Meta model
Llama 4 Maverick
Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward...
JSON modeLong contextStreamingLow costVision
Best use cases
- open-model workflows
- cost-sensitive long context
- classification
OpenAI-compatible code example
Keep the OpenAI SDK style, set base_url to NextModel, and use the catalog model ID llama-4-maverick.
Python
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://api.nextmodel.app/v1"
)
resp = client.chat.completions.create(
model="llama-4-maverick",
messages=[{"role": "user", "content": "Hello from NextModel"}]
)
print(resp.choices[0].message.content)Similar alternatives
GoogleCatalog
Gemini 2.5 Flash is a lower-cost long-context and vision candidate for teams that need multimodal coverage without always using a premium model.
Best forlong-document summarization, image Q&A, fast multimodal routing
Routingconfigured
Tool callingVisionJSON modeLong context
OpenRouter if availableOpenRouter public Models API when available; curated fallback otherwise
OpenRouterCatalog
GPT-4o mini is a mature low-cost multimodal option for teams that already use OpenAI-compatible SDKs and need a balanced default model for product workflows.
Best forlow-cost chat, image understanding, classification
Routingconfigured
Tool callingVisionJSON modeLong context
OpenRouter if availableOpenRouter public Models API when available; curated fallback otherwise
Moonshot AICatalog
Kimi K2.6 is a long-context Chinese model candidate for document-heavy teams comparing cost, context length, and domestic model coverage.
Best forlong Chinese documents, contract review, knowledge-base Q&A
Routingconfigured
JSON modeLong contextStreaming
OpenRouter if availableOpenRouter public Models API when available; curated fallback otherwise
FAQ
Meta: Llama 4 Maverick API questions
Why include Llama 4 Maverick?
It gives teams an open-model candidate when comparing cost, context length, and provider optionality.