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 Mistral Large
AI21: Jamba Large 1.7 and Mistral Large are evenly matched on 6 axes — pick the one whose provider, latency, or licensing fits your stack.
This is Mistral AI's flagship model, Mistral Large 2 (version `mistral-large-2407`). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement [here](https://mistral.ai/news…
Model details →Side-by-side comparison
| Capability | AI21: Jamba Large 1.7 | Mistral Large | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 256K | 128K | 🏆 AI21: Jamba Large 1.7 |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $2.00 | $2.00 | Tie |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $8.00 | $6.00 | 🏆 Mistral Large |
| 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 Mistral Large?
AI21: Jamba Large 1.7 and Mistral Large are evenly matched on 6 axes — pick the one whose provider, latency, or licensing fits your stack.
What's the price difference between AI21: Jamba Large 1.7 and Mistral Large?
Input: $2.00 vs $2.00 per 1M tokens. Output: $8.00 vs $6.00 per 1M tokens.
What context windows do AI21: Jamba Large 1.7 and Mistral Large support?
AI21: Jamba Large 1.7 supports up to 256K tokens. Mistral Large supports up to 128K tokens.
Do both AI21: Jamba Large 1.7 and Mistral Large support tool calling?
AI21: Jamba Large 1.7: yes. Mistral Large: yes.