Qwen3.7 Plus vs Qwen3.8 27B
Compare Qwen3.7 Plus and Qwen3.8 27B 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 27B on Vision Evals
Qwen3.8 27B scores higher on all 4 Vision Evals tasks.
The widest gap is Reasoning, where Qwen3.8 27B leads 62.0% to 39.7%.
Overall, Qwen3.7 Plus averages 58.9% (#35 of 61) against 69.5% (#21 of 61) for Qwen3.8 27B.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0009 per sample) and faster (7.8s vs 18.0s per sample).
Qwen3.7 Plus vs Qwen3.8 27B Comparison Table
Evals updated October 8, 2026Pricing updated October 8, 2026
| Property | Qwen3.7 Plus | Qwen3.8 27B |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | closed | open |
| Modality | — | multimodal |
| Release Date | Jun 2026 | Aug 2026 |
| Context Window | — | 262K |
| Parameters | Unknown | 27.78B |
| License | Unknown | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.320 | $0.425 |
| Output $/1M | $1.28 | $2.55 |
| 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% | 69.5% 4/5 tasks |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0008 | $0.0009 |
| Avg speed / sample | 7.77s | 17.99s |
| By task | ||
| Object Detection (low) | 60.1% | 65.7% ±1.0, Mean of 3 runs, range 64.6 to 66.5 |
| Object Detection (high) | – | 66.1% ±1.4, Mean of 3 runs, range 64.9 to 67.8 |
| Counting (low) | 50.0% | 64.9% ±4.1, Mean of 3 runs, range 60.8 to 68.9 |
| Counting (high) | – | 68.0% ±2.0, Mean of 3 runs, range 66.2 to 70.3 |
| Identification (low) | 84.4% | 85.4% ±4.7, Mean of 3 runs, range 81.3 to 90.6 |
| Identification (high) | – | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR (low) | 60.3% | – |
| by category |
| |
| OCR (high) | 65.5% | – |
| by category |
| |
| Reasoning (low) | 39.7% | 62.0% ±2.0, Mean of 3 runs, range 60.3 to 64.2 |
| Reasoning (high) | 68.2% | 66.0% ±0.7, Mean of 3 runs, range 65.6 to 66.9 |
Qwen3.7 Plus vs Qwen3.8 27B: Overview
Qwen3.8-27B is a dense vision-language model of roughly 27.78 billion parameters from Alibaba's Qwen team, built on the architectural foundation established by the Qwen3.5 generation. It takes interleaved text, image, and video input through an integrated vision encoder and produces text, covering document and chart understanding, visual question answering, front-end code generation from visual references, and agentic computer-use workflows. The decoder is a hybrid stack of 64 layers that repeats a pattern of three Gated DeltaNet linear-attention blocks, each followed by a feed-forward block, then one grouped-query full-attention block, so only a quarter of the layers maintain a conventional key-value cache. Native context length is 262,144 tokens.
Post-training targets long-horizon agentic work, and the model exposes per-request thinking control that lets callers switch reasoning traces on or off and choose a reasoning effort level. Multi-token prediction weights are trained alongside the main network for speculative decoding. Qwen reports improvements over Qwen3.6-27B on agentic and multimodal evaluations including Terminal-Bench 2.1, OSWorld-Verified, and SWE-bench Multimodal, with additional results on MathVision, CharXiv, and an internal Vision2Web suite.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.8 27B performed better. It scores higher on all 4 vision tasks and averages 69.5% (#21 of 61) against 58.9% (#35 of 61) for Qwen3.7 Plus. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Reasoning benchmark at low effort, Qwen3.8 27B leads with 62.0% against 39.7%. 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 $0.0009. 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 18.0s. 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 open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.