Qwen3.7 Plus vs Qwen3.8 Max
Compare Qwen3.7 Plus and Qwen3.8 Max side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, Object Detection, and OCR.
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Models in this comparison
Qwen3.7 Plus vs Qwen3.8 Max on Vision Evals
Qwen3.8 Max scores higher on all five Vision Evals tasks.
The widest gap is Reasoning, where Qwen3.8 Max leads 75.9% to 39.7%.
Overall, Qwen3.7 Plus averages 58.9% (#37 of 63) against 79.3% (#8 of 63) for Qwen3.8 Max.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0080 per sample) and faster (7.8s vs 18.9s per sample).
Qwen3.7 Plus vs Qwen3.8 Max Comparison Table
Evals updated October 13, 2026Pricing updated October 16, 2026
| Property | Qwen3.7 Plus | Qwen3.8 Max |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | closed | closed |
| Modality | — | multimodal |
| Release Date | Jun 2026 | Aug 2026 |
| Context Window | — | 984K |
| Parameters | Unknown | 2.4T total, ~95B active |
| License | Unknown | Apache 2.0 |
| Commercial use | See license terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.320 | No published price |
| Output $/1M | $1.28 | No published price |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | Not listed | Supported |
| Document Question Answering | Not listed | Supported |
| Image Tagging | Not listed | Supported |
| Multi-Label Classification | Not listed | Supported |
| Vision Language | Not listed | Supported |
| Model Features | ||
| Foundation Vision | Not listed | Supported |
| LLMs with Vision Capabilities | Not listed | Supported |
| Multimodal Vision | Not listed | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 58.9% | 79.3% |
| Avg cost / sample | $0.0008 | $0.0080 |
| Avg speed / sample | 7.77s | 18.95s |
| By task | ||
| Object Detection (low) | 60.1% | 76.7% ±0.3, Mean of 3 runs, range 76.5 to 77.1 |
| Object Detection (high) | – | 78.4% ±0.4, Mean of 3 runs, range 78.1 to 78.9 |
| Counting (low) | 50.0% | 81.1% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | – | 81.1% ±0.0, Mean of 3 runs, range 81.1 to 81.1 |
| Identification (low) | 84.4% | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | – | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 60.3% | 74.0% |
| by category |
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| OCR (high) | 65.5% | 71.8% |
| by category |
|
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| Reasoning (low) | 39.7% | 75.9% ±2.0, Mean of 3 runs, range 73.5 to 77.5 |
| Reasoning (high) | 68.2% | 80.3% ±2.0, Mean of 3 runs, range 78.2 to 82.1 |
Qwen3.7 Plus vs Qwen3.8 Max: Overview
Qwen3.8 Max is the flagship tier of Alibaba's Qwen3.8 family, a sparse mixture-of-experts multimodal model with roughly 2.4 trillion total parameters of which about 95 billion activate per token, which keeps serving cost and latency well below what the total parameter count would imply. It builds on the architectural foundation established by Qwen3.5 and accepts text, images, video, and documents as input while producing text output. Reported context handling reaches close to one million tokens, with a maximum generation length of 131,072 tokens, so the model is aimed at long-horizon agentic work such as repository-scale coding, multi-step research, data analysis, and office document workflows.
For vision work the model performs image and video understanding, document and chart interpretation, text recognition inside images, and grounded visual question answering, and Alibaba reports gains concentrated in multimodal and agentic evaluation categories rather than general reasoning. Published figures include 86.6 on Terminal-Bench 2.1, 67.7 on SWE-bench Pro, 93.0 on PaperBench, 82.8 on IFBench, and 92.6 on GPQA Diamond. It is the first model in the Max tier of the Qwen line for which the team states weights will be released publicly, alongside a smaller Qwen3.8 27B checkpoint. No training or safety model card has been published.