Inkling
Inkling is an open-weights multimodal Mixture-of-Experts model that reasons over text, images, and audio. It supports controllable thinking effort and a context window of 262.1K tokens. Call Inkling on AI Gateway with thinkingmachines/inkling.
- Input and output price
- Prices from: Input $1, Output $4.05, Per 1M tokens
- 24h uptime
- Loading AI Gateway uptime
import { streamText } from 'ai'
const result = streamText({ model: 'thinkingmachines/inkling', prompt: 'Why is the sky blue?'})Copy link to headingPlayground
Try out Inkling by Thinking Machines. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.
Inkling
Inkling
Copy link to headingProviders
Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.
Copy link to headingUptime24 hours
Direct request success rate on AI Gateway and per-provider. Visit the docs for more info.
Copy link to headingThroughput24 hours
P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the docs for more info.
Copy link to headingLatency24 hours
P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the docs for more info.
Getting started
Call Inkling through AI Gateway with the AI SDK generateText and streamText functions, or through the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages APIs by changing the base URL. AI Gateway authenticates the request and routes it to an available provider.
Install the AI SDK (pnpm add ai dotenv), create an API key from the API Keys page, and set it as AI_GATEWAY_API_KEY in your environment. Full setup is covered in the text generation quickstart.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'thinkingmachines/inkling', prompt: 'Why is the sky blue?', });
console.log(result.text);}
main().catch(console.error);Top-level parameters
The same Inkling request in each API format AI Gateway supports.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'thinkingmachines/inkling', system: 'You are a concise technical assistant.', prompt: 'Summarize the tradeoffs between static generation and SSR.', maxOutputTokens: 1024, });
console.log(result.text);}
main().catch(console.error);Standard parameters like prompt, messages, temperature, and tools work as documented in the AI SDK docs. These are the parameters with model-specific behavior.
| Parameter | Type | Required | Description |
|---|---|---|---|
model | string | Yes | Model ID in the form creator/model, e.g. thinkingmachines/inkling. AI Gateway routes the request to an available provider. |
maxOutputTokens | number | No | Hard cap on generated tokens. Inkling supports up to 262,144 output tokens. Reasoning tokens count toward this limit. |
reasoning | 'provider-default' | 'none' | 'minimal' | 'low' | 'medium' | 'high' | 'xhigh' | No | Provider-agnostic reasoning effort, available in AI SDK 7 or later. Maps to the provider’s native reasoning configuration; reasoning settings under providerOptions take precedence when both are set. See the Reasoning section below. |
providerOptions | Record<string, JSONValue> | No | AI Gateway routing options under gateway, plus any provider-native options under the provider’s own namespace — see the table below. |
Input limits
| Input | Formats | Sources | Max count | Max size | Limits |
|---|---|---|---|---|---|
| Text | — | — | — | — | Prompt and response share the 262K-token context window |
| Image | — | URL, base64, Uint8Array | — | — | Sent as image parts in messages; counts as input tokens |
| — | URL, base64, Uint8Array | — | — | Sent as file parts in messages; counts as input tokens |
Provider options
Set AI Gateway routing options under providerOptions.gateway. For provider-specific options, pass them under the provider’s namespace as documented by the AI SDK.
Learn more in the AI SDK provider docs.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'thinkingmachines/inkling', prompt: 'Why is the sky blue?', providerOptions: { gateway: { only: ['baseten', 'togetherai'], }, }, });
console.log(result.text);}
main().catch(console.error);These AI Gateway routing options apply to every model. Provider-specific options pass through under the provider’s own namespace (for example providerOptions.anthropic) exactly as documented by the AI SDK.
| Parameter | Type | Required | Description |
|---|---|---|---|
providerOptions.gateway.only | string[] | No | Restrict routing to these provider slugs. Requests fail over only within the listed providers. |
providerOptions.gateway.order | string[] | No | Preferred provider order. Listed providers are tried first; unlisted providers remain available as fallbacks. |
providerOptions.gateway.sort | 'cost' | 'ttft' | 'tps' | No | Rank candidate providers by price, time to first token, or tokens per second instead of the default routing order. |
providerOptions.gateway.zeroDataRetention | boolean | No | Route only to providers with a zero-data-retention policy for this model. |
Routing across providers
AI Gateway serves the same model through multiple providers and fails over automatically. order expresses a preference while keeping every provider eligible; only is a hard allowlist — if none of the listed providers are available the request fails instead of falling back.
