Xiaomi MiMo-V2.5-Pro: The New Benchmark for Asian Frontier Models (April 2026)
Xiaomi's newly open-sourced MiMo-V2.5-Pro (1.02T params, 42B active) introduces hybrid attention and multi-token prediction, achieving SWE-Bench Pro 57.2% and frontier-competitive performance across reasoning, coding, and long-context tasks. This analysis compares MiMo-V2.5-Pro against Kimi K2.5, MiniMax M2.7, and GLM-5.1, revealing a strategic consolidation of Asian frontier capability.
Xiaomi MiMo-V2.5-Pro: The New Benchmark for Asian Frontier Models (April 2026)
Executive Summary
On April 22, 2026, Xiaomi released MiMo-V2.5-Pro, an open-source 1.02T-parameter model with 42B activated parameters that immediately establishes itself as a contender in the Asian frontier AI landscape. Unlike specialized models focused on single domains (multimodal, cost, or iteration), MiMo-V2.5-Pro targets comprehensive frontier capability: competitive reasoning (MMLУ-Pro 68.5%), professional software engineering (SWE-Bench Pro 57.2%), and exceptional long-context performance (1M tokens with sustained reasoning).
Key Distinctions:
- Hybrid Attention Architecture: Interleaves Sliding Window Attention (SWA) and Global Attention (GA) at a 6:1 ratio with 128-token windows, reducing KV-cache by ~7x while maintaining long-context coherence
- Multi-Token Prediction (MTP): Native 3-layer MTP modules increase inference throughput by 3x and accelerate RL rollouts
- 1M Context Window: Sustains reasoning and tool use across extreme context lengths (verified on GraphWalks benchmark)
- Agentic Excellence: SFT + large-scale agentic RL + Multi-Teacher On-Policy Distillation (MOPD) training paradigm
This report positions MiMo-V2.5-Pro within the evolved Asian frontier landscape alongside Kimi K2.5, MiniMax M2.7, and GLM-5.1, revealing how four independent systems now cover complementary strategic niches.
I. Architecture & Design Philosophy
MiMo-V2.5-Pro (Xiaomi)
Architecture: Mixture-of-Experts (MoE)
Total Parameters: 1.02T
Activated Parameters: 42B
Context Length: 1M tokens (full) / 256K tokens (Base variant)
Attention Mechanism: Hybrid (Sliding Window Attention + Global Attention, 6:1 ratio, 128-token window)
Routed Experts: 384 total, 8 per token
Num Layers: 70 (1 dense + 69 MoE), 10 full attention + 60 SWA
Hidden Size: 6144
Multi-Token Prediction: 3 layers
Training Data: 27T tokens (FP8 mixed precision)
Key Innovation: Native hybrid attention + natively integrated multi-token prediction for speed and RL efficiency
MiMo-V2.5-Pro addresses the quadratic complexity of long-context attention through hybrid attention: interleaving sliding window (local) and global attention at a 6:1 ratio. This architectural choice reduces memory overhead while retaining the ability to attend to distant tokens when needed. The learnable attention sink bias ensures smooth degradation at context boundaries.
Multi-token prediction is natively integrated into training and inference—not bolted on. This enables models to predict multiple tokens per forward pass, naturally accelerating inference (3x speedup) and RL training throughput.
Source: HuggingFace Model Card | Xiaomi Technical Blog
Comparison: Architecture Summary
| Dimension | MiMo-V2.5-Pro | Kimi K2.5 | MiniMax M2.7 | GLM-5.1 |
|---|---|---|---|---|
| Total Params | 1.02T | 1T | ~200B | ~200B |
| Activated Params | 42B | 32B | ~16B | ~16B |
| Context Length | 1M | 256K | 256K+ | 256K+ |
| Attention Type | Hybrid (SWA+GA) | MLA | Standard | Dynamic Sparse |
| Key Innovation | Hybrid attention + native MTP | Multimodal + swarm | Self-evolution | Iteration sustain |
II. Performance: Benchmarks & Comparative Analysis
A. General Reasoning & Knowledge
| Benchmark | MiMo-V2.5-Pro | Kimi K2.5 | MiniMax M2.7 | GLM-5.1 | Notes |
|---|---|---|---|---|---|
| BBH (3-shot) | 88.4 | 88.7 | — | — | K2.5 edges MiMo slightly |
| MMLU (5-shot) | 89.4 | 87.8 | — | — | MiMo-V2.5-Pro leads |
| MMLU-Redux (5-shot) | 92.8 | 90.2 | — | — | MiMo-V2.5-Pro strong |
| MMLU-Pro (5-shot) | 68.5 | 69.2 | — | — | K2.5 slight advantage |
| DROP (3-shot) | 86.3 | 83.6 | — | — | MiMo-V2.5-Pro leads reading comprehension |
| GPQA-Diamond (5-shot) | 66.7 | 48.1 | — | — | MiMo-V2.5-Pro significantly ahead |
Interpretation: MiMo-V2.5-Pro demonstrates strong generalist reasoning, particularly on knowledge-intensive tasks (GPQA-Diamond 66.7% vs. K2.5's 48.1%). This represents a notable capability inflection for Xiaomi's approach.
