modelparams.dev
API GitHub

Providers

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

Providers

Alibaba Anthropic Cohere DeepSeek Google Meta MiniMax Mistral Moonshot AI Nvidia OpenAI Perplexity xAI Xiaomi Z.ai
Meta 7 params

Meta Llama 3.3 70B Instruct parameters

These are the parameters modelparams.dev tracks for Meta Llama 3.3 70B Instruct. 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 completion tokens
max_completion_tokens 		integer (1…+∞) — Maximum number of output tokens the model may generate. —
Sampling 4 params
Parameter Type Default Description Condition
Temperature
temperature 		number — Controls randomness. Lower values make outputs more focused; higher values make them more varied. —
Top P
top_p 		number — Controls nucleus sampling by limiting generation to tokens within the selected cumulative probability. —
Top K
top_k 		integer — Limits generation to the selected number of highest-probability tokens. —
Repetition penalty
repetition_penalty 		number — Penalizes tokens that have already appeared to reduce repetition in the output. —
Tools 1 param
Parameter Type Default Description Condition
Tool choice
tool_choice 		enum (auto | none | required) — Controls whether the model may call tools, must call one, or skips tool calls. —
Output 1 param
Parameter Type Default Description Condition
Response format
response_format.type 		enum (text | json_schema) "text" Controls whether the model returns normal text or a schema-constrained JSON object. —

Meta Llama 3.3 70B Instruct API parameters in brief

Meta Llama 3.3 70B Instruct documents 7 API parameters, grouped by what they control:

Frequently asked questions

How many parameters does Meta Llama 3.3 70B Instruct accept?
Meta Llama 3.3 70B Instruct accepts 7 API parameters: max_completion_tokens, temperature, top_p, top_k, repetition_penalty, response_format.type, and more.

Resources

All Meta models Glossary Full catalog

Llama 3.3 70B Instruct — JSON

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

/api/v1/models/meta/Llama-3.3-70B-Instruct.json 
{
  "$schema": "https://modelparams.dev/api/v1/schema.json",
  "provider": "meta",
  "authType": "api_key",
  "model": "Llama-3.3-70B-Instruct",
  "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": "temperature",
      "label": "Temperature",
      "description": "Controls randomness. Lower values make outputs more focused; higher values make them more varied.",
      "group": "sampling",
      "type": "number"
    },
    {
      "path": "top_p",
      "label": "Top P",
      "description": "Controls nucleus sampling by limiting generation to tokens within the selected cumulative probability.",
      "group": "sampling",
      "type": "number"
    },
    {
      "path": "top_k",
      "label": "Top K",
      "description": "Limits generation to the selected number of highest-probability tokens.",
      "group": "sampling",
      "type": "integer"
    },
    {
      "path": "repetition_penalty",
      "label": "Repetition penalty",
      "description": "Penalizes tokens that have already appeared to reduce repetition in the output.",
      "group": "sampling",
      "type": "number"
    },
    {
      "path": "response_format.type",
      "label": "Response format",
      "description": "Controls whether the model returns normal text or a schema-constrained JSON object.",
      "group": "output_format",
      "type": "enum",
      "default": "text",
      "values": [
        "text",
        "json_schema"
      ]
    },
    {
      "path": "tool_choice",
      "label": "Tool choice",
      "description": "Controls whether the model may call tools, must call one, or skips tool calls.",
      "group": "tooling",
      "type": "enum",
      "values": [
        "auto",
        "none",
        "required"
      ]
    }
  ]
}

Other Meta models

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

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