Claude Sonnet 4.5 vs MiMo V2.6 Flash
Compare Claude Sonnet 4.5 and MiMo V2.6 Flash side-by-side. See how these vision models stack up in Object Detection, Classification, Image Captioning, OCR, and Open Prompt.
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Claude Sonnet 4.5 vs MiMo V2.6 Flash Comparison Table
Evals updated September 22, 2026Pricing updated September 23, 2026
| Property | Claude Sonnet 4.5 | MiMo V2.6 Flash |
|---|---|---|
| Organization | Anthropic | Xiaomi |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2025 | Sep 2026 |
| Context Window | 200K | 1.0M |
| Parameters | 309B total, 15B active | |
| License | Proprietary | MIT |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | $0.140 |
| Output $/1M | $15.00 | $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 |
Claude Sonnet 4.5 vs MiMo V2.6 Flash: Overview
Claude Sonnet 4.5, released by Anthropic in September 2025, is the company’s most advanced Sonnet-series model, built for high-performance reasoning, coding, and long-horizon agentic workflows. It is a multimodal system that accepts both text and images, with a 200,000-token context window designed for handling large documents and extended interactions. Anthropic highlights its improvements in reliability, reduced sycophancy, and alignment, making it suitable for sustained enterprise use.
The model delivers strong results in coding and autonomous workflows, achieving 61.4% on the OSWorld benchmark and leading performance on SWE-bench Verified. It introduces infrastructure features such as a memory tool (beta), checkpointing for Claude Code, parallel tool use, and tighter integration with VS Code. Compared to Opus, which targets broader reasoning, Sonnet 4.5 is optimized for structured, long-duration tasks. Positioned against leading offerings from OpenAI and Google, it is aimed at enterprise automation, software engineering, and research-intensive applications.
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
Claude Sonnet 4.5 has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
Claude Sonnet 4.5 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 object detection and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.