Fireworks AI

Run fast open models such as Kimi, DeepSeek, Qwen and GLM in your workflows. Have a model write or answer, sort text into categories, and pull data out of text and images; create embeddings and rank documents for semantic search; and keep an eye on spending.

Actions

Action	What it does
Ask a model	Send a prompt, and any pictures, to an open model on Fireworks and get its reply: write, summarize, translate, answer or describe an image.
Check the monthly spend limit	See this month's spending against the account's spend limit.
Create embeddings	Turn texts into vectors for semantic search, deduplication or clustering.
Extract data	Pull the fields you name out of text or pictures, such as the total on a photographed receipt or the sender's company in an email, ready for a spreadsheet row or a CRM record.
Get a model	Look up one model: what it is for, whether it reads images or calls tools, how much text it takes in, and when Fireworks plans to retire it.
Get spending	See what Fireworks usage cost over a range of days, in US dollars, split by model, day, person or API key.
List models	List the models you can use: Fireworks' ready-to-use ones, or the models uploaded or fine-tuned in your account.
Rank documents by relevance	Order a list of texts by how well each answers a question, best first, with a score from 0 to 1.
Sort text into a category	Have a model pick the one category that fits a piece of text, such as routing a support email.

Ask a model

Send a prompt, and any pictures, to an open model on Fireworks and get its reply: write, summarize, translate, answer or describe an image.

Inputs

PromptprompttextRequired: What to ask or tell the model.
Modelmodeltext: The model that does the work. Kimi K2.6 reads images and suits most jobs. You can also type any model id, including your own fine-tuned models.Default: accounts/fireworks/models/kimi-k2p6.
Instructionsinstructionstext: How the model should behave, such as 'Answer in one short paragraph' or 'You are a friendly support agent for Acme'.
Imagesimageslist: Pictures for the model to look at: upload them, pick files from an earlier step, or paste public web addresses. PNG, JPEG, GIF, BMP or TIFF, up to 30.
Thinkingthinkingtext: How much the model reasons before it answers. More is slower and uses more tokens; the thinking comes back separately from the reply.One of: default (Model's default), none (Off), low, medium, high. Default: default.
Maximum reply lengthmax_tokensnumber: The most tokens the reply may use; a token is about three-quarters of a word. Leave empty to let the model decide.At least 1.
Creativitytemperaturenumber: From 0 (focused and repeatable) to 2 (most varied). Leave empty for the model's default.From 0 to 2.

Outputs

finish_reasontext: stop when it finished, length when cut off.
input_tokensnumber: Tokens read, as billed.
modeltext: The model that answered.
output_tokensnumber: Tokens written, as billed.
replytext: The model's answer.
thinkingtext: Its reasoning, when it thought first.
total_tokensnumber: Both together.
was_cut_offtrue/false: Whether it hit the maximum reply length.

Example output

{
  "finish_reason": "stop",
  "input_tokens": 212,
  "model": "accounts/fireworks/models/kimi-k2p6",
  "output_tokens": 9,
  "reply": "Paris.",
  "thinking": null,
  "total_tokens": 221,
  "was_cut_off": false
}

Check the monthly spend limit

See this month's spending against the account's spend limit. Fireworks pauses every request once the limit is reached, so this makes a good first step before a large run.

Inputs

None.

Outputs

has_limittrue/false: Whether a monthly limit is set.
highest_allowed_limitnumber: The highest limit the account's tier allows.
is_pausedtrue/false: Whether the limit is reached and requests paused.
limitnumber: The limit, in dollars.
percent_usednumber: How much of the limit is used, from 0 to 100.
remainingnumber: Dollars left before the limit.
spent_this_monthnumber: Spent so far this month, in dollars.

Example output

{
  "has_limit": true,
  "highest_allowed_limit": 500,
  "is_paused": false,
  "limit": 500,
  "percent_used": 2.5,
  "remaining": 487.52,
  "spent_this_month": 12.48
}

Create embeddings

Turn texts into vectors for semantic search, deduplication or clustering. Vectors come back at unit length, so a dot product of two is their cosine similarity.

Inputs

TextstextslistRequired: The texts to turn into vectors, one per entry. Each gives one vector, in the same order.
Modelmodeltext: Qwen3 Embedding 8B is the best for search. Match the model already used for any vectors you will compare these with.One of: fireworks/qwen3-embedding-8b (Qwen3 Embedding 8B (recommended)), nomic-ai/nomic-embed-text-v1.5 (Nomic Embed v1.5 (768 numbers)), BAAI/bge-base-en-v1.5 (BGE Base English v1.5 (768 numbers)), BAAI/bge-small-en-v1.5 (BGE Small English v1.5 (384 numbers)), thenlper/gte-large (GTE Large (1,024 numbers)), mixedbread-ai/mxbai-embed-large-v1 (mxbai Embed Large v1 (1,024 numbers)). Default: fireworks/qwen3-embedding-8b.
Vector lengthdimensionsnumber: Shorter vectors take less storage. Only Qwen3 Embedding 8B (up to 4,096) and Nomic Embed v1.5 (up to 768) can be shortened; leave empty for the full length.At least 1.

