AI21 Labs vs Cerebras: Token Pricing, Speed & Intelligence
Full comparison of AI21 Labs and Cerebras — 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.
Cerebras
CS-4 wafer-scale — high-throughput inference on GPT OSS 120B and Qwen 3.8 27B
Cerebras uses wafer-scale silicon (the CS-4 chip) to deliver high-throughput inference significantly faster than conventional GPU systems. The Developer tier provides self-serve access; Enterprise tier adds production capacity, custom model weights, fine-tuning, and dedicated support. Available on AWS Marketplace, OpenRouter, HuggingFace, and Vercel. Check cerebras.ai/pricing for current rates and any free-credit offers.
Key metric comparison
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Live token pricing
Strengths and weaknesses
AI21 Labs
Cerebras
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.
Cerebras CS-4 wafer-scale silicon delivers high-throughput inference on GPT OSS 120B and Qwen 3.8 27B — significantly faster than conventional GPU systems, enabling real-time agentic workflows and interactive AI.
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.
Cerebras FAQs
How fast is Cerebras inference?
Cerebras delivers significantly higher throughput than conventional GPU-based providers on GPT OSS 120B and Qwen 3.8 27B, enabling real-time agentic workflows and interactive AI applications. Check cerebras.ai for current benchmark figures.
What is the Cerebras CS-4 chip?
The CS-4 is Cerebras's latest wafer-scale chip, 58× larger than a GPU. It provides 900,000+ AI cores and massive on-chip SRAM, eliminating the memory bandwidth bottleneck that limits GPU inference speed.
What models does Cerebras support?
Cerebras supports GPT OSS 120B and Qwen 3.8 27B on the Developer tier. Enterprise adds access to all models including production and preview models. Check cerebras.ai/pricing for current rates.
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.
Cerebras — CS-4 wafer-scale — high-throughput inference on GPT OSS 120B and Qwen 3.8 27B
Cerebras uses wafer-scale silicon (the CS-4 chip) to deliver high-throughput inference significantly faster than conventional GPU systems. The Developer tier provides self-serve access; Enterprise tier adds production capacity, custom model weights, fine-tuning, and dedicated support. Available on AWS Marketplace, OpenRouter, HuggingFace, and Vercel. Check cerebras.ai/pricing for current rates and any free-credit offers.
Cerebras CS-4 wafer-scale silicon delivers high-throughput inference on GPT OSS 120B and Qwen 3.8 27B — significantly faster than conventional GPU systems, enabling real-time agentic workflows and interactive AI.
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
Cerebras
- GPT OSS 120B: high-throughput inference on CS-4 wafer-scale silicon
- Qwen 3.8 27B: fast efficient-tier inference
- CS-4 chip: 58× larger than GPUs, purpose-built for AI inference
- Limited model selection vs. Together AI or Fireworks
- No vision or multimodal 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
Cerebras
- Limited model selection vs. Together AI or Fireworks
- No vision or multimodal support
- No fine-tuning on Developer tier
Provider category context
AI21 Labs is a frontier lab, founded in 2017. Cerebras is a inference api, founded in 2016. AI21 Labs as a frontier lab trains and serves its own proprietary models. Cerebras 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 Cerebras if you need gpt oss 120b: high-throughput inference on cs-4 wafer-scale silicon. 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.