Inkling API

Inkling is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 41B active parameters out of 975B total. It is designed for general-purpose reasoning, coding, agentic and tool-use systems.

Modalities: Image, TextText
In / Out price: $1.06/$4.28per 1M
Context window: 1.05M tokens

Inkling overview

Inkling by Thinking Machines

Handles text, image, and audio input with native reasoning across all three, released with open weights for fine-tuning and integration.

Inkling specs

Model ID: thinkingmachines/inkling
Developer: Thinking Machines
Context window: 1,048,576 tokens
Input: Image, Text
Output: Text
API: OpenAI-compatible, base URL https://api.aimlapi.com/v1

What Is Inkling API?

Inkling is a general-purpose multimodal model from Thinking Machines that accepts text, image, and audio inputs and generates text outputs. It is described as a model for developers building AI-powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems.

Thinking Machines describes Inkling as an efficient open model for text, images, and audio, and says it is their most capable open model with native reasoning over those modalities. The model is released with open weights for research, fine-tuning, and downstream integration.

Technical Reference

Developer	Thinking Machines
Model ID	thinkingmachines/inkling
Context Window	1,048,576 tokens (~1.0M)
Modalities	Image, Text → Text
Capabilities	file input, parallel tool calls, reasoning, streaming, structured (JSON schema) output, tool/function calling, vision

What Inkling Brings to the Table

Multimodal input support
Inkling accepts text, image, and audio inputs and produces text outputs.

Open weights
The model is released with open weights to support research, fine-tuning, and integration into third-party products.

Developer-focused design
Thinking Machines says Inkling is intended for developers building AI-powered applications such as agentic systems, coding assistants, chatbots, and RAG systems.

Reasoning across modalities
Thinking Machines describes Inkling as their most capable open model with native reasoning over text, images, and audio.

Where Inkling Makes the Most Sense

Agentic applications
Inkling is positioned for agentic and tool-use systems that need multimodal understanding and text generation.

Coding assistants
The model is described as suitable for coding assistants and across multiple coding languages.

Chatbots
Thinking Machines lists chatbots among the application types Inkling is designed to support.

Retrieval-augmented generation
Inkling is presented as appropriate for retrieval-augmented generation systems.

Inkling vs the Alternatives

None stated
No attributable comparisons to named alternative models were found in the search results for this exact model id.

Sources: https://huggingface.co/thinkingmachines/Inkling · https://thinkingmachines.ai/inkling/ · https://thinkingmachines.ai/news/introducing-inkling/

Use cases

Inkling is documented as suited to building AI-powered applications that use agents and tools, including agentic and tool-use systems. It is also suited to coding assistants and coding workflows, with the model described as handling coding tasks and writing code to get work done.

It is documented for general-purpose conversational use, instruction-following, and other natural language tasks, including detailed multi-step instructions. It is also suited to multimodal workflows involving text, image, and audio input, including reasoning across those modalities.

Capabilities

File input
Reasoning
Streaming
Vision

Inkling benchmarks by Artificial Analysis

See how Inkling compares in intelligence, generation speed, cost per task and latency. Data retrieved Nov 3, 2026.

Intelligence#40 / 5012.5Index v5.0 Speed#25 / 219215Output tokens / second Cost per task#22 / 44$1.35USD / benchmark task Latency#123 / 2161.71Seconds to first token

Intelligence

Index v5.0

Speed

Output tokens / second

Cost per task

USD / benchmark task

Benchmark results

Compare 17 models available on AI/ML API across 5 independent benchmarks.

Humanity's Last Exam

Expert-written questions across academic subjects.

Inkling API pricing

Inkling pricing in US dollars
Usage	Price	Unit
Input	$1.06	1M tokens
Output	$4.28	1M tokens
Cached input	$0.1799	1M tokens

Prices from the AI/ML API price list, checked on Nov 4, 2026.

Inkling vs other models: token prices

AI/ML API input and output rates for 5 models, per million tokens.