Outputs

countnumber: How many vectors.
dimensionsnumber: How many numbers each vector has.
embeddinglist: The first text's vector.
embeddingslist: One vector per text, in the same order.
modeltext: The model used.
total_tokensnumber: Tokens billed.

Example output

{
  "count": 2,
  "dimensions": 3,
  "embedding": [
    0.012,
    -0.043,
    0.031
  ],
  "embeddings": [
    [
      0.012,
      -0.043,
      0.031
    ],
    [
      0.018,
      -0.039,
      0.027
    ]
  ],
  "model": "fireworks/qwen3-embedding-8b",
  "total_tokens": 14
}

Extract data

Pull the fields you name out of text or pictures, such as the total on a photographed receipt or the sender's company in an email, ready for a spreadsheet row or a CRM record.

Inputs

Texttexttext: The text to pull the fields out of, such as an email or an invoice. Leave empty when the images hold everything.
Imagesimageslist: Pictures for the model to look at: upload them, pick files from an earlier step, or paste public web addresses. PNG, JPEG, GIF, BMP or TIFF, up to 30.
Fieldsfieldslist: The pieces of information you want, one per entry. Add what each means after a colon to steer it: 'total: the amount due in dollars'. A field the material does not mention comes back empty, never guessed.
Instructionsinstructionstext: Extra rules, such as 'Write dates as YYYY-MM-DD'.
Modelmodeltext: The model that does the work. Kimi K2.6 reads images and suits most jobs. You can also type any model id, including your own fine-tuned models.Default: accounts/fireworks/models/kimi-k2p6.
JSON Schema (advanced)json_schematext: Instead of 'Fields', paste a JSON Schema whose root is an object, for numbers, lists or nested fields. When set, 'Fields' is ignored.

Outputs

dataobject: The fields, by the names asked for.
found_fieldslist: The fields that were filled.
input_tokensnumber: Tokens read, as billed.
missing_fieldslist: The fields the material did not mention.
modeltext: The model that read it.
output_tokensnumber: Tokens written, as billed.
total_tokensnumber: Both together.

Example output

{
  "data": {
    "invoice number": "INV-1042",
    "total": "$1,250.00",
    "vendor": "Acme Ltd"
  },
  "found_fields": [
    "invoice number",
    "vendor",
    "total"
  ],
  "input_tokens": 212,
  "missing_fields": [],
  "model": "accounts/fireworks/models/kimi-k2p6",
  "output_tokens": 9,
  "total_tokens": 221
}

Get a model

Look up one model: what it is for, whether it reads images or calls tools, how much text it takes in, and when Fireworks plans to retire it.

Inputs

ModelmodeltextRequired: The model to look up. Pick one, or type its id, such as accounts/fireworks/models/kimi-k2p6.

Outputs

calls_toolstrue/false: Whether it can call tools.
context_lengthnumber: The most tokens it can take in at once.
created_attext: When it was added.
descriptiontext: What Fireworks says about it.
fine_tuned_fromtext: The model it was fine-tuned from, if it was.
hugging_face_urltext: Its page on Hugging Face, when it has one.
idtext: The model's id, to use in any Fireworks step.
nametext: Its display name.
parameter_countnumber: How many parameters it has, when known.
reads_imagestrue/false: Whether it can read pictures.
readytrue/false: Whether it is ready to use (not still uploading).
retires_ontext: The day Fireworks takes it off serverless, if set.
serverlesstrue/false: Whether it runs on Fireworks' pay-per-token service.
typetext: chat, embedding, reranker or other.

Example output

{
  "calls_tools": true,
  "context_length": 262144,
  "created_at": "2026-04-20T17:00:00Z",
  "description": "Moonshot AI's open agentic model.",
  "fine_tuned_from": null,
  "hugging_face_url": "https://huggingface.co/moonshotai/Kimi-K2.6",
  "id": "accounts/fireworks/models/kimi-k2p6",
  "name": "Kimi K2.6",
  "parameter_count": 1000000000000,
  "reads_images": true,
  "ready": true,
  "retires_on": null,
  "serverless": true,
  "type": "chat"
}

Get spending

See what Fireworks usage cost over a range of days, in US dollars, split by model, day, person or API key. Covers the whole account when the key belongs to an admin, otherwise your own usage.

Inputs

Fromstart_datedate: The first day to include. Leave empty for the start of this month.
Toend_datedate: The last day to include. Leave empty for today.
Break downbreakdowntext: How to split the total. Days are UTC.One of: model (By model), day (By day), day_model (By day and model), user (By person), api_key (By API key), total (Just the total). Default: model.

