Inference Logoinference shell

xLAM 2 32B

A system capable of advanced multi-step reasoning and a strong understanding of language and context to create actionable plans.

run in browser →run via APIrun with your agent
maker
infsh
category
chat
runs via
api, javascript and python sdk, belt cli, mcp
updated
2026-09-17

api reference

about

a system capable of advanced multi-step reasoning and a strong understanding of language and context to create actionable plans.

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": "infsh/xlam-2-32b-fc-r-i1",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": "infsh/xlam-2-32b-fc-r-i1",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 $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{6    "app": "infsh/xlam-2-32b-fc-r-i1",7    "input": {}8  }' | jq -r '.data.id')910# later: read its state. done when status is 10 (completed), 11 (failed) or 12 (cancelled)11curl -s https://api.inference.sh/tasks/$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": "infsh/xlam-2-32b-fc-r-i1",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": "infsh/xlam-2-32b-fc-r-i1",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

system_promptstring

the system prompt to use for the model

default: ""
context_sizeinteger

context size

default: 4096
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

contextarray

context

default: []
rolestring

chatmessagerole

default: "user"
options:"system""user""assistant""tool""injection""compaction"
textstring

text

default: ""
reasoningstring

reasoning

attachmentsarray

attachments

imagesarray

images

filesarray

files

tool_call_idstring

tool call id

output

reasoningstring

reasoning

responsestring

response

tool_callsarray

tool calls

usageobject

llmusage

other chat apps

DeepSeek V4.1 Flash 1017

melious/deepseek-v4-1-flash-1017

$0.341/M input, $0.0341/M cached input, $1.364/M output

Gemini 4 Argon

google/gemini-4-argon

$2.00/M input, $10.00/M output

GPT-6 Sol

openai/gpt-6-sol

$2.00/M input, $0.20/M cached input, $10.00/M output

GPT-6 Luna

openai/gpt-6-luna

$0.10/M input, $0.01/M cached, $0.50/M output

GPT-6.1 Sol

openai/gpt-6-1-sol

$2.00/M input, $0.10/M cached, $10.00/M output

more from infsh

HTTP Request

infsh/http

from $0.00000277/sec

DramaBox Text-to-Speech

infsh/dramabox

GPU time, billed per millisecond

Markdown to Word Converter

infsh/markdown-to-word

from $0.00000277/sec

Harrier OSS v1 Text Embeddings

infsh/harrier-oss-v1

GPU time, billed per millisecond

new models

  1. VACE

    pruna/vace

    video$0.40/video

  2. P-Video-Animate

    pruna/p-video-animate

    video$0.03/sec 720p, $0.06/sec 1080p

  3. P-Video-Upscale

    pruna/p-video-upscale

    videoStd: $0.01/sec 720p, $0.02 1080p, $0.04 2k, $0.08 4k; high fps 2x

  4. P-Video-2-Pro-Avatar

    pruna/p-video-2-pro-avatar

    video$0.02/sec 480p, $0.04/sec 768p, $0.06/sec 1080p, $0.08/sec 2k, $0.12/sec 4k

  5. P-Video-2-Pro-Reference

    pruna/p-video-2-pro-reference

    videoOutput: cost $0.015-$0.105/sec, speed $0.02-$0.115/sec. Ref video: $0.03-$0.06/sec. Ref img: $0.005/img. Ref audio: $0.0015-$0.0025/sec

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