AI21 Labs vs Groq: Token Pricing, Speed & Intelligence
Full comparison of AI21 Labs and Groq — 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.
Groq
LPU + LPX inference — high-throughput tokens per second, $350M Series A
Groq runs custom Language Processing Units (LPUs) and the newer LPX chips that work alongside NVIDIA next-generation GPUs for high-throughput inference. Groq raised a $350M Series A in 2026 and is building hundreds of megawatts of capacity. Check listed rates and provider documentation for current model availability and throughput figures.
Key metric comparison
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Live token pricing
Strengths and weaknesses
AI21 Labs
Groq
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.
Groq's custom LPU/LPX chips deliver high-throughput inference significantly faster than conventional GPU systems, backed by $350M in Series A funding and hundreds of megawatts of planned capacity.
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.
Groq FAQs
How fast is Groq inference?
Groq delivers 750+ tokens/second on Llama 3.3 70B and 1,200+ tokens/second on Llama 3.1 8B using custom LPU/LPX chips, making it well-suited for real-time and low-latency applications.
What is Groq's LPX chip?
The LPX is Groq's next-generation inference chip, introduced alongside the existing LPU lineup. It expands Groq's capacity and enables even higher throughput for large-scale deployments.
How much does Groq cost?
Groq uses per-token pricing across its model catalog. Check console.groq.com/docs/openai for current rates on Llama 3.3 70B, Llama 3.1 8B, and other models.
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.
Groq — LPU + LPX inference — high-throughput tokens per second, $350M Series A
Groq runs custom Language Processing Units (LPUs) and the newer LPX chips that work alongside NVIDIA next-generation GPUs for high-throughput inference. Groq raised a $350M Series A in 2026 and is building hundreds of megawatts of capacity. Check listed rates and provider documentation for current model availability and throughput figures.
Groq's custom LPU/LPX chips deliver high-throughput inference significantly faster than conventional GPU systems, backed by $350M in Series A funding and hundreds of megawatts of planned capacity.
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
Groq
- 750+ tokens/sec on Llama 3.3 70B — high-throughput GPU-class inference (Groq-published)
- Sub-100ms time-to-first-token for real-time applications
- LPX chips alongside LPUs for expanded capacity
- Limited model selection vs. Together AI or Fireworks
- No vision model support on most models
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
Groq
- Limited model selection vs. Together AI or Fireworks
- No vision model support on most models
- No fine-tuning capability
Provider category context
AI21 Labs is a frontier lab, founded in 2017. Groq is a inference api, founded in 2016. AI21 Labs as a frontier lab trains and serves its own proprietary models. Groq 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 Groq if you need 750+ tokens/sec on llama 3.3 70b — high-throughput gpu-class inference (groq-published). 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.