Model Release

2026-06-02 mimo-v2.5-asr Released

Model Introduction:

• Bilingual & Dialects: Supports Chinese, English, code-switching, and various regional dialects (Wu, Cantonese, Minnan, Sichuanese).

• Lyrics Transcription: High-accuracy Chinese/English lyrics transcription in mixed vocal-instrumental tracks.

• Robust in Complex Audio: Excels in challenging environments (high noise, far-field, multi-speaker).

• Knowledge-Intensive AI: Pinpoint accuracy for classical poetry, jargon, and proper nouns, with auto-punctuation.

2026-04-23 mimo-v2.5-pro Released

Model Introduction:

• Trillion parameters, efficient architecture: 1T total parameters | 42B activations | 1M ultra-long context

• Ultimate Agent Performance: In high-intensity agent scenarios, it performs comparably to Claude Opus4.6

2026-04-23 mimo-v2.5 Released

Model Introduction:

• Native full-modal perception + 1M context: Supports native understanding of images, videos, audio, and text, enabling cross-modal precise perception and long-range reasoning, with comprehensive perception capabilities ranking among the industry's forefront

• Powerful full-modal Agent capabilities: It has native Agent execution capabilities, enabling it to efficiently complete complex tasks such as browsing, understanding, reasoning, and operation, with its performance in daily tasks comparable to that of mimo-v2.5-pro

• Combining Performance and Efficiency: While maintaining leading capabilities, achieving superior token efficiency, and positioned at the Pareto frontier of performance and efficiency

2026-04-23 MiMo-V2.5-TTS Series Release

Model Introduction:

• Premium Voice TTS: Built-in with multiple high-quality premium voices, it has strong capabilities in understanding and adhering to style instructions, supports fine-grained control over speech rate, emotion, tone, etc., and meets the expression needs of multiple scenarios

• Timbre Design: Supports quickly defining and generating new timbres through a single sentence, making timbre creation more intuitive and efficient

• Timbre Cloning: Based on a small number of audio samples, it can reproduce the target timbre with high fidelity, while maintaining the consistency of timbre characteristics and possessing good generalization and stability

2026-03-18 mimo-v2-pro Release

Model overview:

• Uses hybrid architecture with a 1:7 ratio of Global Attention to Sliding Window Attention (SWA);

• 1T total parameters, with 42B active parameters;

• Supports an ultra-long context window of 1M tokens.

Model details: https://platform.xiaomimimo.com/#/docs/news/v2-pro-release

2026-03-18 mimo-v2-omni Release

Model overview:

• Supports up to 256K context length;

• Supports text, vision, and speech modalities.

Model details: https://platform.xiaomimimo.com/#/docs/news/v2-omni-release

2026-03-18 mimo-v2-tts Release

Model overview:

• Pretrained on over 100 million hours of data, using a self-developed multi-codebook speech modeling architecture;

• Offers unique capabilities such as style control, singing, and voice cloning.

Pricing: free for a limited time.

Model details: https://platform.xiaomimimo.com/#/docs/news/v2-tts-release

2026-02-04 mimo-v2-flash Update

1. Upgraded Coding Capabilities in Thinking Mode: Specifically optimized for programming scenarios, the Thinking Mode now achieves a score of 78.6 on SWE-Bench Verified. Both the resolution rate and the quality of code generation have been significantly improved.

2. Substantial Boost in Tool Calling Accuracy: Stability issues regarding tool usage have been resolved. Tool calling accuracy in Thinking Mode has surged from 64% to 97.0%, greatly enhancing execution reliability in Agent scenarios.

3. Enhanced Instruction Following & Reduced Hallucinations:

• Instruction Following: Improved adherence to specific instructions, achieving an AA-IFBench score of 72.

• Factuality: Enhanced rigor in factual responses, with the Non-Hallucination Rate updated to 52%.

1. Optimized Handling of Complex Tasks: Performance on Arena-Hard (Hard Prompts) in Thinking Mode has been strengthened, with the score rising to 60.6. The model now demonstrates superior performance when handling high-difficulty logic problems.

2. More Efficient Chain-of-Thought (CoT): By optimizing CoT generation strategies, the consumption of redundant tokens has been significantly reduced. In benchmarks such as AIME25 and HMMT, the average generation length has decreased by 13% to 30%. This effectively lowers latency and token costs while maintaining model performance.

Note: The model API call method and model name remain unchanged

2026-01-12 mimo-v2-flash Update

1. Enhanced general capabilities: Improved the model’s performance on a wide range of general-purpose tasks.

2. Upgraded coding performance in Thinking mode: Strengthened code generation quality in Thinking mode, especially for programming scenarios.

3. Deep integration with Claude Code: Fully supports using Thinking mode in Claude Code.

   • Best practice: Set Thinking as the default mode to achieve more stable, higher-quality code generation.

4. Optimized Experience for Other Code Agents: Synchronized improvements to the interaction experience and generation quality across code assistant tools (Code Scaffolds) such as Kilo, Cline, and Roo.

5. Improved Stability & Instruction Following: Enhanced output stability and significantly improved adherence to specific output formats.

Note: The model API call method and model name remain unchanged

2025-12-16 mimo-v2-flash Release

Model overview:

• Uses hybrid architecture with a 1:5 ratio of Global Attention to Sliding Window Attention (SWA), a window size of 128, native 32K context, and extended training up to 256K;

• Introduces 3 MTP layers, delivering 2.5 to 3.7× faster inference.

Pricing: input 0.1/M tokens, output 0.3/M tokens.

Model details: mimo-v2-flash: High-Efficiency Inference, Code & Agent Foundation Model

Usage guide: First API call