MiniMax-01

MiniMax-01 by MiniMax excels in the Coding category, where it ranks #214 with a composite score of 62/100. MiniMax-01 is a combines MiniMax-Text-01 for text generation and MiniMax-VL-01 for image understanding. It has 456 billion parameters, with 45.9 billion parameters activated per inference, and can handle a context of up to 4 million tokens. The text model adopts a hybrid architecture that combines Lightning Attention, Softmax Attention, and Mixture-of-Experts (MoE). The image model adopts the “ViT-MLP-LLM” framework and is trained on top of the text model. To read more about the release, see: https://www.minimaxi.com/en/news/minimax-01-series-2 It is particularly strong in areas highlighted by its top benchmark performance and adoption metrics, making it suitable for both individual developers and enterprise teams looking for a reliable coding solution.

MiniMax-01 is priced at $0.20 per million input tokens and $1.10 per million output tokens (USD). Contact the provider for volume discounts and enterprise pricing. Pricing is competitive within the coding category and reflects the model's quality-to-cost ratio.

In the Coding category, MiniMax-01 holds rank #214 out of 6 models tracked. Its quality rank is #214 and adoption rank is #214. You can use our comparison tool at /compare to see detailed side-by-side metrics with specific alternatives. Key differentiators include its composite scoring across benchmarks, community sentiment, and real-world adoption rates.

MiniMax-01 has been evaluated across 5 different signals. Its strongest areas include Capabilities (33/100), Pricing (1/100), Context Window (95/100). These scores are derived from industry-standard benchmarks, community ratings, and real-world performance metrics. The composite score of 62/100 reflects a weighted combination of all tracked signals.

MiniMax-01 is a paid model, though some providers may offer trial credits or limited free tiers for evaluation. Check MiniMax's website for current free tier availability and promotional offers.