AI/ML API model prices in USD per million tokens
Model	Input	Output
Inkling	$1.06	$4.28
Inkling Small	$0.6136	$1.52
Mistral Large 4	$1.36	$4.18
Sakana Namazu	$1.01	$4.23
GLM 5.2	$1.4	$4.4

How to use the Inkling API

View docs

Build with your agent

Give your agent the model details, code and documentation.

1Get your API key

Create API key

export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"

2Make your first request

curl 'https://api.aimlapi.com/v1/chat/completions' \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "thinkingmachines/inkling",
  "messages": [
    {
      "role": "user",
      "content": "Write a haiku about the ocean."
    }
  ]
}'

import os
import requests

headers = {
    "Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
    "Content-Type": "application/json",
}
payload = {
    "model": "thinkingmachines/inkling",
    "messages": [
        {
            "role": "user",
            "content": "Write a haiku about the ocean."
        }
    ]
}

response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())

const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "thinkingmachines/inkling",
    "messages": [
      {
        "role": "user",
        "content": "Write a haiku about the ocean."
      }
    ]
  }),
});

if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());

Inkling FAQ

What is the context window of Inkling?

Inkling has a 1,048,576 tokens context window.

What inputs and outputs does Inkling support?

Inkling takes image, text as input and returns text.

What is the model ID for Inkling?

Use thinkingmachines/inkling as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.

How much does Inkling cost?

Inkling costs $1.06 input and $4.28 output per 1M tokens on AI/ML API, billed as you go with no subscription.

Does Inkling support streaming?

Yes, Inkling can stream responses as they are generated.

Can Inkling read images?

Yes, Inkling accepts image input alongside text.

Who created Inkling?

Inkling was built by Thinking Machines.

How large is Inkling in terms of parameters?

Inkling is a mixture-of-experts model with 41B active parameters out of 975B total.

Does Inkling support tool use for agentic workflows?

Inkling supports tools, parallel tool calls, structured output, and reasoning, making it suitable for agentic and tool-use systems.

Start building with Inkling

Get API key

Inkling API

thinkingmachines/inkling

Build with your agent

Give your agent the model details, code and documentation.

1Get your API key

Create API key

export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"

2Make your first request

curl 'https://api.aimlapi.com/v1/chat/completions' \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "thinkingmachines/inkling",
  "messages": [
    {
      "role": "user",
      "content": "Write a haiku about the ocean."
    }
  ]
}'

import os
import requests

headers = {
    "Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
    "Content-Type": "application/json",
}
payload = {
    "model": "thinkingmachines/inkling",
    "messages": [
        {
            "role": "user",
            "content": "Write a haiku about the ocean."
        }
    ]
}

response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())

const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "thinkingmachines/inkling",
    "messages": [
      {
        "role": "user",
        "content": "Write a haiku about the ocean."
      }
    ]
  }),
});

if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());

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Inkling API

Inkling overview

Inkling by Thinking Machines

Inkling specs

What Is Inkling API?

Technical Reference

What Inkling Brings to the Table

Where Inkling Makes the Most Sense

Inkling vs the Alternatives

Use cases

Capabilities

Inkling benchmarks by Artificial Analysis

Intelligence

Speed

Cost per task

Benchmark results

Humanity's Last Exam

GPQA Diamond

Terminal-Bench 4.0

AA-LCR

SciCode

Inkling API pricing

Inkling vs other models: token prices

How to use the Inkling API

Build with your agent

1Get your API key

2Make your first request

Inkling FAQ

Start building with Inkling

Inkling API

Build with your agent

1Get your API key

2Make your first request

Inkling API

Inkling overview

Inkling by Thinking Machines

Inkling specs

What Is Inkling API?

Technical Reference

What Inkling Brings to the Table

Where Inkling Makes the Most Sense

Inkling vs the Alternatives

Use cases

Capabilities

Intelligence

Speed

Cost per task

Benchmark results

Humanity's Last Exam

GPQA Diamond

Terminal-Bench 4.0

AA-LCR

SciCode

Inkling API pricing

Inkling vs other models: token prices

How to use the Inkling API

Build with your agent

1Get your API key

2Make your first request

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