AI21 Labs vs OctoAI: Token Pricing, Speed & Intelligence
Full comparison of AI21 Labs and OctoAI — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
AI21 Labs
Jamba 1.6 — hybrid SSM-Transformer models with 256K context
AI21 Labs develops the Jamba model family, which uses a hybrid SSM (Mamba) and Transformer architecture. Jamba 1.6 Large and Mini offer 256K token context windows at competitive prices. The hybrid architecture delivers faster inference and lower memory usage than pure-Transformer models of comparable size.
OctoAI
Acquired by NVIDIA — OctoAI inference is now available via NVIDIA NIM
OctoAI was acquired by NVIDIA in 2024 and its inference platform has been integrated into NVIDIA NIM (Inference Microservices). Existing OctoAI customers have been migrated to NVIDIA NIM, which provides the same optimised open-weight model inference on NVIDIA GPU infrastructure with TensorRT-LLM acceleration. New users should use NVIDIA NIM directly for access to Llama 4, Mistral, Qwen3, and other open-weight models.
Key metric comparison
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Live token pricing
Strengths and weaknesses
AI21 Labs
OctoAI
Key differentiators
Jamba's hybrid SSM-Transformer architecture enables 256K context at lower memory and latency cost than pure-Transformer models — with Jamba 1.6 Mini among the cheapest 256K-context options available.
OctoAI's inference technology lives on inside NVIDIA NIM — the same hardware-aware optimisation, now with TensorRT-LLM acceleration and NVIDIA's global GPU infrastructure behind it.
Frequently asked questions
AI21 Labs FAQs
What is the Jamba architecture?
Jamba uses a hybrid of Mamba (SSM) and Transformer layers. This reduces memory usage and improves throughput on long sequences compared to pure-Transformer models, while maintaining strong reasoning performance.
How much does AI21 Labs API cost?
Jamba 1.6 Large is $2.00/$8.00 per 1M input/output tokens. Jamba 1.6 Mini is $0.20/$0.40 per 1M tokens. Both support 256K context. See ai21.com/pricing for current rates.
What context window does Jamba 1.6 support?
Both Jamba 1.6 Large and Jamba 1.6 Mini support 256K token context windows, making them suitable for long-document analysis and extended conversations.
OctoAI FAQs
What happened to OctoAI?
OctoAI was acquired by NVIDIA in 2024. Its inference platform has been integrated into NVIDIA NIM (Inference Microservices). Existing customers were migrated to NIM, which provides the same optimised open-weight model inference on NVIDIA GPU infrastructure.
Can I still use the OctoAI API?
The original OctoAI API endpoints have been deprecated. Existing OctoAI customers should migrate to NVIDIA NIM, which provides equivalent functionality with TensorRT-LLM optimisation and access to the full NVIDIA model catalog.
What is NVIDIA NIM?
NVIDIA NIM (Inference Microservices) are containerized AI inference packages optimized for NVIDIA GPUs using TensorRT-LLM. They can be accessed via the NVIDIA API catalog at build.nvidia.com or self-hosted on any NVIDIA GPU infrastructure.
Provider resources
AI21 Labs — Jamba 1.6 — hybrid SSM-Transformer models with 256K context
AI21 Labs develops the Jamba model family, which uses a hybrid SSM (Mamba) and Transformer architecture. Jamba 1.6 Large and Mini offer 256K token context windows at competitive prices. The hybrid architecture delivers faster inference and lower memory usage than pure-Transformer models of comparable size.
Jamba's hybrid SSM-Transformer architecture enables 256K context at lower memory and latency cost than pure-Transformer models — with Jamba 1.6 Mini among the cheapest 256K-context options available.
OctoAI — Acquired by NVIDIA — OctoAI inference is now available via NVIDIA NIM
OctoAI was acquired by NVIDIA in 2024 and its inference platform has been integrated into NVIDIA NIM (Inference Microservices). Existing OctoAI customers have been migrated to NVIDIA NIM, which provides the same optimised open-weight model inference on NVIDIA GPU infrastructure with TensorRT-LLM acceleration. New users should use NVIDIA NIM directly for access to Llama 4, Mistral, Qwen3, and other open-weight models.
OctoAI's inference technology lives on inside NVIDIA NIM — the same hardware-aware optimisation, now with TensorRT-LLM acceleration and NVIDIA's global GPU infrastructure behind it.
Key strengths compared
AI21 Labs
- Hybrid SSM-Transformer architecture delivers faster inference than pure-Transformer models
- 256K token context window on both Jamba 1.6 Large and Mini
- Jamba 1.6 Mini at $0.20/$0.40 per 1M tokens is highly cost-competitive
- Smaller model family than frontier labs — two models vs dozens at OpenAI or Anthropic
- Less ecosystem support than OpenAI-compatible providers
OctoAI
- Now backed by NVIDIA's full GPU infrastructure and TensorRT-LLM stack
- Seamless migration path for existing OctoAI customers to NVIDIA NIM
- Access to NVIDIA's full open-weight model catalog via NIM
- OctoAI as a standalone product no longer exists — new users should use NVIDIA NIM directly
- Migration required for any OctoAI-specific API integrations
Key weaknesses compared
AI21 Labs
- Smaller model family than frontier labs — two models vs dozens at OpenAI or Anthropic
- Less ecosystem support than OpenAI-compatible providers
- No image, audio, or multimodal models
OctoAI
- OctoAI as a standalone product no longer exists — new users should use NVIDIA NIM directly
- Migration required for any OctoAI-specific API integrations
- Self-hosted path requires NVIDIA GPU infrastructure investment
Provider category context
AI21 Labs is a frontier lab, founded in 2017. OctoAI is a inference api, founded in 2022. AI21 Labs as a frontier lab trains and serves its own proprietary models. OctoAI as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers but without access to proprietary frontier models.
How to choose between them
Choose AI21 Labs if you need Long-context tasks. Choose OctoAI if you need now backed by nvidia's full gpu infrastructure and tensorrt-llm stack. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.