AI21 Labs vs Deep Infra: Token Pricing, Speed & Intelligence
Full comparison of AI21 Labs and Deep Infra — 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.
Deep Infra
Ultra-cheap open-weight inference — Llama 4, DeepSeek V4 Pro, Qwen3 at rock-bottom prices
Deep Infra is an inference-focused API provider that consistently offers the lowest per-token prices for open-weight models. The 2026 catalog includes Llama 4 Scout/Maverick, DeepSeek V4 Pro, Qwen3-235B, Kimi K3, and the full Llama 3.x family. Deep Infra is the go-to choice for cost-sensitive production inference where price is the primary constraint.
Key metric comparison
—
—
—
—
—
—
—
—
—
—
—
—
Live token pricing
Strengths and weaknesses
AI21 Labs
Deep Infra
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.
The most price-competitive inference API for open-weight models — Llama 4, DeepSeek V4 Pro, and Qwen3 at prices 30–50% below comparable providers, with zero migration friction via OpenAI-compatible API.
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.
Deep Infra FAQs
What models does Deep Infra support in 2026?
Deep Infra hosts Llama 4 Scout and Maverick, DeepSeek V4 Pro, Qwen3-235B, Kimi K3, the full Llama 3.x family, Mistral, Mixtral, and many others. The catalog is updated rapidly as new open-weight models are released.
How cheap is Deep Infra compared to other providers?
Deep Infra is consistently among the more affordable providers for open-weight models. Check deepinfra.com/pricing for current rates on Llama 3.1 8B, Llama 3.3 70B, and other models.
Is Deep Infra OpenAI-compatible?
Yes. Deep Infra provides an OpenAI-compatible API, so you can use the OpenAI SDK by pointing it at the Deep Infra endpoint. This makes migration from OpenAI or other providers straightforward.
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.
Deep Infra — Ultra-cheap open-weight inference — Llama 4, DeepSeek V4 Pro, Qwen3 at rock-bottom prices
Deep Infra is an inference-focused API provider that consistently offers the lowest per-token prices for open-weight models. The 2026 catalog includes Llama 4 Scout/Maverick, DeepSeek V4 Pro, Qwen3-235B, Kimi K3, and the full Llama 3.x family. Deep Infra is the go-to choice for cost-sensitive production inference where price is the primary constraint.
The most price-competitive inference API for open-weight models — Llama 4, DeepSeek V4 Pro, and Qwen3 at prices 30–50% below comparable providers, with zero migration friction via OpenAI-compatible API.
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
Deep Infra
- Consistently lowest prices for open-weight models — often 30–50% below competitors
- 2026 catalog: Llama 4, DeepSeek V4 Pro, Qwen3-235B, Kimi K3
- Wide model catalog updated rapidly as new models release
- No proprietary models — open-weight only
- No enterprise SLAs or dedicated support
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
Deep Infra
- No proprietary models — open-weight only
- No enterprise SLAs or dedicated support
- Smaller ecosystem than Together AI or Fireworks
Provider category context
AI21 Labs is a frontier lab, founded in 2017. Deep Infra is a inference api, founded in 2023. AI21 Labs as a frontier lab trains and serves its own proprietary models. Deep Infra 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 Deep Infra if you need consistently lowest prices for open-weight models — often 30–50% below competitors. 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.