MiniMax
MiniMax M3 — 1M context frontier model available on Together AI and Amazon Bedrock
MiniMax is a Chinese AI company offering the MiniMax M-series of large language models. MiniMax M3 is the latest generation, supporting a 1M token context window and designed for enterprise chat, long-document analysis, and agentic workflows. M3 is now available on Together AI and Amazon Bedrock, significantly expanding its global accessibility.
MiniMax M3 combines a 1M token context window with broad availability (Together AI, Amazon Bedrock) — making it one of the most accessible long-context models for global enterprise customers.
- 1M token context window on MiniMax M3
- Available on Together AI and Amazon Bedrock
- Competitive pricing for long-context workloads
- Strong multilingual performance
- Agentic workflow support
- Smaller international developer community
- Less third-party tooling than OpenAI
MiniMax Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
minimax-text-01 MiniMax Text | Chat | efficient | $0.20/ $1.10 in / out per 1M EST Jul 2026 | — | 420ms | 60 t/s | 1.0M | 70 | ||
minimax-m2.7 MiniMax M2 | Chat | advanced | $0.30/ $1.10 in / out per 1M EST Jul 2026 | — | 380ms | 54 t/s | 205K | 75 | ||
minimax-m1 MiniMax M1 | Chat | advanced | $0.30/ $1.10 in / out per 1M EST Jul 2026 | — | 500ms | 45 t/s | 1.0M | 74 | ||
minimax-m3 MiniMax M3 | Chat | frontier | $0.80/ $2.40 in / out per 1M EST Aug 2026 | $0.40 | 340ms | 130 t/s | 1.0M | 84 |
MiniMax — Frequently Asked Questions (FAQs)
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MiniMax is a proprietary AI lab that develops and hosts its own large language models. MiniMax is a Chinese AI company offering the MiniMax M-series of large language models. MiniMax M3 is the latest generation, supporting a 1M token context window and designed for enterprise chat, long-document analysis, and agentic workflows. M3 is now available on Together AI and Amazon Bedrock, significantly expanding its global accessibility. Common use cases include Chat, Long-document analysis, Agentic workflows, Enterprise AI. Headquartered in Shanghai, China, founded 2021. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
MiniMax vs other LLM providers
MiniMax competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. MiniMax M3 combines a 1M token context window with broad availability (Together AI, Amazon Bedrock) — making it one of the most accessible long-context models for global enterprise customers. Use the LLM provider comparison tool to see MiniMax token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose MiniMax?
MiniMax stands out because: MiniMax M3 combines a 1M token context window with broad availability (Together AI, Amazon Bedrock) — making it one of the most accessible long-context models for global enterprise customers.. The API currently offers 4 models, spanning 1 frontier, 2 advanced, 1 efficient tiers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding MiniMax token pricing
MiniMax charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. The cheapest {meta.name} input rate is currently $0.20/1M tokens. Prices shown are sourced from MiniMax's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
MiniMax context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. MiniMax's largest context window is 1.0M tokens. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when MiniMax makes sense
Managed inference APIs like MiniMax eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at MiniMax API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical MiniMax token price data is available in the LLM price history charts.
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