AI21 Labs vs Modal: Token Pricing, Speed & Intelligence
Full comparison of AI21 Labs and Modal — 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.
Modal
Managed inference API — 10 shared-endpoint models with per-token pricing
Modal Shared Endpoints serve a fixed catalog of models from Modal-managed pools. Modal manages the hardware and autoscaling, and usage is billed per token. Shared Endpoints have model-specific concurrency limits on concurrent in-flight requests. Prices are published rates from modal.com/docs/guide/supported-models.md, re-read 2026-10-31.
Key metric comparison
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Live token pricing
Strengths and weaknesses
AI21 Labs
Modal
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.
Modal Shared Endpoints provide managed per-token inference on a fixed 10-model catalog with published cached-input pricing and no infrastructure to manage.
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.
Modal FAQs
What models does Modal offer on Shared Endpoints?
Modal Shared Endpoints currently serve 10 models: GLM 5.3 Flash NVFP4, DeepSeek V4.1 Flash 1017, DeepSeek V4.1 Flash, GLM 5.3 Flash, Muse Spark Open, GLM 5.3, Inkling NVFP4, Qwen3.8-Max, Qwen3.8-Max VL, and Kimi K3. Use the model table above for per-model pricing and capability details. Source: Modal's published Shared Endpoints catalog, re-read 2026-10-31.
How much does Modal charge per 1M tokens?
Modal Shared Endpoints input prices range from $0.15/1M (GLM 5.3 Flash NVFP4) to $3.00/1M (Kimi K3). All models include cached-input pricing. See the model table for full per-model rates.
Does Modal offer prompt caching?
Yes. All 10 models in the Modal Shared Endpoints catalog have published cached-input pricing, ranging from $0.005/1M (DeepSeek V4.1 Flash 1017) to $0.30/1M (Kimi K3). Depending on the model, the cached-input rate is 77.5% to 98% below the standard input rate. Source: Modal's published Shared Endpoints rates, re-read 2026-10-31.
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.
Modal — Managed inference API — 10 shared-endpoint models with per-token pricing
Modal Shared Endpoints serve a fixed catalog of models from Modal-managed pools. Modal manages the hardware and autoscaling, and usage is billed per token. Shared Endpoints have model-specific concurrency limits on concurrent in-flight requests. Prices are published rates from modal.com/docs/guide/supported-models.md, re-read 2026-10-31.
Modal Shared Endpoints provide managed per-token inference on a fixed 10-model catalog with published cached-input pricing and no infrastructure to manage.
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
Modal
- Managed hardware and autoscaling — no infrastructure to operate
- Published per-token rates including cached-input pricing
- OpenAI-style per-token billing across all 10 models
- Small fixed catalog of 10 models
- Model-specific concurrency limits on shared pools
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
Modal
- Small fixed catalog of 10 models
- Model-specific concurrency limits on shared pools
- Prices are published rates re-read 2026-10-31, not continuously collected
Provider category context
AI21 Labs is a frontier lab, founded in 2017. Modal is a inference api. AI21 Labs as a frontier lab trains and serves its own proprietary models. Modal 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 Modal if you need managed hardware and autoscaling — no infrastructure to operate. 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.