Source: MiMo-V2.5-Pro Model Card Evaluation Results
B. Mathematics & Problem-Solving
| Benchmark | MiMo-V2.5-Pro | Kimi K2.5 | MiniMax M2.7 | GLM-5.1 |
|---|---|---|---|---|
| GSM8K (8-shot) | 99.6% | 92.1% | — | — |
| MATH (4-shot) | 86.2% | 70.2% | — | — |
| AIME 24&25 (2-shot) | 37.3% | 31.6% | — | — |
Finding: MiMo-V2.5-Pro dominates mathematics benchmarks—GSM8K 99.6% (near-perfect elementary problem-solving) and MATH 86.2% (university-level). This positions MiMo as a top-tier mathematical reasoner among Asian models.
Source: MiMo-V2.5-Pro Model Card
C. Code Generation & Software Engineering
| Benchmark | MiMo-V2.5-Pro | Kimi K2.5 | MiniMax M2.7 | GLM-5.1 |
|---|---|---|---|---|
| HumanEval+ (1-shot) | 75.6% | 84.8% | — | — |
| MBPP+ (3-shot) | 74.1% | 73.8% | — | — |
| LiveCodeBench v6 (1-shot) | 39.6% | 26.3% | — | — |
| SWE-Bench AgentLess (3-shot) | 35.7% | 28.2% | — | — |
| SWE-Bench Pro (agentic) | 57.2% | 50.7% | 56.22% | 58.4% |
Key Findings:
- HumanEval+: K2.5 leads (84.8% vs. MiMo's 75.6%), suggesting K2.5's multimodal training enhances simple code completion
- Real-World Agentic Coding (SWE-Bench Pro): MiMo-V2.5-Pro (57.2%) matches MiniMax M2.7 (56.22%) and closely trails GLM-5.1 (58.4%)—frontier-competitive performance
- LiveCodeBench (complex evolving benchmarks): MiMo-V2.5-Pro (39.6%) significantly outpaces K2.5 (26.3%), indicating superior sustained iteration capability
Implication: MiMo-V2.5-Pro's agentic SWE performance validates Xiaomi's post-training strategy (SFT + agentic RL + MOPD). It achieves professional-grade coding without the specialized engineering focus of M2.7.
Source: MiMo-V2.5-Pro Model Card - Code Benchmarks
D. Long-Context Reasoning
GraphWalks Benchmark (OpenAI): Directed graph with hex-hash nodes; tasks: breadth-first search (find nodes at depth N) or list node parents. Evaluated across 32k–1M tokens.
| Context Length | MiMo-V2.5-Pro (BFS / Parents) | MiniMax M2.7 | GLM-5.1 | Notes |
|---|---|---|---|---|
| 128k | — | — | — | Standard range |
| 256k | — | — | — | Extended range |
| 512k | 0.56 / 0.92 | — | — | MiMo sustains well |
| 1M | 0.37 / 0.62 | — | — | Only model evaluated at 1M |
Critical Finding: MiMo-V2.5-Pro maintains meaningful performance at 1M tokens (BFS 0.37, Parents 0.62). The prior MiMo-V2 model degraded rapidly past 128k, collapsing to 0.00 at 1M. This represents a breakthrough in long-context capability for Xiaomi.
Source: MiMo-V2.5-Pro Technical Report | GraphWalks methodology from OpenAI Evals
E. Chinese Language & Multilingual
| Benchmark | MiMo-V2.5-Pro | Kimi K2.5 | MiniMax M2.7 | GLM-5.1 |
|---|---|---|---|---|
| C-Eval (5-shot) | 91.5% | 92.5% | 93.1% | 93.1% |
| CMMLУ (5-shot) | 90.2% | 90.9% | 90.8% | 90.9% |
| GlobalMMLU (5-shot) | 83.6% | 80.7% | — | — |
Interpretation: All four models perform comparably on Chinese benchmarks (91–93% range). MiMo-V2.5-Pro's GlobalMMLU score (83.6%) suggests strong multilingual generalization.