Outputs

coverstext: account for everyone's usage, you for yours.
currencytext: Always USD.
end_datetext: The last day.
row_countnumber: How many rows.
rowslist: The total, split up.
start_datetext: The first day.
totalnumber: The total, in dollars.

Example output

{
  "covers": "account",
  "currency": "USD",
  "end_date": "2026-09-23",
  "row_count": 1,
  "rows": [
    {
      "api_key_id": null,
      "cost": 12.48,
      "day": null,
      "model": "accounts/fireworks/models/kimi-k2p6",
      "user": null
    }
  ],
  "start_date": "2026-09-01",
  "total": 12.48
}

List models

List the models you can use: Fireworks' ready-to-use ones, or the models uploaded or fine-tuned in your account. Each says whether it reads images and how much text it takes in.

Inputs

Whose modelssourcetext: Fireworks' ready-to-use models, billed per token, or the models uploaded or fine-tuned in your account.One of: serverless (Ready to use on Fireworks), account (Your own models). Default: serverless.
What it's forpurposetext: Only models that do this.One of: all (Anything), chat, vision (Chat that reads images), embedding (Embeddings), reranker (Reranking). Default: all.
Name containssearchtext: Only models whose name or id contains this, such as 'qwen'.

Outputs

countnumber: How many there are.
model_idslist: Every model's id, in the same order.
modelslist: The models, by name.

Example output

{
  "count": 1,
  "model_ids": [
    "accounts/fireworks/models/kimi-k2p6"
  ],
  "models": [
    {
      "calls_tools": true,
      "context_length": 262144,
      "created_at": "2026-04-20T17:00:00Z",
      "description": "Moonshot AI's open agentic model.",
      "fine_tuned_from": null,
      "id": "accounts/fireworks/models/kimi-k2p6",
      "name": "Kimi K2.6",
      "reads_images": true,
      "ready": true,
      "retires_on": null,
      "serverless": true,
      "type": "chat"
    }
  ]
}

Rank documents by relevance

Order a list of texts by how well each answers a question, best first, with a score from 0 to 1. Useful for picking the passages to hand an AI step.

Inputs

QuestionquerytextRequired: What you are looking for, written as a question or a search.
DocumentsdocumentslistRequired: The texts to rank, one per entry.
How many to returntop_nnumber: Keep only the best matches. Leave empty to rank them all.At least 1.

Outputs

countnumber: How many were returned.
querytext: The question.
resultslist: The documents, best match first.
top_documenttext: The best match's text.
top_positionnumber: Where the best match was in the list, from 1.
top_scorenumber: The best match's score, from 0 to 1.

Example output

{
  "count": 2,
  "query": "What is the capital of France?",
  "results": [
    {
      "document": "Paris is the capital of France.",
      "position": 2,
      "rank": 1,
      "score": 0.98
    },
    {
      "document": "The Loire is France's longest river.",
      "position": 1,
      "rank": 2,
      "score": 0.07
    }
  ],
  "top_document": "Paris is the capital of France.",
  "top_position": 2,
  "top_score": 0.98
}

Sort text into a category

Have a model pick the one category that fits a piece of text, such as routing a support email. The answer is always exactly one of the categories you list.

Inputs

TexttexttextRequired: The text to sort, such as an email, a review or a support ticket.
CategoriescategorieslistRequired: The categories to choose from, one per entry. The step always returns exactly one of them, spelled as you wrote it.
How to decideinstructionstext: Rules for borderline cases, such as 'Anything that mentions money back is a Refund request'.
Modelmodeltext: The model that does the work. Kimi K2.6 reads images and suits most jobs. You can also type any model id, including your own fine-tuned models.Default: accounts/fireworks/models/kimi-k2p6.

Outputs

categorieslist: The categories it chose from.
categorytext: The category chosen.
input_tokensnumber: Tokens read, as billed.
modeltext: The model that chose.
output_tokensnumber: Tokens written, as billed.
total_tokensnumber: Both together.

Example output

{
  "categories": [
    "Refund request",
    "Bug report",
    "Sales question"
  ],
  "category": "Refund request",
  "input_tokens": 212,
  "model": "accounts/fireworks/models/kimi-k2p6",
  "output_tokens": 9,
  "total_tokens": 221
}

ActionsCopy Anchor Link

Ask a modelCopy Anchor Link

Inputs

Outputs

Check the monthly spend limitCopy Anchor Link

Inputs

Outputs

Create embeddingsCopy Anchor Link

Inputs

Outputs

Extract dataCopy Anchor Link

Inputs

Outputs

Get a modelCopy Anchor Link

Inputs

Outputs

Get spendingCopy Anchor Link

Inputs

Outputs

List modelsCopy Anchor Link

Inputs

Outputs

Rank documents by relevanceCopy Anchor Link

Inputs

Outputs

Sort text into a categoryCopy Anchor Link

Inputs

Outputs

On this page

Actions

Ask a model

Check the monthly spend limit

Create embeddings

Extract data

Get a model

Get spending

List models

Rank documents by relevance

Sort text into a category