Luma Dream Machine vs Xiaomi MiMo Code
Compare Luma Dream Machine and Xiaomi MiMo Code: free credits, features, and ratings. Which one is right for you?
Category
Video Generation
LLM
Rating
β 3.5
β 4.5
Free Credits
30 generations/month
1 month (MiMo-V2.5, 1M context). Platform signup bonus valid 40 days, invite friends for $2 each
Refresh Period
monthly
one-time
Resolution
720p
N/A
Watermark
Yes
No
Commercial Use
No
No
Paid From
$9.99/month
Token Plan Lite and up
Luma Dream Machine
By Luma, realistic physics and lighting. 30 free generations per month, enough to try it out.γ
Pros
- βRealistic physics
- βBeautiful lighting
- β30/month is enough
Cons
- βOnly 720p
- βOnly 5 seconds
- βWatermark
Key Features
Realistic physicsExcellent lighting/renderingImage to VideoText to VideoUp to 1080pFast mode acceleration
Xiaomi MiMo Code
Xiaomi's AI coding agent forked from OpenCode, open-sourced under MIT. The killer feature is the built-in MiMo Auto channel β zero config, free for 1 month, runs MiMo-V2.5 with 1M token context. Coding performance is on par with Claude Sonnet 4.6 on SWE-Bench, but 40-60% more token-efficient. Highlights: cross-session memory, voice input, auto knowledge distillation. After the free period, you need a Token Plan subscription.γ
Pros
- βFree for 1 month, zero config
- βCoding on par with Claude Sonnet 4.6
- β40-60% more token-efficient than Claude
- β1M token context window
- βPersistent cross-session memory
- βVoice input and auto knowledge distillation
- βMIT open source, BYOK supported
Cons
- βFree for only 1 month
- βFree channel only gives V2.5, Pro requires payment
- βCredit multiplier is high β advertised 1.6B credits sounds more than it is
- βEcosystem still early, community building
- βReal-name auth required for paid plans
Key Features
MiMo Auto zero-config free channelCross-session persistent memory (SQLite FTS5)Compose mode for spec-driven developmentVoice input (TenVAD + MiMo ASR)/dream auto knowledge extraction + /distill skill miningGoal stop-condition verificationSubagent parallel system1M token context windowMIT open source