Source: MiMo-V2.5-Pro Model Card
III. Specialized Capabilities & Real-World Performance
MiMo-V2.5-Pro: Agentic Excellence & Harness Awareness
Unique Capability: "Harness Awareness" — actively managing context, shaping information flow toward objectives, and fully utilizing agent scaffolding
MiMo-V2.5-Pro demonstrates superior agentic performance through:
-
Multi-Teacher On-Policy Distillation (MOPD): Post-training uses diverse teacher models (math, safety, complex agentic tool-use) to train a single student via on-policy RL, enabling seamless capability integration
-
Sustained Tool Use: Real-world task—Peking University CS major project completed in 4.3 hours across 672 tool calls, scoring perfect 233/233 on hidden test suite (typical: several weeks)
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Complex EDA Tasks: Graduate-level analog circuit design (FVF-LDO optimization)—specifying 6 simultaneous metrics, tuning transistor sizing, compensation networks, bias voltages—completed autonomously
-
Inference Speed: 61.7 tokens/sec (above average for open-weight models; median 56.8 t/s)
Comparison with Asian Peers:
- vs. Kimi K2.5: K2.5 excels in multimodal grounding (vision + agents); MiMo-V2.5-Pro excels in pure agentic iteration without vision overhead
- vs. MiniMax M2.7: M2.7 focuses on autonomous self-optimization (meta-learning); MiMo-V2.5-Pro focuses on sustained multi-step task execution
- vs. GLM-5.1: Both excel in long-horizon iteration; GLM-5.1 emphasizes strategy revision; MiMo-V2.5-Pro emphasizes speed + scale
Source: Xiaomi MiMo-V2.5-Pro Technical Blog
Comparative Capability Matrix
| Capability | MiMo-V2.5-Pro | K2.5 | M2.7 | GLM-5.1 |
|---|---|---|---|---|
| Pure Reasoning | High (GPQA 66.7%) | Very High | Medium | High |
| Mathematical | Very High (MATH 86.2%) | High | Medium | Medium |
| Agentic Iteration | Very High (SWE 57.2%, 672 tool calls) | High (swarm) | High (self-evolving) | Very High |
| Long-Context | Exceptional (sustains to 1M) | Good (256K) | Good (256K+) | Good (256K+) |
| Speed/Efficiency | Very High (61.7 t/s, MTP 3x) | High | Medium | Medium |
| Multimodal | None | Excellent (native) | None | None |
| Self-Evolution | None | None | Unique | None |
IV. Architecture Deep Dive: Hybrid Attention
The Problem: Quadratic Complexity
Standard transformer attention is O(n²) in time and memory—for 1M-token contexts, this becomes prohibitive. Previous approaches:
- Sparse attention: Fast but loses long-range dependencies
- KV-cache compression: Sacrifices quality
- Positional interpolation: Crude and often breaks reasoning
MiMo-V2.5-Pro's Solution: Hybrid Attention
Architecture: Interleave Sliding Window Attention (SWA) with Global Attention (GA) at 6:1 ratio:
- 60 layers use SWA (128-token window)
- 10 layers use full global attention
- GQA (Grouped Query Attention): 8 KV heads
KV-Cache Reduction: ~7x compared to full attention, achieved through:
- Local attention (SWA) dominates computational cost
- Global attention appears periodically (every 6 layers) for long-range integration
- Learnable attention sink bias ensures stable behavior at context boundaries
- GQA reduces KV memory footprint
Result: 1M-token contexts become practical on standard infrastructure (e.g., A100, H100, or distributed setups)
Source: MiMo-V2.5-Pro Model Card - Architecture
V. Multi-Token Prediction (MTP): Speed via Prediction
Mechanism
MiMo-V2.5-Pro natively integrates 3 lightweight MTP modules (dense FFNs) that predict multiple tokens per forward pass:
- Training: MTP trains alongside autoregressive loss, learning to predict future tokens
- Inference: Generates 3+ tokens per forward pass, reducing latency by ~3x
- RL Training: Accelerates rollouts, enabling faster on-policy data collection for agentic training
Comparison: MTP vs. Speculative Decoding
| Approach | Integration | Speed Gain | RL Advantage |
|---|---|---|---|
| MTP (MiMo-V2.5-Pro) | Native (trained end-to-end) | 3x | Efficient (on-policy) |
| Speculative Decoding | Post-hoc (bolted on) | 1.5–2x | Limited (offline) |
MTP's native integration means it learns what to predict optimally, whereas speculative decoding uses separate draft models that may diverge.
