Changelog

AddedCompound Beta Search Settings

Groq's Compound Beta and Compound Beta Mini agentic tool systems now support domain-based search filtering through two new parameters: exclude_domains and include_domains.

• exclude_domains allows you to specify domains that should be omitted from web search results.
• include_domains lets you limit web searches to only return results from specified domains.

Example usage to exclude Wikipedia from searches:

curl "https://api.groq.com/openai/v1/chat/completions" \
  -X POST \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${GROQ_API_KEY}" \
  -d '{
         "messages": [
           {
             "role": "user",
             "content": "Tell me about the history of Bonsai trees in America"
           }
         ],
         "model": "compound-beta-mini",
         "exclude_domains": ["wikipedia.org"]
       }'

Learn more about search settings in our docs, including advanced usage with domain wildcards.

ChangedPython SDK v0.24.0, TypeScript SDK v0.21.0

The Python SDK has been updated to v0.24.0 and the Typescript SDK has been updated to v0.21.0.

Key Changes:

• Added support for domain filtering in Compound Beta search settings. Use include_domains to restrict searches to specific domains, or exclude_domains to omit results from certain domains when using compound-beta or compound-beta-mini models.

ChangedPython SDK v0.23.0, TypeScript SDK v0.20.0

The Python SDK has been updated to v0.23.0 and the Typescript SDK has been updated to v0.20.0.

Key Changes:

• groq.files.content returns a Response object now to allow parsing as text (for jsonl files) or blob for generic file types. Previously, the return type as a JSON object was incorrect, and this caused the SDK to encounter an error instead of returning the file's contents. Example usage in Typescript:

1const response = await groq.files.content("file_XXXX");
2const file_text = await response.text();

• BatchCreateParams now accepts a string as input to completion_window to allow for durations between 24h and 7d. Using a longer completion window gives your batch job a greater chance of completing successfully without timing out. For larger batch requests, it's recommended to split them up into multiple batch jobs. Learn more about best practices for batch processing.
• Updated chat completion model parameter to remove deprecated models and add newer production models.
  • Removed: gemma-7b-it and mixtral-8x7b-32768.
  • Added: gemma2-9b-it, llama-3.3-70b-versatile, llama-3.1-8b-instant, and llama-guard-3-8b.
  • For the most up-to-date information on Groq's models, see the models page, or learn more about our deprecations policy.
• Added optional chat completion metadata parameter for better compatibility with OpenAI chat completion API. Learn more about switching from OpenAI to Groq.

AddedCompound Beta and Compound Beta Mini Systems

Compound Beta and Compound Beta Mini are agentic tool systems with web search and code execution built in. These systems simplify your workflow when interacting with realtime data and eliminate the need to add your own tools to search the web. Read more about agentic tooling on Groq, or start using them today by switching to compound-beta or compound-beta-mini.

Performance:

• Compound Beta (compound-beta): 350 tokens per second (TPS) with a latency of ~4,900 ms
• Compound Beta Mini (compound-beta-mini): 275 TPS with a latency of ~1,600 ms

Example Usage:

curl "https://api.groq.com/openai/v1/chat/completions" \
  -X POST \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${GROQ_API_KEY}" \
  -d '{
         "messages": [
           {
             "role": "user",
             "content": "what happened in ai this week?"
           }
         ],
         "model": "compound-beta",
       }'

AddedMeta Llama 4 Support

Meta's Llama 4 Scout (17Bx16MoE) and Maverick (17Bx128E) models for image understanding and text generation are now available through Groq API with support for a 128K token context window, image input up to 5 images, function calling/tool use, and JSON mode. Read more in our tool use and vision docs.

Performance (as benchmarked by AA):

• Llama 4 Scout (meta-llama/llama-4-scout-17b-16e-instruct): Currently 607 tokens per second (TPS)
• Llama 4 Maverick (meta-llama/llama-4-maverick-17b-128e-instruct): Currently 297 TPS

Example Usage:

curl "https://api.groq.com/openai/v1/chat/completions" \
  -X POST \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${GROQ_API_KEY}" \
  -d '{
         "messages": [
           {
             "role": "user",
             "content": "why is fast inference crucial for ai apps?"
           }
         ],
         "model": "meta-llama/llama-4-maverick-17b-128e-instruct",
       }'