Inference Logoinference shell

Claude Fable 5.1 (Claude Code)

Claude Fable 5.1 through Claude Code on your own Claude Pro/Max plan. Connect your `claude setup-token` token; usage counts against your Claude plan, not inference.sh.

claude codesubscriptionreasoningcoding
run in browser →run via APIrun with your agent
maker
anthropic
category
chat
runs via
api, javascript and python sdk, belt cli, mcp
updated
2026-10-29
price
from $0.00000277/sec

api reference

about

claude fable 5.1 through claude code on your own claude pro/max plan. connect your `claude setup-token` token; usage counts against your claude plan, not inference.sh.

get an api key

create one in settings → api keys. the examples read it from INFERENCE_API_KEY.

bash
1export INFERENCE_API_KEY="inf_your_key"

1. calling the api

install the client

nothing to install. any http client works; the examples use curl, and jq to read responses. every response wraps its result in a data field.

run and get result

send "wait": true to hold the connection until the task finishes. without it the call returns right away with the task still running.

bash
12# "wait": true holds the connection until the task finishes.3# the response is {"data": {"id", "status", "status_text", "output", ...}}4curl -X POST https://api.inference.sh/apps/run \5  -H "Authorization: Bearer $INFERENCE_API_KEY" \6  -H "Content-Type: application/json" \7  -d '{8    "app": "anthropic/claude-code-fable-5-1",9    "input": {},10    "wait": true11  }'

stream live updates

get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks. send "stream": true and the response is a server-sent event stream that ends when the task does.

bash
12# "stream": true answers with server-sent events until the task finishes3curl -N -X POST https://api.inference.sh/apps/run \4  -H "Authorization: Bearer $INFERENCE_API_KEY" \5  -H "Content-Type: application/json" \6  -d '{7    "app": "anthropic/claude-code-fable-5-1",8    "input": {},9    "stream": true10  }'

run in the background

start the task and get its id back right away, then check on it later. for long tasks, or when you'd rather be called back, see webhooks.

bash
1# without "wait" the call returns the task right away (status 1, "received")2TASK_ID=$(curl -s -X POST https://api.inference.sh/apps/run \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{6    "app": "anthropic/claude-code-fable-5-1",7    "input": {}8  }' | jq -r '.data.id')910# later: read its state. done when status is 10 (completed), 11 (failed) or 12 (cancelled)11curl -s https://api.inference.sh/tasks/$TASK_ID \12  -H "Authorization: Bearer $INFERENCE_API_KEY" | jq '.data | {status, status_text, output}'

2. authentication

the api uses api keys for authentication. see the authentication docs for detailed setup instructions.

3. files

file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.

automatic upload

pass urls directly or use data uris for inline content.

bash
1# pass URLs or data URIs as input values2curl -X POST https://api.inference.sh/apps/run \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{6    "app": "anthropic/claude-code-fable-5-1",7    "input": {8      "image": "https://example.com/image.png",9      "audio": "data:audio/mp3;base64,..."10    },11    "wait": true12  }'

manual upload

you can also upload files manually and use the returned url.

bash
1# 1. create the file: the response has an upload_url and the file's uri2FILE=$(curl -s -X POST https://api.inference.sh/files \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{"files": [{"filename": "image.png", "content_type": "image/png"}]}')67# 2. PUT the bytes to upload_url8curl -X PUT "$(echo "$FILE" | jq -r '.data[0].upload_url')" \9  -H "Content-Type: image/png" \10  --data-binary @image.png1112# 3. pass the uri as an input value13echo "$FILE" | jq -r '.data[0].uri'

4. webhooks

get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.

bash
1curl -X POST https://api.inference.sh/apps/run \2  -H "Authorization: Bearer $INFERENCE_API_KEY" \3  -H "Content-Type: application/json" \4  -d '{5    "app": "anthropic/claude-code-fable-5-1",6    "input": {},7    "webhook": "https://your-server.com/webhook"8  }'

webhook payload

your endpoint receives a JSON POST with the task result:

json
1{2  "event": "task.completed",3  "timestamp": "2024-01-15T10:30:05Z",4  "data": {5    "id": "task_abc123",6    "short_id": "abc123",7    "status": 10,8    "status_text": "completed",9    "output": { ... },10    "created_at": "2024-01-15T10:30:00Z",11    "updated_at": "2024-01-15T10:30:05Z"12  }13}
eventstringtask.completed, task.failed or task.cancelled
timestampstringiso timestamp of the delivery
data.idstringtask id
data.short_idstringshort task id
data.statusnumberterminal status (10=completed, 11=failed, 12=cancelled, 13=rejected)
data.status_textstringthe status as a word: completed, failed or cancelled
data.outputobjecttask output (when completed)
data.errorstringerror message (when failed, omitted otherwise)
data.session_idstringsession id (when the task ran in a session, omitted otherwise)
data.created_atstringiso timestamp
data.updated_atstringiso timestamp

5. schema

input

textstring

text

default: ""
imagesarray

images

filesarray

files

attachmentsarray

attachments

contextarray

context

default: []
rolestring

chatmessagerole

default: "user"
options:"system""user""assistant""tool""injection""compaction""event"
reasoningstring

reasoning

tool_call_idstring

tool call id

system_promptstring

system prompt

default: "you are a helpful assistant that can answer questions and help with tasks."
context_sizeinteger

the context size for the model.

default: 1000000
temperaturenumber

temperature

default: 0.7min:0max:1
top_pnumber

top p

default: 0.95min:0max:1
top_kinteger

top k

min_pnumber

min p

frequency_penaltynumber

frequency penalty

presence_penaltynumber

presence penalty

repetition_penaltynumber

repetition penalty

seedinteger

seed

stoparray

stop

max_tokensinteger

max tokens

default: 64000
reasoning_effortstring

reasoningeffortenum

default: "none"
options:"low""medium""high""none"
reasoning_max_tokensinteger

reasoning max tokens

toolsarray

tools

tool_choiceobject

toolchoice

response_formatobject

responseformat

output

reasoningstring

reasoning

reasoning_stateobject

reasoning state

responsestring

response

tool_callsarray

tool calls

usageobject

llmusage

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Claude Fable 5.1

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new models

  1. Tripo Auto Rig

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    3d$0.01/credit (rig check free, auto rig 25 credits, retarget 10 credits/animation)

  2. Hunyuan 3D Auto Rigging

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    3d$0.02/credit, 10 credits/call ($0.20/call)

  3. TokenRig

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    3dGPU time, billed per millisecond

  4. Hunyuan 3D 3.1

    tencent/hunyuan-3d-3-1

    3d$0.02/credit; Geometry 15cr, Normal 25cr, +10cr views/PBR/face-count

  5. Rodin Gen-2.5

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