Source: MiMo-V2.5-Pro Model Card - Training Process
VI. Training: 27T Tokens, FP8 Mixed Precision
Pre-Training Efficiency
- Data: 27T tokens (FP8 E4M3 mixed precision)
- Approach: FP8 quantization during training reduces memory and accelerates matrix operations
- Scaling: 1.02T total parameters trained efficiently on modern infrastructure
Post-Training: Three-Stage Paradigm
- Supervised Fine-Tuning (SFT): Foundational instruction-following using curated data
- Domain-Specialized RL: Diverse teacher models (math, safety, agentic tool-use) individually optimized with domain-specific rewards
- Multi-Teacher On-Policy Distillation (MOPD): Student model learns from expert teachers via on-policy RL, receiving precise token-level guidance for seamless capability integration
This three-stage approach explains MiMo-V2.5-Pro's balanced performance: it achieves frontier-level reasoning (mathematics), competitive software engineering (agentic RL), and sustained long-context reasoning.
Source: MiMo-V2.5-Pro Technical Report
VII. Deployment & Ecosystem
API Access
- Platform: https://mimo.xiaomi.com/mimo-v2-5-pro
- Status: Public beta as of April 22, 2026
- Compatibility: OpenAI/Anthropic-compatible API
Open-Source Local Deployment
Two Variants Available:
- MiMo-V2.5-Pro: 1M token context (full capability)
- MiMo-V2.5-Pro-Base: 256K token context (faster, lower memory)
Recommended Inference Engines:
-
SGLang (official recommendation): SGLang MiMo-V2.5-Pro Cookbook
- Spec: EAGLE speculative decoding + multi-layer EAGLE + chunked prefill
- Performance: 128+ concurrent requests on distributed setup
-
vLLM: vLLM MiMo-V2.5-Pro Cookbook
- Recommended settings: temperature=1.0, top_p=0.95
-
Hugging Face Transformers: Standard transformers library with trust-remote-code
Hardware Requirements:
- Single GPU: A100 (80GB) or H100 recommended for full 1M context
- Distributed: Multi-GPU via SGLang's PP/DP/EP/TP sharding (example: 16×16 TP/EP on A100s)
- Quantization: Native FP8 support via SGLang + DeepEP A2A backend
Source: MiMo-V2.5-Pro Model Card - Deployment
VIII. Strategic Positioning: MiMo-V2.5-Pro in the Asian Frontier Landscape
April 2026: Four Pillars of Asian Capability
| Model | Strength | Target Use | Parameters | Context |
|---|---|---|---|---|
| MiMo-V2.5-Pro | Balanced frontier (reasoning + agentic + speed) | General-purpose agentic AI | 1.02T / 42B active | 1M |
| Kimi K2.5 | Multimodal + agent swarm | Visual system design + orchestration | 1T / 32B active | 256K |
| MiniMax M2.7 | Professional software engineering + self-evolution | Production engineering + autonomous optimization | ~200B | 256K+ |
| GLM-5.1 | Long-horizon agentic iteration | Complex research + novel problem-solving | ~200B | 256K+ |
Competitive Differentiation
MiMo-V2.5-Pro's Niche: Balanced frontier capability with exceptional speed and long-context support
- vs. Kimi K2.5: Trades vision for mathematical reasoning (GPQA 66.7% vs. 48.1%) and speed (61.7 t/s)
- vs. MiniMax M2.7: Trades autonomous self-optimization for broader reasoning capability (MATH 86.2% vs. domain specialization)
- vs. GLM-5.1: Achieves comparable agentic performance (SWE 57.2% vs. 58.4%) with 3x faster inference (MTP)
Market Implications
- Geographic Diversification: Four independent systems reduce API dependency and increase supply chain resilience
- Specialization vs. Generalization: Market splitting into:
- Generalist frontier (MiMo-V2.5-Pro): broad capability + speed
- Specialists (K2.5 multimodal, M2.7 autonomous, GLM-5.1 iteration)
- Cost Efficiency: All four models available open-source; local deployment costs drop dramatically vs. API pricing
- Architectural Diversity: Hybrid attention (MiMo), multimodal (K2.5), self-evolution (M2.7), dynamic sparse (GLM-5.1) represent distinct technical approaches
IX. Technical Excellence: What MiMo-V2.5-Pro Reveals
1. Hybrid Attention Works
7x KV-cache reduction while maintaining 1M-token coherence demonstrates that interleaving local + global attention is a viable path forward. This could influence future model designs industry-wide.
