Baidu's large vision-language MoE model — 424B total, 47B active.
MoonshotAI's coding-focused Kimi model for end-to-end software engineering.
No shared agent demos for these models yet.
No captured outputs for these models yet.
This model hasn't been benchmarked yet.
Both ERNIE 4.5 VL 424B and Kimi K2.7 Code are capable AI models. The best choice depends on your specific use case: consider pricing, context window, speed, and which capabilities (vision, reasoning, audio) you need.
ERNIE 4.5 VL 424B has a lower blended cost. ERNIE 4.5 VL 424B: $0.42 input / $1.25 output. Kimi K2.7 Code: $0.82 input / $3.75 output.
Kimi K2.7 Code has a larger context window: ERNIE 4.5 VL 424B supports 123K tokens vs Kimi K2.7 Code at 262K tokens.
Yes, both ERNIE 4.5 VL 424B and Kimi K2.7 Code support vision/image input.
ERNIE 4.5 VL 424B and Kimi K2.7 Code have similar overall capabilities. The main differences lie in pricing, context window size, and provider-specific strengths. Use this comparison page to review all metrics side by side.
If cost is your priority, choose the cheaper option. If you need the highest intelligence for complex tasks, pick the higher-scoring model. For long documents or codebases, choose the larger context window. You can try both ERNIE 4.5 VL 424B and Kimi K2.7 Code for free on idapt.app to see which performs better for your specific needs.