Gemini 2.5 Flash vs MiMo V2.6 Flash
Compare Gemini 2.5 Flash and MiMo V2.6 Flash side-by-side. See how these vision models stack up in Open Prompt, OCR, Classification, Image Captioning, and Object Detection.
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Gemini 2.5 Flash vs MiMo V2.6 Flash Comparison Table
Evals updated September 22, 2026Pricing updated September 23, 2026
| Property | Gemini 2.5 Flash | MiMo V2.6 Flash |
|---|---|---|
| Organization | Xiaomi | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2025 | Sep 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | 309B total, 15B active | |
| License | Proprietary | MIT |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | $0.140 |
| Output $/1M | $2.50 | $0.280 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 60.6% |
| Avg cost / sample | – | $0.0003 |
| Avg speed / sample | – | 9.20s |
| By task | ||
| Object Detection (low) | – | 37.8% ±1.1, Mean of 3 runs, range 36.4 to 38.7 |
| Object Detection (high) | – | 45.0% ±2.5, Mean of 3 runs, range 42.2 to 47.1 |
| Counting (low) | – | 49.5% ±8.1, Mean of 3 runs, range 41.9 to 58.1 |
| Counting (high) | – | 64.9% ±1.4, Mean of 3 runs, range 63.5 to 66.2 |
| Identification (low) | – | 76.0% ±3.1, Mean of 3 runs, range 71.9 to 78.1 |
| Identification (high) | – | 82.3% ±4.7, Mean of 3 runs, range 78.1 to 87.5 |
| OCR (low) | – | 87.0% ±0.5, Mean of 3 runs, range 86.6 to 87.7 |
| OCR (high) | – | 87.0% ±2.1, Mean of 3 runs, range 84.3 to 88.5 |
| Data Extraction (low) | – | 80.1% ±1.0, Mean of 3 runs, range 79.4 to 81.4 |
| Data Extraction (high) | – | 82.5% ±1.0, Mean of 3 runs, range 81.4 to 83.5 |
| Reasoning (low) | – | 33.1% ±2.0, Mean of 3 runs, range 31.1 to 35.1 |
| Reasoning (high) | – | 58.5% ±1.3, Mean of 3 runs, range 57.0 to 59.6 |
Gemini 2.5 Flash vs MiMo V2.6 Flash: Overview
Gemini 2.5 Flash, released on June 17, 2025, is Google DeepMind’s production-ready, efficiency-focused model in the Gemini 2.5 family. It is multimodal, accepting text, images, video, and audio as inputs, with text as the primary output format. The model supports 1 million input tokens and up to 65K output tokens, enabling it to process very large contexts such as books, long video transcripts, or extensive datasets. Its training knowledge extends to January 2025.
Designed as a price-performance leader, Gemini 2.5 Flash balances speed and reasoning power, making it suitable for everyday enterprise and developer use cases without the higher latency and cost of Pro models. It supports advanced workflows like function calling, code execution, search grounding, URL context ingestion, and structured outputs. While efficient and scalable, output length is still limited compared to its input capacity, and multimodal outputs (e.g. image or audio generation) remain restricted to specialized or preview variants.
MiMo-V2.6-Flash is the efficiency-oriented checkpoint of Xiaomi's MiMo-V2.6 series, a natively omnimodal foundation model that accepts text, image, video, and audio in a single model and supports a one million token context window. The language backbone is a sparse mixture-of-experts transformer with roughly 309 billion total parameters and 15 billion activated per token, organized as 48 layers with 256 routed experts and top-8 routing. It uses a hybrid attention scheme that interleaves sliding window attention with global attention layers to cut key-value cache cost on long sequences, and pairs the backbone with a vision encoder, an audio encoder, and an audio tokenizer, plus a multi-token prediction module and a draft model for faster decoding.
Training emphasizes large scale reinforcement learning on verifiable, long-horizon tasks, with RL compute, environment diversity, and grader compute scaled together in a single mixed run. Xiaomi reports gains during RL on SWE-bench Verified, Terminal Bench, a cybersecurity benchmark, and an internal visual coding benchmark, reflecting a focus on agentic coding, computer use, and multimodal document and screen understanding rather than single turn chat.
Frequently Asked Questions
Gemini 2.5 Flash has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
Gemini 2.5 Flash is released under Proprietary, while MiMo V2.6 Flash uses MIT. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.
Yes. The comparison demo on this page runs both models on the same image side by side for open prompts and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.