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Alibaba Anthropic Bedrock Cerebras Cohere DeepSeek Fireworks Google Groq Meta MiniMax Mistral Moonshot AI NVIDIA OpenAI Opencode Go Perplexity Thinking Machines Vertex xAI Xiaomi Z.ai

Providers

Alibaba Anthropic Bedrock Cerebras Cohere DeepSeek Fireworks Google Groq Meta MiniMax Mistral Moonshot AI NVIDIA OpenAI Opencode Go Perplexity Thinking Machines Vertex xAI Xiaomi Z.ai
Meta Chat Completions 9 params

Meta Muse Spark 1.1 API parameters

These are the API parameters modelparams.dev tracks for Meta Muse Spark 1.1 on Chat Completions with an API key — the settings you send in a request. 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.

After the parameter count instead — how many weights Muse Spark 1.1 has? That's a different number, and we don't track it. Here's the difference.

Length 1 param
Parameter Type Default Description Condition
Max completion tokens
max_completion_tokens 		integer (1…+∞) — Maximum number of output tokens the model may generate. —
Sampling 5 params
Parameter Type Default Description Condition
Temperature
temperature 		number (0…2 step 0.1) 1 Controls randomness; use this or Top P, but not both.
Not when top_p ≠ 1
Top P
top_p 		number (0.01…1 step 0.01) 1 Controls nucleus sampling; use this or Temperature, but not both.
Not when temperature ≠ 1
Frequency penalty
frequency_penalty 		number (-2…2 step 0.1) 0 Penalizes tokens in proportion to how often they have appeared, reducing repetition. —
Presence penalty
presence_penalty 		number (-2…2 step 0.1) 0 Penalizes tokens that have already appeared, encouraging new topics. —
Random seed
seed 		integer — Requests best-effort deterministic sampling for repeated requests. —
Reasoning 1 param
Parameter Type Default Description Condition
Reasoning effort
reasoning_effort 		enum (minimal | low | medium | high | xhigh) — Controls how much reasoning the model should perform before producing an answer. —
Output 1 param
Parameter Type Default Description Condition
Response format
response_format.type 		enum (text | json_object | json_schema) "text" Controls whether the model returns text, JSON, or schema-constrained JSON. —
Metadata 1 param
Parameter Type Default Description Condition
Prompt cache retention
prompt_cache_retention 		enum (in_memory | 24h) — Controls whether the prompt cache stays in memory or persists for up to 24 hours. —

Meta Muse Spark 1.1 API parameters in brief

Meta Muse Spark 1.1 on Chat Completions documents 9 API parameters, grouped by what they control:

Frequently asked questions

Which API parameters does Meta Muse Spark 1.1 support?
Meta Muse Spark 1.1 accepts 9 API parameters in the request body: max_completion_tokens, reasoning_effort, temperature, top_p, frequency_penalty, presence_penalty, and more.
What is the default temperature for Meta Muse Spark 1.1?
The default temperature for Meta Muse Spark 1.1 is 1, within a valid range of 0 to 2.
What is the default top_p for Meta Muse Spark 1.1?
The default top_p for Meta Muse Spark 1.1 is 1, within a valid range of 0.01 to 1.

Resources

All Meta models Glossary Full catalog

Muse Spark 1.1 — JSON

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

/api/v1/models/meta/muse-spark-1.1.json 
{
  "$schema": "https://modelparams.dev/api/v1/schema.json",
  "provider": "meta",
  "authType": "api_key",
  "apiSurface": "openai-chat-completions",
  "model": "muse-spark-1.1",
  "params": [
    {
      "path": "max_completion_tokens",
      "label": "Max tokens",
      "description": "Maximum number of output tokens the model may generate.",
      "group": "generation_length",
      "type": "integer",
      "range": {
        "min": 1
      }
    },
    {
      "path": "reasoning_effort",
      "label": "Reasoning effort",
      "description": "Controls how much reasoning the model should perform before producing an answer.",
      "group": "reasoning",
      "type": "enum",
      "values": [
        "minimal",
        "low",
        "medium",
        "high",
        "xhigh"
      ]
    },
    {
      "path": "temperature",
      "label": "Temperature",
      "description": "Controls randomness; use this or Top P, but not both.",
      "group": "sampling",
      "applicability": {
        "except": {
          "top_p": {
            "not": 1
          }
        }
      },
      "type": "number",
      "default": 1,
      "range": {
        "min": 0,
        "max": 2,
        "step": 0.1
      }
    },
    {
      "path": "top_p",
      "label": "Top P",
      "description": "Controls nucleus sampling; use this or Temperature, but not both.",
      "group": "sampling",
      "applicability": {
        "except": {
          "temperature": {
            "not": 1
          }
        }
      },
      "type": "number",
      "default": 1,
      "range": {
        "min": 0.01,
        "max": 1,
        "step": 0.01
      }
    },
    {
      "path": "frequency_penalty",
      "label": "Frequency penalty",
      "description": "Penalizes tokens in proportion to how often they have appeared, reducing repetition.",
      "group": "sampling",
      "type": "number",
      "default": 0,
      "range": {
        "min": -2,
        "max": 2,
        "step": 0.1
      }
    },
    {
      "path": "presence_penalty",
      "label": "Presence penalty",
      "description": "Penalizes tokens that have already appeared, encouraging new topics.",
      "group": "sampling",
      "type": "number",
      "default": 0,
      "range": {
        "min": -2,
        "max": 2,
        "step": 0.1
      }
    },
    {
      "path": "seed",
      "label": "Random seed",
      "description": "Requests best-effort deterministic sampling for repeated requests.",
      "group": "sampling",
      "type": "integer"
    },
    {
      "path": "response_format.type",
      "label": "Response format",
      "description": "Controls whether the model returns text, JSON, or schema-constrained JSON.",
      "group": "output_format",
      "type": "enum",
      "default": "text",
      "values": [
        "text",
        "json_object",
        "json_schema"
      ]
    },
    {
      "path": "prompt_cache_retention",
      "label": "Prompt cache retention",
      "description": "Controls whether the prompt cache stays in memory or persists for up to 24 hours.",
      "group": "provider_metadata",
      "type": "enum",
      "values": [
        "in_memory",
        "24h"
      ]
    }
  ]
}

Other Meta models

Llama 3.3 70B Instruct 7 params View Llama 3.3 8B Instruct 7 params View Llama 4 Maverick 17B 128E Instruct FP8 7 params View Llama 4 Scout 17B 16E Instruct FP8 7 params View Muse Spark 1.2 Contributor 9 params View

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/models/openai/gpt-5.5.json
curl https://modelparams.dev/api/v1/models/openai/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