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...
Model details →AI21: Jamba Large 1.7 vs Z.ai: GLM 5.3 FlashX
Z.ai: GLM 5.3 FlashX wins on 5 of 6 axes — pricing and capability skew in its favour for most workloads.
GLM-5.3-FlashX is the high-speed variant of Z.ai's GLM-5.3-Flash, a native multimodal model delivering inference speeds of up to 200 tokens/s. Built on the same hybrid sparse and linear attention architecture...
Model details →Side-by-side comparison
| Capability | AI21: Jamba Large 1.7 | Z.ai: GLM 5.3 FlashX | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 256K | 1.0M | 🏆 Z.ai: GLM 5.3 FlashX |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $2.00 | $0.37 | 🏆 Z.ai: GLM 5.3 FlashX |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $8.00 | $1.25 | 🏆 Z.ai: GLM 5.3 FlashX |
| Tool / function calling First-class support for emitting structured tool calls. | Yes | Yes | Tie |
| Vision input Accepts image inputs alongside text. | — | Yes | 🏆 Z.ai: GLM 5.3 FlashX |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | — | Yes | 🏆 Z.ai: GLM 5.3 FlashX |
Frequently asked questions
Is AI21: Jamba Large 1.7 better than Z.ai: GLM 5.3 FlashX?
Z.ai: GLM 5.3 FlashX wins on 5 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 Z.ai: GLM 5.3 FlashX?
Input: $2.00 vs $0.37 per 1M tokens. Output: $8.00 vs $1.25 per 1M tokens.
What context windows do AI21: Jamba Large 1.7 and Z.ai: GLM 5.3 FlashX support?
AI21: Jamba Large 1.7 supports up to 256K tokens. Z.ai: GLM 5.3 FlashX supports up to 1.0M tokens.
Do both AI21: Jamba Large 1.7 and Z.ai: GLM 5.3 FlashX support tool calling?
AI21: Jamba Large 1.7: yes. Z.ai: GLM 5.3 FlashX: yes.