MiniMax-M1-80K is the most enhanced version of the base model series. It retains the same 456 billion total parameters , with 45.9 billion activated per token , but has been further trained on 7.5 trillion tokens of diverse tasks, including mathematical reasoning , competitive programming, logical reasoning using the SynLogic framework , and real-world software engineering tasks in sandboxed environments based on SWE-bench. Compared to MiniMax-M1-40K , its reasoning budget has been increased from 40,000 to 80,000 tokens .
The model utilizes an enhanced version of the CISPO algorithm , combined with a progressive window expansion strategy . This approach ensures training stability at each stage and enables the model to adapt to increasing task complexity. An important innovation is the Lightning Attention mechanism , which represents a key scientific breakthrough by MiniMax in the field of large language models. It significantly reduces resource consumption during both training and inference — MiniMax-M1-80K consumes only 25% of the FLOPs required by DeepSeek R1 when generating 100,000 tokens , achieving unprecedented efficiency in long-sequence processing. Full reinforcement learning training was completed in just three weeks using 512 H800 GPUs .
MiniMax-M1-80K demonstrates outstanding performance across multiple benchmarks , achieving 86.0% on AIME 2024 and 56.0% on SWE-bench Verified . Particularly impressive are its capabilities in agent-based systems utilizing tool calling , where it scores 62.0% on TAU-bench airline and 63.5% on TAU-bench retail , even outperforming Gemini 2.5 Pro in certain scenarios.These results confirm its suitability for a wide range of research applications and commercial products . Furthermore, the model is supported by modern deployment frameworks , including vLLM and Transformers , and is available under the Apache-2.0 license , making it fully open and unrestricted for use .
| Model Name | Context | Type | GPU | Status | Link |
|---|
There are no public endpoints for this model yet.
An endpoint is a server with pre-installed software that automatically downloads neural network weights and launches an OpenAI-compatible API to handle incoming requests.
A private endpoint is created within the user's project, belongs exclusively to the user, and is billed per server configuration in accordance with the infrastructure pricing.
We recommend creating private endpoints when you need to:
There are no configurations for this model, context and quantization yet.
Contact our dedicated neural networks support team at support@immers.cloud or send your request to the sales department at sale@immers.cloud.