MiniMax M2.5 (free)

MiniMax M2.5 (free) by MiniMax excels in the Coding category, where it ranks #70 with a composite score of 80/100. MiniMax-M2.5 is a SOTA large language model designed for real-world productivity. Trained in a diverse range of complex real-world digital working environments, M2.5 builds upon the coding expertise of M2.1 to extend into general office work, reaching fluency in generating and operating Word, Excel, and Powerpoint files, context switching between diverse software environments, and working across different agent and human teams. Scoring 80.2% on SWE-Bench Verified, 51.3% on Multi-SWE-Bench, and 76.3% on BrowseComp, M2.5 is also more token efficient than previous generations, having been trained to optimize its actions and output through planning. 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 M2.5 (free) is priced at $0.00 per million input tokens and $0.00 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 M2.5 (free) holds rank #70 out of 6 models tracked. Its quality rank is #70 and adoption rank is #70. 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 M2.5 (free) has been evaluated across 5 different signals. Its strongest areas include Capabilities (67/100), Pricing (30/100), Context Window (84/100). These scores are derived from industry-standard benchmarks, community ratings, and real-world performance metrics. The composite score of 80/100 reflects a weighted combination of all tracked signals.

MiniMax M2.5 (free) is available as a free, open-source model. You can run it locally or use it through various hosting providers. Some API providers may charge for hosted inference, so check individual provider pricing.