AI21 Labs vs Stability AI: Token Pricing, Speed & Intelligence
Full comparison of AI21 Labs and Stability 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.
Stability AI
Stable Diffusion 3.5 Large and Stable Image Ultra — open-weight image generation
Stability AI is the creator of Stable Diffusion, the most widely deployed open-weight image generation model family. The 2026 API offers Stable Diffusion 3.5 Large (highest quality), SD 3.5 Medium (balanced), and Stable Image Ultra for photorealistic output. All SD 3.5 models are open-weight and available for self-hosting. Stability AI also offers Stable Video Diffusion for short video generation.
Key metric comparison
—
Not comparable
—
Not comparable
—
Not comparable
—
Not comparable
—
Not comparable
—
Not comparable
Live token pricing
Strengths and weaknesses
AI21 Labs
Stability 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.
Stable Diffusion 3.5 models are open-weight and self-hostable — enabling the largest fine-tuning and LoRA ecosystem of any image generation model family, with full control over the generation pipeline.
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.
Stability AI FAQs
What is Stable Diffusion 3.5?
Stable Diffusion 3.5 is Stability AI's latest generation of open-weight text-to-image models. SD 3.5 Large offers the highest quality; SD 3.5 Medium is a balanced option for production use. Both are available for self-hosting.
How does Stability AI compare to FLUX?
FLUX 1.1 Pro generally outperforms SD 3.5 on photorealism and prompt adherence benchmarks. However, SD 3.5 has a much larger community ecosystem of fine-tuned variants and LoRAs, making it more flexible for specialised use cases.
Can I self-host Stable Diffusion 3.5?
Yes. SD 3.5 Large and Medium weights are publicly available under the Stability AI Community License. You can run them on any GPU with sufficient VRAM — SD 3.5 Medium requires ~8GB VRAM; SD 3.5 Large requires ~24GB.
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.
Stability AI — Stable Diffusion 3.5 Large and Stable Image Ultra — open-weight image generation
Stability AI is the creator of Stable Diffusion, the most widely deployed open-weight image generation model family. The 2026 API offers Stable Diffusion 3.5 Large (highest quality), SD 3.5 Medium (balanced), and Stable Image Ultra for photorealistic output. All SD 3.5 models are open-weight and available for self-hosting. Stability AI also offers Stable Video Diffusion for short video generation.
Stable Diffusion 3.5 models are open-weight and self-hostable — enabling the largest fine-tuning and LoRA ecosystem of any image generation model family, with full control over the generation pipeline.
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
Stability AI
- Open-weight SD 3.5 models — self-host on any GPU cloud
- Largest community ecosystem of fine-tuned variants and LoRAs
- Stable Image Ultra for photorealistic output
- Image quality trails FLUX 1.1 Pro and Midjourney on photorealism benchmarks
- Company has faced financial challenges and leadership changes
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
Stability AI
- Image quality trails FLUX 1.1 Pro and Midjourney on photorealism benchmarks
- Company has faced financial challenges and leadership changes
- SD 3.5 adoption slower than FLUX in the community
Provider category context
AI21 Labs is a frontier lab, founded in 2017. Stability AI is a frontier lab, founded in 2020. 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 Stability 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 Stability AI leads on open-weight sd 3.5 models — self-host on any gpu cloud. 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.