Kimi K2.5 vs MiMo V2.6 Pro
MiMo V2.6 Pro: 50.3% average across five Vision Evals tasks (#59 of 68 models). We have not run Kimi K2.5 through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
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Kimi K2.5 vs MiMo V2.6 Pro Comparison Table
Evals updated November 10, 2026Pricing updated November 12, 2026
| Property | Kimi K2.5 | MiMo V2.6 Pro |
|---|---|---|
| Organization | Moonshot AI | Xiaomi |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jan 2026 | Sep 2026 |
| Context Window | 256K | 1.0M |
| Parameters | 1T | 1.02T total, 42B active |
| License | Modified MIT | MIT |
| Commercial use | See license terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.532 | $0.435 |
| Output $/1M | $3.32 | $0.870 |
| Vision Tasks | ||
| Classification | Supported | Demo |
| object-detection | Not listed | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 50.3% |
| Avg cost / sample | – | $0.0011 |
| Avg speed / sample | – | 9.45s |
| By task | ||
| Object Detection (low) | – | 42.0% ±1.1, Mean of 3 runs, range 40.9 to 43.1 |
| Object Detection (high) | – | 46.7% ±0.8, Mean of 3 runs, range 45.7 to 47.3 |
| Counting (low) | – | 50.0% ±2.0, Mean of 3 runs, range 48.6 to 52.7 |
| Counting (high) | – | 59.0% ±5.4, Mean of 3 runs, range 52.7 to 63.5 |
| Identification (low) | – | 76.0% ±1.6, Mean of 3 runs, range 75.0 to 78.1 |
| Identification (high) | – | 78.1% ±4.7, Mean of 3 runs, range 71.9 to 81.3 |
| OCR (low) | – | 48.6% |
| by category |
| |
| OCR (high) | – | 49.9% |
| by category |
| |
| Reasoning (low) | – | 35.1% ±2.6, Mean of 3 runs, range 32.5 to 37.8 |
| Reasoning (high) | – | 55.9% ±2.3, Mean of 3 runs, range 54.3 to 58.9 |
Kimi K2.5 vs MiMo V2.6 Pro: Overview
Kimi K2.5 is a frontier-scale multimodal AI model developed by Moonshot AI and released on January 27, 2026. As a significant advancement within the Kimi K2 family, it utilizes a sparse Mixture-of-Experts (MoE) architecture with 1 trillion total parameters (32 billion active per inference) and a massive 256K-token context window. The model features native multimodal integration via a 400M-parameter MoonViT encoder, allowing it to process text, images, and video frames simultaneously. Built for both speed and depth, it offers "Instant" and "Thinking" modes, the latter of which excels at expert-level reasoning, scoring 50.2% on the Humanity’s Last Exam (HLE) benchmark when equipped with tools.
The model is released under a Modified MIT License, which remains open-weight but requires attribution for high-revenue commercial entities. It introduces an "Agent Swarm" paradigm capable of coordinating up to 100 specialized sub-agents for parallel workflows, significantly reducing latency in complex research tasks. For vision tasks, Kimi K2.5 demonstrates strong autonomous visual debugging capabilities, where it can inspect its own generated UI outputs against visual specifications to iteratively refine frontend code. This makes it a powerful choice for developers testing automated UI reconstruction, high-fidelity OCR document processing, and multi-step agentic research grounded in complex visual data.
MiMo V2.6 Pro is the flagship omni-modal foundation model in Xiaomi's MiMo V2.6 series, released as open weights alongside a Flash variant and a 9B distillation of Qwen3.5. It uses a sparse mixture-of-experts transformer with 1.02 trillion total parameters and roughly 42 billion activated per token, paired with a hybrid attention design that interleaves sliding-window and global attention layers to support a context window of about one million tokens. Dedicated encoders handle non-text inputs, including a vision encoder of roughly 681 million parameters and an audio tokenizer stack, so the model accepts text, images, video, and audio and returns text.
Post-training centers on large-scale reinforcement learning across thousands of interactive environments, combined with agentic grading, self-correction cold start, and a multi-prefix multi-teacher on-policy distillation stage that extends behavior to tasks that are hard to verify automatically.