MiniMax API Pricing
Chinese AI company with strong multimodal and voice capabilities
9 paid models · Price range: $0.15 - $0.40 /1M
Last updated: Aug 8, 2026
About MiniMax
MiniMax is a Chinese AI company known for multimodal AI capabilities, particularly in voice synthesis and video generation. Their models power popular consumer applications in China and offer competitive API pricing for developers.
Key Highlights
- Strong voice synthesis capabilities
- Video generation models
- Multimodal understanding
- Popular in Chinese consumer apps
- Competitive pricing
Pricing Features
- Pay-per-token billing
- Prompt Caching (discounted)
Competitive pricing for multimodal applications. Good value for voice and video use cases.
API Features
Common Use Cases
- • Voice Applications
- • Video Generation
- • Chinese Consumer Apps
- • Multimodal Content
📊 MiniMax Model Comparison
Compare all models side by side. Sorted by total price (input + output).
| Model | Tier | Input /1M | Output /1M | Total /1M | Context | Best For |
|---|---|---|---|---|---|---|
| MiniMax M3 (batch) | Budget | $0.15 | $0.60 | $0.75 | 524k | Complex reasoning, math |
| MiniMax M2.7 | Balanced | $0.24 | $0.96 | $1.20 | 205k | Complex reasoning, math |
| MiniMax M2.5 | Balanced | $0.27 | $0.95 | $1.22 | 205k | Complex reasoning, math |
| MiniMax M2.1 | Balanced | $0.27 | $0.95 | $1.22 | 205k | Complex reasoning, math |
| MiniMax M2 | Balanced | $0.26 | $1.00 | $1.25 | 205k | Complex reasoning, math |
| MiniMax-01 | Balanced | $0.20 | $1.10 | $1.30 | 1.0M | Image analysis |
🎯 Which MiniMax Model Should You Choose?
Quick recommendations based on your use case.
💰 MiniMax Monthly Cost Examples
Estimated monthly costs for common use cases.
| Use Case | Monthly Usage | MiniMax M3 (batch) (Budget) |
|---|---|---|
|
Customer Service Bot
1000 conversations/day
|
500k input 200k output |
$0.20/mo |
|
Code Assistant
200 requests/day
|
1.0M input 500k output |
$0.45/mo |
|
Data Analysis
500 analyses/day
|
2.0M input 300k output |
$0.48/mo |
⚔️ MiniMax vs Competitors
How does {brand} compare to other major AI providers?
| Brand | Model | Input /1M | Output /1M | Total /1M | Context | vs {brand} |
|---|---|---|---|---|---|---|
|
MiniMax
|
MiniMax M3 (batch) Current | $0.15 | $0.60 | $0.75 | 524k | — |
OpenAI
|
Codex Mini | $1.50 | $6.00 | $7.50 | 200k | 900% more |
|
OpenAI
|
GPT-5.2 Pro | $21.00 | $168.00 | $189.00 | 400k | 25100% more |
|
OpenAI
|
GPT-5.2 | $1.75 | $14.00 | $15.75 | 400k | 2000% more |
|
OpenAI
|
GPT-5.1-Codex-Max | $1.25 | $10.00 | $11.25 | 400k | 1400% more |
|
OpenAI
|
GPT-5.1 | $1.25 | $10.00 | $11.25 | 400k | 1400% more |
|
OpenAI
|
GPT-5.1-Codex | $1.25 | $10.00 | $11.25 | 400k | 1400% more |
All Models
| Model | Input /1M | Output /1M | Context | Capabilities | Actions | |
|---|---|---|---|---|---|---|
| MiniMax M3 (batch) Cheapest | $0.15 | $0.60 | 524k | View Details | ||
| MiniMax M3 | $0.30 | $1.20 | 1.0M | View Details | ||
| MiniMax M2.7 | $0.24 | $0.96 | 205k | View Details | ||
| MiniMax M2.5 | $0.27 | $0.95 | 205k | View Details | ||
| MiniMax M2-her | $0.30 | $1.20 | 66k | View Details | ||
| MiniMax M2.1 | $0.27 | $0.95 | 205k | View Details | ||
| MiniMax M2 | $0.26 | $1.00 | 205k | View Details | ||
| MiniMax M1 | $0.40 | $2.20 | 1.0M | View Details | ||
| MiniMax-01 | $0.20 | $1.10 | 1.0M | View Details |
❓ MiniMax Pricing FAQ
What is the cheapest MiniMax model?
The cheapest MiniMax model is MiniMax M3 (batch) at $0.75 per 1M tokens (input + output combined).
What is the maximum context length for MiniMax models?
MiniMax models support up to 1.0M context length, allowing you to process large documents and maintain long conversations.
How do I choose between MiniMax models?
For budget projects, choose the cheapest model. For code generation, prioritize low output price. For complex reasoning, choose models with reasoning capability. Use our scenario guide above.
Does MiniMax support prompt caching?
Yes, MiniMax supports prompt caching which can significantly reduce costs for repeated prompts. Check individual model pages for caching prices.