AI21 Jamba 1.5 Mini
Cost-efficient hybrid Mamba-Transformer model with 256k context. Tuned for high-throughput RAG.
AI21 Jamba 1.5 Mini is text & chat AI model from Custom, priced at €0.200 per 1M input tokens with a 256K tokens context window.
0.7
Pricing
API Integration
Use our OpenAI-compatible API to integrate AI21 Jamba 1.5 Mini into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
// Simple — just pass a string
const reply = await rw.run("jamba-1-5-mini", "Hello! What can you do?");
console.log(reply);
// With message history
const reply2 = await rw.run("jamba-1-5-mini", [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Explain quantum computing simply." },
]);
console.log(reply2);
// Full response with usage info
const res = await rw.chat("jamba-1-5-mini", [
{ role: "user", content: "Hello!" },
], { temperature: 0.7, max_tokens: 500 });
console.log(res.choices[0].message.content);
console.log(res.usage);Deep dive — AI21 Labs's AI21 Jamba 1.5 Mini
AI21 Labs is an Israeli LLM pioneer founded in 2017 by Yoav Shoham (Stanford emeritus), Ori Goshen and Amnon Shashua (Mobileye founder). Long active in commercial LLMs (Jurassic-1, Jurassic-2), AI21 pioneered the hybrid State-Space + Transformer Jamba architecture in March 2024. Jamba 1.5 Mini is the small-and-fast variant of the August 2024 Jamba 1.5 release, optimised for long-context inference at production cost points and tractable on a single H100 80GB. AI21 has raised over $336M from investors including Google, Nvidia, Walden Catalyst and Pitango.
Visit AI21 Labs →Jamba 1.5 Mini uses the same hybrid SSM+Transformer+MoE recipe as Jamba 1.5 Large at smaller scale. Across 32 blocks each block alternates Mamba (selective state-space) and self-attention layers in a 7:1 ratio. MLPs are MoE with 16 experts and top-2 routing, giving 12B active parameters out of 52B total. The 12B active count means inference cost is competitive with dense 12B models, while Mamba layers provide constant-memory long-context scaling. The model uses a 64,000-token BPE tokeniser and supports the same nine languages as Jamba 1.5 Large (English, Spanish, French, Portuguese, Italian, Dutch, German, Arabic, Hebrew). Released August 2024 under the Jamba Open Model License with hosted access via AI21 Studio, AWS Bedrock, Azure AI Studio and Snowflake Cortex.
- Parameters
- 52B total, 12B active per token (16 experts, top-2 routing)
- Context
- 256K tokens
- Hybrid Mamba+Transformer+MoE architecture at compact scale
- 52B total / 12B active parameters
- 256K effective context
- Fits on a single H100 80GB at FP16 or A100 at INT8
- Native function calling and JSON-mode output
- Multilingual (9 languages)
- Open weights under Jamba Open Model License
- Best for: cheap long-context summarisation, RAG with large retrieval windows, single-GPU enterprise pilots.
Same data mixture and methodology as Jamba 1.5 Large: trillions of tokens of web, code, math, books and multilingual sources, knowledge cutoff March 2024, followed by supervised fine-tuning and preference optimisation.
License: Jamba Open Model License. Permits research and commercial use with attribution and AUP compliance. Hosted access via AI21 Studio, AWS Bedrock, Azure AI Studio and Snowflake Cortex.
Known limitations
- Lower quality than Jamba 1.5 Large or Mixtral 8x7B Instruct on hard reasoning
- No vision modality
- Limited community ecosystem — fewer inference engines support hybrid Mamba+attention
- Behind dense 70B-class instructs on benchmark depth
- Multilingual coverage narrower than Command R, Aya or Mistral Large
Frequently asked questions
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