Options under a provider's own namespace (for example providerOptions.anthropic) are forwarded to that provider with the request. Providers ignore option namespaces that don't apply to them, so it is safe to set provider options alongside gateway routing options.
Reasoning
AI Gateway bridges reasoning across every API format. The AI SDK exposes a provider-agnostic top-level reasoning level (none, minimal, low, medium, high, or xhigh); the Chat Completions and Responses formats take the same effort under reasoning.effort; and the Anthropic Messages format uses a native thinking token budget. Whichever you send, the gateway maps it to the target model’s native configuration, converting between effort levels and token budgets as needed. Reasoning-related settings under providerOptions take full precedence over the top-level reasoning value and are never merged. Reasoning tokens typically count toward your output-token usage, though how they’re reported and billed varies by provider.
Learn more in the AI Gateway reasoning guide.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'thinkingmachines/inkling', prompt: 'Explain the Monty Hall problem step by step.', reasoning: 'high', });
console.log(result.text);}
main().catch(console.error);Image input
Send images alongside text as message parts. Images count as input tokens.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'thinkingmachines/inkling', messages: [ { role: 'user', content: [ { type: 'text', text: 'Describe this image.' }, { type: 'image', image: 'https://example.com/photo.jpg' }, ], }, ], });
console.log(result.text);}
main().catch(console.error);PDF input
Attach PDFs as file parts. Their contents count as input tokens.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'thinkingmachines/inkling', messages: [ { role: 'user', content: [ { type: 'text', text: 'Summarize this document.' }, { type: 'file', mediaType: 'application/pdf', data: 'https://example.com/document.pdf', }, ], }, ], });
console.log(result.text);}
main().catch(console.error);Tool calling
Expose tools the model can call. Define each tool’s inputs with a Zod schema.
import { generateText, tool } from 'ai';import { z } from 'zod';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'thinkingmachines/inkling', prompt: 'What is the weather in San Francisco?', tools: { getWeather: tool({ description: 'Get the current weather for a location', inputSchema: z.object({ location: z.string() }), execute: async ({ location }) => ({ location, temperatureC: 18 }), }), }, });
console.log(result.text);}
main().catch(console.error);Copy link to headingMore models by Thinking Machines
| Model |
|---|
| 1M | 0.3 s | 322 tps | $0.30/M | $1.20/M | Read$0.06/M | — | +2 |   +1 | 07/30/2026 |
Copy link to headingAbout Inkling
Inkling became available on AI Gateway on July 15, 2026. Inkling is a decoder-only transformer with a sparse Mixture-of-Experts (MoE) backbone: 66 layers, 975 billion total parameters, and 41 billion active per token, with each token routed to 6 of 256 experts plus 2 shared experts. Attention mixes local and global layers, and the context window is 262.1K tokens. Thinking Machines released the weights under the Apache 2.0 license.
Multimodality is native rather than bolted on. Images enter through a hierarchical patch encoder and audio through discrete token encoding, and both are processed jointly with text by the same decoder. Inkling accepts pixel-based images with each dimension between 40px and 4096px, and WAV audio sampled at 16kHz. Inkling transcribes speech, follows spoken instructions, and answers questions about recordings, scoring 91.4% on VoiceBench, 77.2% on MMAU, and 56.6% on Audio MC. On vision, Inkling scores 73.5% on MMMU Pro and 78.1% on CharXiv reasoning questions, rising to 82.0% when it uses a Python tool to zoom into and crop the image.
On agentic and reasoning evaluations at maximum effort, Inkling scores 77.6% on SWE-bench Verified, 54.3% on SWE-bench Pro (public), 63.8% on Terminal-Bench 2.1, 76.0% on MCP Atlas, and 45.5% on Toolathlon Verified. Reasoning results include 87.2% on GPQA Diamond, 97.1% on AIME 2026, and 29.7% on Humanity's Last Exam text-only, which rises to 46.0% with tools. Inkling scores 79.8% on IFBench for instruction following.
Controllable thinking effort is the setting you tune most. Raising effort spends more thinking tokens for higher scores, and lowering it returns answers sooner for less. Inkling reaches a given score at fewer thinking tokens than the open-weights models Thinking Machines compared it against, so sweep the effort setting across a representative slice of your traffic before you fix a value.
Inkling also aims for calibrated confidence. It hedges or says it doesn't know rather than guessing, which helps in forecasting and in any workflow where a confident wrong answer costs more than an uncertain one.
