars compare input and output list prices for this model.

Intelligence index+108% vs median
Claude Fable 5 API: Endpoint & 30% Discount50
Median tracked model24

Higher is better. The median covers every model tracked by Artificial Analysis.

Output speed
66tok/s

Measured throughput; the full dial is 300 tokens per second.

Reference price / 1M tokens
Input$10.00
Output$50.00

Provider list prices, before the LLM.API discount.

Independent scores published by Artificial Analysis (source). Reference prices are provider list prices, not LLM.API pricing.

RELIABILITY

Claude Fable 5 uptime, last 30 days

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

Last 30 days

19/30 days operational | 97.55% uptime

Availability tracked for this model. Full history on the LLM Uptime Status page or the status hub. LLM.API routes around provider outages automatically.

COMMUNITY

What developers say about Claude models

LMArena#8 · Elo 1491Arena coding#9 · Elo 1518Speed66 tokens/sSources: LMArena, Artificial Analysis

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to Claude Fable 5.

  • Reviewers consistently rank the Opus tier at or near the frontier for coding and agentic work, and several note the price/performance gap to rivals has narrowed.
  • A recurring complaint in community round-ups is verbosity — strong reasoning, but long answers and heavy token use unless you constrain output.
  • Teams report the Sonnet/Haiku tiers as the practical default for volume, keeping Opus for planning and hard debugging.

SOURCES

Frequently Asked Questions

  • How do I call Claude Fable 5 via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "claude-fable-5" and your LLM.API key. See the code snippet on this page.

  • What modalities does Claude Fable 5 support?

    Claude Fable 5 accepts text, image and produces text according to its architecture metadata on LLM.API.

  • Where is the canonical page for Claude Fable 5?

    https://llmapi.ai/models/anthropic-claude-fable-5/

  • What are limitations of Claude Fable 5?

    Like other API models, Claude Fable 5 can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

  • How is Claude Fable 5 priced on LLM.API?

    Listed pricing metadata shows: In $10.00 / 1M tokens · Out $50.00 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

  • Is Claude Fable 5 a chat model?

    Yes—Claude Fable 5 is exposed as a chat/completions-style endpoint on LLM.API.

  • Which providers serve Claude Fable 5?

    LLM.API currently lists: anthropic, aws-bedrock, aws-mantle. Availability can vary by region and account.

  • What is the context length for Claude Fable 5?

    Reported context for Claude Fable 5 is 1M tokens. Always verify the active provider row if multiple providers are listed.

  • When should I choose Claude Fable 5?

    You need provider failover options exposed for this model id — especially when you specifically need Claude Fable 5.

  • What is Claude Fable 5?

    claude-fable-5 provided by anthropic, aws-bedrock. It is wired for API access through LLM.API with OpenAI-compatible patterns. On LLM.API it is addressed as `claude-fable-5`.

COMPARE

Competitive Models

Get one key to every model

Swap your API key. Keep your code.

Start for free Talk to sales