Thinking Machines Lab's smaller, more efficient open-weight multimodal model, with 12B active parameters out of 276B total.
MoonshotAI's coding-focused Kimi model for end-to-end software engineering.
No shared demos for these models yet.
No captured outputs for these models yet.
Both Inkling Small 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.
Inkling Small has a lower blended cost. Inkling Small: $0.45 input / $1.20 output. Kimi K2.7 Code: $0.82 input / $3.75 output.
Inkling Small has a larger context window: Inkling Small supports 524K tokens vs Kimi K2.7 Code at 262K tokens.
Yes, both Inkling Small and Kimi K2.7 Code support vision/image input.
Inkling Small 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 Inkling Small and Kimi K2.7 Code for free on idapt.app to see which performs better for your specific needs.