MiniMax의 MiniMax-M3 모델은 524K 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
Technical Specifications
- Provider
- MiniMax
- Category
- open-source
- Release Date
- 2026-06
- Batch API Discount
- Standard rate
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Configurable (Low / Med / High)
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- minimax-m3
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2028+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Free across full context window (only KV-cache VRAM scales)
- Prompt Cache Write & TTL Policy
- Automatic prefix KV-caching in vLLM / SGLang (0 cost)
- Computer Use & Browser GUI Agent
- Supported (Screen coordinate grounding, browser & desktop GUI action loops)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 186 GB VRAM (4-bit Quantized) · 645 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
MiniMax-M3 Cost, Context, Hardware & API Integration
Open-weights or free-tier model: $0 API license cost when self-hosted.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to MiniMax-M3:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 65%
- GPQA Diamond
- 72%
- MMLU-Pro
- 85%
- HumanEval
- 95%
- Chatbot Arena Elo
- 1420pts
Strengths
- MiniMax의 첨단 Multimodal Long-Context Sparse MoE Transformer 아키텍처와 최대 524K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0/1M, 출력 $0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Limitations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Editorial Assessment
MiniMax-M3은(는) 성능, 속도, 비용 효율성의 균형이 뛰어난 MiniMax의 핵심 AI 모델입니다.
Best for: 기업 업무 자동화, 소프트웨어 개발, 자율형 AI 에이전트 및 MiniMax 기반 프로덕션 파이프라인
Compare MiniMax-M3
Frequently Asked Questions about MiniMax-M3
How much does the MiniMax-M3 API cost per 1M tokens?+
MiniMax-M3 by MiniMax is priced at $0 per 1M input tokens and $0 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 0% discount.
Should I use a $20/month subscription or the MiniMax-M3 pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in MiniMax-M3's context window?+
MiniMax-M3 features a 524K tokens context window, which equals approximately 393,216 English words or ~1,049 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 32,000 tokens.
Can I run MiniMax-M3 locally on my own GPU (Open Weights)?+
Yes, MiniMax-M3 (Confidential / 300B MoE) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does MiniMax train on data sent to the MiniMax-M3 API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Open-Weights Research & Commercial License. Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does MiniMax-M3 perform on coding and reasoning benchmarks?+
MiniMax-M3 achieves a composite Intelligence Index of 89/100, scoring 65% on SWE-bench Verified, 72% on GPQA Diamond, 85% on MMLU-Pro, and 1420 pts on Chatbot Arena Elo.
What is MiniMax-M3 best used for?+
MiniMax-M3 is best suited for: 기업 업무 자동화, 소프트웨어 개발, 자율형 AI 에이전트 및 MiniMax 기반 프로덕션 파이프라인. Assessment: MiniMax-M3은(는) 성능, 속도, 비용 효율성의 균형이 뛰어난 MiniMax의 핵심 AI 모델입니다.
MiniMax Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to MiniMax's flagship reasoning tier:
MiniMax-M2.7
MiniMax-M2.7-highspeed
MiniMax-M2.5
MiniMax-M2.5-highspeed
MiniMax-M2.1
MiniMax-M2
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to MiniMax-M3:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
An AI model trained via reinforcement learning to spend dynamic compute thinking through complex logic, math, and architectural problems before outputting a solution. Examples include OpenAI o1, o3, and DeepSeek R1.
A step-by-step reasoning technique where an AI model generates intermediate problem-solving thoughts before arriving at a final answer. Native reasoning models like OpenAI o3 and DeepSeek R1 use CoT to solve complex math and coding challenges.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.