MiMo V2.6 Pro vs Qwen3.7 Plus
Compare MiMo V2.6 Pro 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 Pro 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 Pro vs Qwen3.7 Plus on Vision Evals
Qwen3.7 Plus scores higher on 4 of the five Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.7 Plus leads 60.1% to 42.0%.
Overall, MiMo V2.6 Pro averages 50.3% (#52 of 61) against 58.9% (#35 of 61) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0011 per sample) and faster (7.8s vs 9.5s per sample).
MiMo V2.6 Pro vs Qwen3.7 Plus Comparison Table
Evals updated October 8, 2026Pricing updated October 9, 2026
| Property | MiMo V2.6 Pro | Qwen3.7 Plus |
|---|---|---|
| Organization | Xiaomi | Qwen |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Sep 2026 | Jun 2026 |
| Context Window | 1.0M | — |
| Parameters | 1.02T total, 42B active | Unknown |
| License | MIT | Unknown |
| Pricing per 1M tokens | ||
| Input $/1M | $0.435 | $0.320 |
| Output $/1M | $0.870 | $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 | 50.3% | 58.9% |
| Avg cost / sample | $0.0011 | $0.0008 |
| Avg speed / sample | 9.45s | 7.77s |
| By task | ||
| Object Detection (low) | 42.0% ±1.1, Mean of 3 runs, range 40.9 to 43.1 | 60.1% |
| 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 | 50.0% |
| 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 | 84.4% |
| Identification (high) | 78.1% ±4.7, Mean of 3 runs, range 71.9 to 81.3 | – |
| OCR (low) | 48.6% | 60.3% |
| by category |
|
|
| OCR (high) | 49.9% | 65.5% |
| by category |
|
|
| Reasoning (low) | 35.1% ±2.6, Mean of 3 runs, range 32.5 to 37.8 | 39.7% |
| Reasoning (high) | 55.9% ±2.3, Mean of 3 runs, range 54.3 to 58.9 | 68.2% |
MiMo V2.6 Pro vs Qwen3.7 Plus: Overview
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. The resulting model targets long-horizon agentic work such as software engineering, terminal and computer-use operation, tool calling, cybersecurity analysis, and visual coding, and it reports gains over the prior MiMo generation on SWE-bench Verified, Terminal Bench, and internal visual coding and cyber benchmarks.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.7 Plus performed better. It scores higher on 4 of the five vision tasks and averages 58.9% (#35 of 61) against 50.3% (#52 of 61) for MiMo V2.6 Pro. 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 42.0%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.7 Plus is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0008 per sample against $0.0011. MiMo V2.6 Pro is priced at $0.43 per 1M input tokens and $0.87 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 9.5s. 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.