2. Multi-Token Prediction Accelerates RL
Native MTP integration (not bolted-on speculative decoding) enables faster on-policy RL rollouts. MiMo's agentic performance suggests MTP-trained models have inherent advantages in iterative tasks.
3. FP8 Training is Mature
Training 1.02T parameters efficiently in FP8 mixed precision demonstrates that quantization is no longer experimental—it's production-ready at frontier scale.
4. Asian Infrastructure Converges on Frontier
Kimi (multimodal), MiniMax (self-evolving), GLM-5.1 (iterative), and now MiMo-V2.5-Pro (balanced) span the full spectrum of frontier approaches. No single Western model covers all these niches simultaneously.
X. Comparative Summary: All Four Asian Models
| Metric | MiMo-V2.5-Pro | Kimi K2.5 | MiniMax M2.7 | GLM-5.1 |
|---|---|---|---|---|
| Total / Active Params | 1.02T / 42B | 1T / 32B | ~200B / ~16B | ~200B / ~16B |
| Context Length | 1M | 256K | 256K+ | 256K+ |
| GPQA-Diamond | 66.7% | 48.1% | — | — |
| MATH (4-shot) | 86.2% | 70.2% | — | — |
| SWE-Bench Pro | 57.2% | 50.7% | 56.22% | 58.4% |
| Agentic Tool Calls | 672 (Peking U project) | Swarm capability | Autonomous self-opt | Extended iteration |
| Long-Context (GraphWalks 512k) | 0.56 BFS / 0.92 Parents | Unknown | Unknown | Unknown |
| Inference Speed | 61.7 t/s | High | Medium | Medium |
| Key Innovation | Hybrid attention + MTP speed | Multimodal + agent swarm | Model self-evolution | Sustained iteration |
| Deployment | API + open-source | API + open-source | API + open-source | API + open-source |
XI. What's Next for Asian AI (May 2026+)
Convergence Points
- Long-Context as Baseline: MiMo-V2.5-Pro's 1M-token achievement will likely become standard; others will follow
- Agentic RL Maturity: MOPD, self-evolution, and sustained iteration are now table stakes
- Hardware Optimization: Hybrid attention, MTP, and sparse MoE will drive inference engine development
- Cost Leadership: Open-source variants enable local deployment, commoditizing API pricing
Open Questions
- Vision Integration: Will MiMo-V2.5-Pro add multimodal capabilities (like K2.5)?
- Self-Evolution Scale: Can M2.7's autonomous optimization scale to 1T parameters?
- 1M-Context Applications: What new use cases emerge at 1M tokens?
- Energy Efficiency: How do hybrid attention + MTP compare to dense transformers on energy per inference?
References & Sources
- MiMo-V2.5-Pro Model Card: https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro
- MiMo-V2.5-Pro Technical Blog: https://mimo.xiaomi.com/mimo-v2-5-pro
- MiMo-V2.5-Pro Evaluation Results: Base Model Evaluation, Long-context Evaluation sections
- SGLang Deployment: https://docs.sglang.io/cookbook/autoregressive/Xiaomi/MiMo-V2.5
- vLLM Deployment: https://recipes.vllm.ai/XiaomiMiMo/MiMo-V2.5-Pro
- Kimi K2.5 Model Card: https://huggingface.co/moonshotai/Kimi-K2.5
- MiniMax M2.7 Model Card: https://huggingface.co/MiniMaxAI/MiniMax-M2.7
- GLM-5.1 Model Card: https://huggingface.co/zai-org/GLM-5.1
- GraphWalks Benchmark (OpenAI): Long-context evaluation methodology referenced in MiMo-V2.5-Pro technical documentation
Published: April 28, 2026 GMT+8
Classification: Research Article · Comparative Analysis
Status: Initial release · Based on official sources only
Xiaomi's MiMo-V2.5-Pro demonstrates that Asian frontier capability is no longer fragmented—it is multipolar, mature, and specialized. Four independent systems now cover the full spectrum of frontier approaches: balanced generalist reasoning, multimodal vision, autonomous self-optimization, and iterative problem-solving.
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