Jamba Large 1.7 is the latest model in the Jamba open family, offering improvements in grounding, instruction-following, and overall efficiency. Built on a hybrid SSM-Transformer architecture with a 256K context window, it delivers more acc…
Model details →AI21: Jamba Large 1.7 vs OpenAI: GPT-3.5 Turbo 16k
AI21: Jamba Large 1.7 wins on 2 of 6 axes — pricing and capability skew in its favour for most workloads.
This model offers four times the context length of gpt-3.5-turbo, allowing it to support approximately 20 pages of text in a single request at a higher cost. Training data: up to Sep 2021.
Model details →Side-by-side comparison
| Capability | AI21: Jamba Large 1.7 | OpenAI: GPT-3.5 Turbo 16k | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 256K | 16K | 🏆 AI21: Jamba Large 1.7 |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $2.00 | $3.00 | 🏆 AI21: Jamba Large 1.7 |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $8.00 | $4.00 | 🏆 OpenAI: GPT-3.5 Turbo 16k |
| Tool / function calling First-class support for emitting structured tool calls. | Yes | Yes | Tie |
| Vision input Accepts image inputs alongside text. | — | — | Tie |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | — | — | Tie |
Frequently asked questions
Is AI21: Jamba Large 1.7 better than OpenAI: GPT-3.5 Turbo 16k?
AI21: Jamba Large 1.7 wins on 2 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between AI21: Jamba Large 1.7 and OpenAI: GPT-3.5 Turbo 16k?
Input: $2.00 vs $3.00 per 1M tokens. Output: $8.00 vs $4.00 per 1M tokens.
What context windows do AI21: Jamba Large 1.7 and OpenAI: GPT-3.5 Turbo 16k support?
AI21: Jamba Large 1.7 supports up to 256K tokens. OpenAI: GPT-3.5 Turbo 16k supports up to 16K tokens.
Do both AI21: Jamba Large 1.7 and OpenAI: GPT-3.5 Turbo 16k support tool calling?
AI21: Jamba Large 1.7: yes. OpenAI: GPT-3.5 Turbo 16k: yes.