MiMo V2.6 Flash vs Qwen3.7 Plus
Compare MiMo V2.6 Flash and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, Classification, Object Detection, and Open Prompt.
Compare MiMo V2.6 Flash vs Qwen3.7 Plus live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Detect and compare bounding boxes across models on the same image.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
MiMo V2.6 Flash vs Qwen3.7 Plus on Vision Evals
Qwen3.7 Plus scores higher on all five Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.7 Plus leads 60.1% to 37.8%.
Overall, MiMo V2.6 Flash averages 49.2% (#55 of 63) against 58.9% (#37 of 63) for Qwen3.7 Plus.
MiMo V2.6 Flash is cheaper ($0.0003 vs $0.0008 per sample), while Qwen3.7 Plus is faster (7.8s vs 16.3s per sample).
MiMo V2.6 Flash vs Qwen3.7 Plus Comparison Table
Evals updated October 13, 2026Pricing updated October 17, 2026
| Property | MiMo V2.6 Flash | Qwen3.7 Plus |
|---|---|---|
| Organization | Xiaomi | Qwen |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Sep 2026 | Jun 2026 |
| Context Window | 1.0M | — |
| Parameters | 309B total, 15B active | Unknown |
| License | MIT | Unknown |
| Commercial use | free for commercial use | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.140 | $0.320 |
| Output $/1M | $0.280 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| object-detection | Demo | Demo |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | Supported | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Multi-Label Classification | Supported | Not listed |
| Vision Language | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 49.2% | 58.9% |
| Avg cost / sample | $0.0003 | $0.0008 |
| Avg speed / sample | 16.34s | 7.77s |
| By task | ||
| Object Detection (low) | 37.8% ±1.1, Mean of 3 runs, range 36.4 to 38.7 | 60.1% |
| 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 | 50.0% |
| 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 | 84.4% |
| Identification (high) | 82.3% ±4.7, Mean of 3 runs, range 78.1 to 87.5 | – |
| OCR (low) | 49.7% | 60.3% |
| by category |
|
|
| OCR (high) | 50.4% | 65.5% |
| by category |
|
|
| Reasoning (low) | 33.1% ±2.0, Mean of 3 runs, range 31.1 to 35.1 | 39.7% |
| Reasoning (high) | 58.5% ±1.3, Mean of 3 runs, range 57.0 to 59.6 | 68.2% |
MiMo V2.6 Flash vs Qwen3.7 Plus: Overview
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
On Roboflow's Vision Evals, Qwen3.7 Plus performed better. It scores higher on all five vision tasks and averages 58.9% (#37 of 63) against 49.2% (#55 of 63) for MiMo V2.6 Flash. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Qwen3.7 Plus leads with 60.1% against 37.8%. This is the widest gap between the two models across the benchmark's tasks.
MiMo V2.6 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0003 per sample against $0.0008. MiMo V2.6 Flash is priced at $0.14 per 1M input tokens and $0.28 per 1M output; Qwen3.7 Plus is priced at $0.32 per 1M input tokens and $1.28 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.7 Plus is faster. Across Roboflow's Vision Evals it averaged 7.8s per inference against 16.3s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.