modelparams.dev
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Providers

Alibaba Anthropic Cohere DeepSeek Google Meta MiniMax Mistral Moonshot AI Nvidia OpenAI Perplexity Thinking Machines xAI Xiaomi Z.ai

Providers

Alibaba Anthropic Cohere DeepSeek Google Meta MiniMax Mistral Moonshot AI Nvidia OpenAI Perplexity Thinking Machines xAI Xiaomi Z.ai
Thinking Machines 5 params

Thinking Machines Inkling parameters

These are the parameters modelparams.dev tracks for Thinking Machines Inkling. Each row gives the type, default, valid range or values, and the conditions that gate it. It's the same data the JSON API serves.

Length 1 param
Parameter Type Default Description Condition
Max tokens
max_tokens 		integer (1…+∞) — Maximum number of output tokens the model may generate. —
Sampling 2 params
Parameter Type Default Description Condition
Temperature
temperature 		number 1 Controls randomness. Lower values make outputs more focused; higher values make them more varied. —
Top P
top_p 		number (0…1) 1 Controls nucleus sampling by limiting generation to tokens within the selected cumulative probability. —
Reasoning 2 params
Parameter Type Default Description Condition
Reasoning effort
reasoning_effort 		enum (none | minimal | low | medium | high | xhigh) "high" Controls how much thinking Inkling performs before answering. Accepts a preset name or a number between 0.0 and 0.99; presets map to numeric effort levels (none=0.0, minimal=0.1, low=0.2, medium=0.7, high=0.9, xhigh=0.99). —
Separate reasoning
separate_reasoning 		boolean true Returns the model's reasoning in a dedicated reasoning_content field instead of interleaving it with the final message content. —

Thinking Machines Inkling API parameters in brief

Thinking Machines Inkling documents 5 API parameters, grouped by what they control:

Frequently asked questions

How many parameters does Thinking Machines Inkling accept?
Thinking Machines Inkling accepts 5 API parameters: max_tokens, temperature, top_p, reasoning_effort, separate_reasoning.
What is the default temperature for Thinking Machines Inkling?
The default temperature for Thinking Machines Inkling is 1.
What is the default top_p for Thinking Machines Inkling?
The default top_p for Thinking Machines Inkling is 1, within a valid range of 0 to 1.

Resources

All Thinking Machines models Glossary Full catalog

Inkling — JSON

The full model definition as served by the API. Copy it or open the endpoint directly.

/api/v1/models/thinking-machines/Inkling.json 
{
  "$schema": "https://modelparams.dev/api/v1/schema.json",
  "provider": "thinking-machines",
  "authType": "api_key",
  "model": "Inkling",
  "params": [
    {
      "path": "max_tokens",
      "label": "Max tokens",
      "description": "Maximum number of output tokens the model may generate.",
      "group": "generation_length",
      "type": "integer",
      "range": {
        "min": 1
      }
    },
    {
      "path": "temperature",
      "label": "Temperature",
      "description": "Controls randomness. Lower values make outputs more focused; higher values make them more varied.",
      "group": "sampling",
      "type": "number",
      "default": 1
    },
    {
      "path": "top_p",
      "label": "Top P",
      "description": "Controls nucleus sampling by limiting generation to tokens within the selected cumulative probability.",
      "group": "sampling",
      "type": "number",
      "default": 1,
      "range": {
        "min": 0,
        "max": 1
      }
    },
    {
      "path": "reasoning_effort",
      "label": "Reasoning effort",
      "description": "Controls how much thinking Inkling performs before answering. Accepts a preset name or a number between 0.0 and 0.99; presets map to numeric effort levels (none=0.0, minimal=0.1, low=0.2, medium=0.7, high=0.9, xhigh=0.99).",
      "group": "reasoning",
      "type": "enum",
      "default": "high",
      "values": [
        "none",
        "minimal",
        "low",
        "medium",
        "high",
        "xhigh"
      ]
    },
    {
      "path": "separate_reasoning",
      "label": "Separate reasoning",
      "description": "Returns the model's reasoning in a dedicated reasoning_content field instead of interleaving it with the final message content.",
      "group": "reasoning",
      "type": "boolean",
      "default": true
    }
  ]
}

How to use

Building with an AI agent? Hit Copy to grab this whole guide as Markdown and paste it in — or point your agent straight at /llms.txt.

modelparams.dev is an open, community-maintained catalog of model parameters. Each entry shows the knobs you can turn — type, default, range, and the conditions that gate it.

The same model accessed via an API key and via a subscription usually exposes a different set of parameters. We list both as separate entries so the data stays honest.

Catalog API

The full catalog is static JSON, CORS-enabled, served from the edge.

curl https://modelparams.dev/api/v1/models.json

Each entry is keyed by provider/model for API-key variants; subscription variants append -subscription.

If you only need the params for one model contract, use the providerless endpoint. Subscription contracts are model slugs with -subscription. 

curl https://modelparams.dev/api/v1/params/gpt-5.5.json
curl https://modelparams.dev/api/v1/params/gpt-5.5-subscription.json

Single model

curl https://modelparams.dev/api/v1/models/anthropic/claude-opus-4-7.json
curl https://modelparams.dev/api/v1/models/anthropic/claude-opus-4-7-subscription.json

JSON Schema

Every entry validates against a JSON Schema you can use in your editor or pipeline. 

curl https://modelparams.dev/api/v1/schema.json

Add this header to any YAML you author for autocomplete in VS Code: 

# yaml-language-server: $schema=https://modelparams.dev/api/v1/schema.json

Logos

Provider logos are available at /assets/logos/{provider}.svg where {provider} is the provider slug. They use currentColor so they inherit your text color. 

curl https://modelparams.dev/assets/logos/anthropic.svg

Logos are sourced from the models.dev repo (MIT) and used under nominative fair use.

Contribute

The data lives in YAML under models/{provider}/{model}-{auth}.yaml in the GitHub repo. Open a PR; CI validates against the schema and rebuilds.

Edit on GitHub MIT licensed