Set the model to thinkingmachines/inkling in the AI SDK, Chat Completions API, Responses API, Messages API, or other API formats, from TypeScript or Python. AI Gateway serves Inkling through Baseten, Together AI, Modal, Thinking Machines, with retries and failover, and mirrors provider pricing with no markup and no platform fee on inference, including on Bring Your Own Key (BYOK) requests.
Copy link to headingWhat To Consider When Choosing a Provider
- Configuration: Thinking Machines states plainly that Inkling is not the strongest overall model available, open or closed. Its case is breadth: one model that accepts text, images, and audio, follows instructions closely, and exposes an effort dial. Factual recall is the weakest area, with 43.9% on SimpleQA Verified, so pair Inkling with retrieval or search when answers depend on specific facts.
- Zero Data Retention: Zero Data Retention is available for this model. It is offered on a per-provider and model basis. See the documentation for details.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
Copy link to headingWhen to Use Inkling
Best for
- Multimodal Agent Backends: Text, image, and audio input handled by a single model rather than three
- Speech and Audio Reasoning: Transcription, spoken instructions, and questions over longer recordings
- Chart and Document Vision: Charts, diagrams, and visual math, with a Python tool for zooming and cropping
- Tool-Heavy Agent Workflows: Broad tool use across harnesses, with 76.0% on MCP Atlas
- Mixed Workload Consolidation: One generalist covering reasoning, coding, chat, and multimodal input
- Effort-Tuned Cost Control: Thinking effort set per request to balance answer quality against latency
Consider alternatives when
- Peak Coding Scores: Dedicated coding models post higher SWE-bench and Terminal-Bench 2.1 results
- Factual Recall Workloads: 43.9% on SimpleQA Verified means knowledge-heavy answers need a retrieval layer
- Lower Cost Per Task: Inkling Small matches or beats Inkling on many evaluations at a quarter of the size
- Text-Only Pipelines: A text-focused model fits better when image and audio input never apply
Copy link to headingConclusion
Inkling is a broad, balanced model rather than a leader on any single benchmark family. Use Inkling when one model needs to read images, listen to audio, call tools, and write code, and when you want an effort dial to control what each request spends.
Copy link to headingFrequently Asked Questions
What is Inkling designed for?
Broad generalist use. Inkling covers agentic work, reasoning, coding, instruction following, factuality, vision, and audio, rather than optimizing narrowly for one domain. Thinking Machines positions that breadth as the reason to pick it.
What input types does Inkling accept?
Text, images, and audio, with text output. Images work best with each dimension between 40px and 4096px, and audio should be WAV sampled at 16kHz. All three modalities are processed jointly by the same decoder.
How does controllable thinking effort work in Inkling?
You set an effort level per request. Higher effort spends more thinking tokens and raises scores on hard tasks; lower effort returns answers sooner. Published benchmark results for Inkling are reported at maximum effort.
How does Inkling compare to Inkling Small?
Inkling Small is roughly a quarter of the size and matches or beats Inkling on many coding, reasoning, and tool-use evaluations. Inkling stays ahead on world knowledge, scoring 43.9% on SimpleQA Verified against 20.6%, and posts slightly higher audio scores.
How did Inkling score on agentic coding benchmarks?
77.6% on SWE-bench Verified, 54.3% on SWE-bench Pro (public), and 63.8% on Terminal-Bench 2.1, all at maximum effort. On general agentic evaluations, Inkling scores 76.0% on MCP Atlas and 45.5% on Toolathlon Verified.
How good is Inkling at audio and vision tasks?
Audio is among its stronger areas, with 91.4% on VoiceBench, 77.2% on MMAU, and 56.6% on Audio MC. On vision, Inkling scores 73.5% on MMMU Pro and 78.1% on CharXiv reasoning questions, or 82.0% when it uses a Python tool to inspect the image.
What context window does Inkling support?
A context window of 262.1K tokens. That covers long documents, extended tool-calling sessions, and conversations that mix text with images and audio.
Are the weights for Inkling open?
Yes. Thinking Machines released Inkling with open weights under the Apache 2.0 license, which permits commercial use and modification.
Does AI Gateway support Zero Data Retention for Inkling?
Yes, Zero Data Retention is available for this model. Zero Data Retention is offered on a per-provider basis. See https://vercel.com/docs/ai-gateway/capabilities/zdr for details.
How do I call Inkling through AI Gateway?
Set the model to
thinkingmachines/inklingin the AI SDK, Chat Completions API, Responses API, Messages API, or other API formats, from TypeScript or Python. AI Gateway handles authentication, retries, and failover acrossbaseten,togetherai,modal,thinkingmachines.
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