AI21 Labs vs Moonshot AI: Token Pricing, Speed & Intelligence
Full comparison of AI21 Labs and Moonshot AI — 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.
Moonshot AI
Kimi K3 — 1T MoE open-weight model, strong agentic and coding benchmark scores
Moonshot AI is a Chinese AI startup behind the Kimi model family. Kimi K3 is their latest 1-trillion-parameter MoE model, succeeding K2 with improved coding and agentic benchmark scores. Kimi K2.6 remains available as a stable production option. The Kimi API offers long-context processing up to 128K tokens with competitive pricing, and models are available via OpenRouter for global access.
Key metric comparison
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Live token pricing
Strengths and weaknesses
AI21 Labs
Moonshot AI
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.
Kimi K3 is a 1-trillion-parameter open-weight MoE model with strong coding and agentic benchmark scores — available for self-hosting or via the Moonshot API and OpenRouter.
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.
Moonshot AI FAQs
What is Kimi K3?
Kimi K3 is Moonshot AI's latest 1-trillion-parameter mixture-of-experts model, succeeding K2. It activates approximately 32B parameters per token and leads on coding and agentic benchmarks as of September 2026.
What is the difference between Kimi K3 and K2.6?
Kimi K3 is the latest generation with improved benchmark scores. Kimi K2.6 is a stable, battle-tested version suitable for production workloads that prioritise reliability over cutting-edge performance.
Is Kimi K3 open-weight?
Yes. Kimi K3 weights are publicly available on Hugging Face, making it one of the largest open-weight models available. Teams can self-host it on multi-GPU clusters or access it via the Moonshot API.
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.
Moonshot AI — Kimi K3 — 1T MoE open-weight model, strong agentic and coding benchmark scores
Moonshot AI is a Chinese AI startup behind the Kimi model family. Kimi K3 is their latest 1-trillion-parameter MoE model, succeeding K2 with improved coding and agentic benchmark scores. Kimi K2.6 remains available as a stable production option. The Kimi API offers long-context processing up to 128K tokens with competitive pricing, and models are available via OpenRouter for global access.
Kimi K3 is a 1-trillion-parameter open-weight MoE model with strong coding and agentic benchmark scores — available for self-hosting or via the Moonshot API and OpenRouter.
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
Moonshot AI
- Kimi K3: 1T MoE open-weight model with strong coding and agentic benchmark scores
- Kimi K2.6 available as stable production option
- Competitive on agentic and tool-use benchmarks
- API primarily targets Chinese market — international latency may vary
- Smaller ecosystem than OpenAI or Anthropic
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
Moonshot AI
- API primarily targets Chinese market — international latency may vary
- Smaller ecosystem than OpenAI or Anthropic
- Fewer third-party integrations available
Provider category context
AI21 Labs is a frontier lab, founded in 2017. Moonshot AI is a frontier lab, founded in 2023. Both are frontier lab providers — the comparison is primarily about pricing, model selection, and feature differentiation within the same tier.
How to choose between them
Both AI21 Labs and Moonshot AI are frontier labs with proprietary models. Choose based on benchmark performance for your specific task: AI21 Labs leads on hybrid ssm-transformer architecture delivers faster inference than pure-transformer models, while Moonshot AI leads on kimi k3: 1t moe open-weight model with strong coding and agentic benchmark scores. For cost-sensitive workloads, compare the cheapest model tier from each provider in the pricing table above — the gap between efficient-tier models is often larger than between flagship models.