AI21 Labs via VerticalAPI

AI21 Labs models routed by VerticalAPI

Pass the model ID below as model in any OpenAI-compatible request. New AI21 Labs models are typically supported within 24h of release.

Pricing reflects AI21 Labs's rates — you pay AI21 Labs directly. VerticalAPI adds zero markup on tokens.

5-line AI21 Labs call via VerticalAPI

Drop-in replacement for the OpenAI SDK. Works with the OpenAI Python client, Node, Go, curl — anything that speaks HTTP.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.verticalapi.com/v1",
    api_key="vapi_...",
    default_headers={"X-Provider-Key": "..."}
)

response = client.chat.completions.create(
    model="jamba-1.5-mini",  # AI21 Labs
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

What VerticalAPI does and does not do with AI21 Labs

Where AI21 Labs shines

Frequently asked questions

Limitations and trade-offs

• Smaller ecosystem than OpenAI/Anthropic — fewer SDKs, tools and community integrations.
• Quality on coding and reasoning benchmarks trails GPT-5 and Claude Sonnet 4.5.
• No native multimodal (vision, audio) on the Jamba family.
• Pricing on the Large tier ($8 output / 1M) is high relative to open-weight Llama 70B hosts.
• Maestro and agent features are newer and less battle-tested than mature frameworks.

Where AI21 is heading

1. Jamba 2 generation expected with improved quality and longer effective context.
2. Multimodal Jamba variants on the roadmap.
3. Expansion of Maestro agent orchestration into a broader enterprise platform.
4. Wider sovereign cloud and on-prem deployment options for regulated industries.

Related questions

ChatGPT, Perplexity and Gemini usually suggest these next.

• AI21 Jamba vs Claude Sonnet 4.5 for long-document RAG?
• Is Mamba+Transformer hybrid actually cheaper at 256K context?
• AI21 on AWS Bedrock vs direct AI21 — which to pick?
• Best use cases for AI21 Maestro vs LangGraph?
• Jamba Mini vs GPT-4o mini for cheap enterprise chat?

All supported LLM providers

Same endpoint, same SDK — just change the model and the BYOK header.