MiniMax vs Together AI: Token Pricing, Speed & Intelligence
Full comparison of MiniMax and Together AI — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.
MiniMax
Long-context frontier models with 1M token windows
MiniMax is a Chinese AI company offering the MiniMax M-series of large language models. MiniMax M2.7 and M1 support context windows up to 1M tokens and are designed for enterprise chat, long-document analysis, and agentic workflows.
Together AI
Open-source model hosting with competitive inference pricing
Together AI specialises in hosting open-weight models including the full Llama family, Mixtral, and DeepSeek variants. They offer live pricing via their public API and support fine-tuning workflows. A popular choice for teams that want open-source flexibility without managing their own GPU infrastructure.
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Live token pricing
Strengths & weaknesses
MiniMax
Together AI
Key differentiators
MiniMax M1 supports a 1M token context window at $0.30/1M input tokens — one of the most cost-effective long-context models available.
The broadest open-weight model catalog with fine-tuning support — ideal for teams that need model customisation without self-hosting.
Frequently asked questions
MiniMax FAQs
What is MiniMax M2.7?
MiniMax M2.7 is MiniMax's latest chat model, supporting a 205K token context window. It is designed for enterprise chat, long-document analysis, and agentic tasks.
Is MiniMax available internationally?
Yes. The MiniMax API is accessible globally, and models are also available through OpenRouter and other inference aggregators.
Together AI FAQs
What models does Together AI support?
Together AI hosts 100+ open-weight models including the full Llama 3.x family (8B, 70B, 405B), Mixtral, DeepSeek R1, Qwen, and many others. They also support custom fine-tuned model deployment.
How much does Together AI cost?
Llama 3.3 70B costs $0.88/1M tokens (input and output). Llama 3.1 405B is $3.50/1M tokens. Smaller models like Llama 3.2 11B Vision start at $0.18/1M tokens.
Does Together AI support fine-tuning?
Yes. Together AI offers supervised fine-tuning for Llama and other open-weight models. You can upload training data, run fine-tuning jobs, and deploy the resulting model via their inference API.
Provider resources
MiniMax — Long-context frontier models with 1M token windows
MiniMax is a Chinese AI company offering the MiniMax M-series of large language models. MiniMax M2.7 and M1 support context windows up to 1M tokens and are designed for enterprise chat, long-document analysis, and agentic workflows.
MiniMax M1 supports a 1M token context window at $0.30/1M input tokens — one of the most cost-effective long-context models available.
Together AI — Open-source model hosting with competitive inference pricing
Together AI specialises in hosting open-weight models including the full Llama family, Mixtral, and DeepSeek variants. They offer live pricing via their public API and support fine-tuning workflows. A popular choice for teams that want open-source flexibility without managing their own GPU infrastructure.
The broadest open-weight model catalog with fine-tuning support — ideal for teams that need model customisation without self-hosting.
Key strengths compared
MiniMax
- ▸1M token context window
- ▸Competitive pricing
- ▸Strong multilingual performance
Together AI
- ▸Largest selection of open-weight models
- ▸Fine-tuning support for custom model training
- ▸OpenAI-compatible API — easy migration
Provider category context
MiniMax is a frontier lab, founded in 2021. Together AI is a inference api, founded in 2022. MiniMax as a frontier lab trains and serves its own proprietary models. Together AI as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers but without access to proprietary frontier models.
How to choose between them
Choose MiniMax if you need 1m token context window. Choose Together AI if you need largest selection of open-weight models. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.