Qwen3.5 35B A3B vs Qwen3.7 Plus
Compare Qwen3.5 35B A3B and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Models in this comparison
Qwen3.5 35B A3B vs Qwen3.7 Plus on Vision Evals
Qwen3.5 35B A3B scores higher on 2 of the 4 Vision Evals tasks.
The widest gap is Reasoning, where Qwen3.5 35B A3B leads 54.1% to 39.7%.
Overall, Qwen3.5 35B A3B averages 62.5% (#30 of 61) against 58.9% (#35 of 61) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0016 per sample) and faster (7.8s vs 31.9s per sample).
Qwen3.5 35B A3B vs Qwen3.7 Plus Comparison Table
Evals updated October 8, 2026Pricing updated October 9, 2026
| Property | Qwen3.5 35B A3B | Qwen3.7 Plus |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Feb 2026 | Jun 2026 |
| Context Window | 262K | — |
| Parameters | 35B | Unknown |
| License | Apache 2.0 | Unknown |
| Pricing per 1M tokens | ||
| Input $/1M | $0.080 | $0.320 |
| Output $/1M | $0.750 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Supported | Demo |
| Object Detection | Supported | 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 | 62.5% 4/5 tasks | 58.9% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0016 | $0.0008 |
| Avg speed / sample | 31.88s | 7.77s |
| By task | ||
| Object Detection | 52.9% ±3.2, Mean of 3 runs, range 49.5 to 55.9 | 60.1% |
| Counting | 62.6% ±2.0, Mean of 3 runs, range 60.8 to 64.9 | 50.0% |
| Identification | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | 84.4% |
| OCR (low) | – | 60.3% |
| by category |
| |
| OCR (high) | – | 65.5% |
| by category |
| |
| Reasoning (low) | 54.1% ±0.3, Mean of 3 runs, range 53.6 to 54.3 | 39.7% |
| Reasoning (high) | – | 68.2% |
Qwen3.5 35B A3B vs Qwen3.7 Plus: Overview
The Qwen3.5-35B-A3B is a native vision-language model developed by Alibaba Cloud’s Qwen team, released on February 24, 2026, as a high-efficiency entry in the Qwen 3.5 family. It utilizes a sophisticated hybrid architecture that integrates Gated Delta Networks with a sparse Mixture-of-Experts (MoE) system. While the model houses 35 billion total parameters, its routing mechanism activates only 8 routed experts and 1 shared expert per token, totaling approximately 3 billion active parameters. This design achieves cross-generational parity with the previous flagship Qwen3-235B dense model, delivering comparable reasoning and multimodal intelligence with significantly reduced inference latency and compute requirements. Available under the Apache 2.0 license, it is released in both base and instruction-tuned variants for seamless integration with open-source stacks like vLLM and Hugging Face Transformers.
Designed for the emerging era of agentic AI, the model utilizes a unified multimodal foundation built through early-fusion training. This approach allows it to outperform the prior Qwen3-VL series in spatial grounding, document analysis, and UI/GUI interaction. It features a native context window of 262,144 tokens, which is extensible up to 1,010,000 tokensvia RoPE scaling, and provides global support for 201 languages and dialects. This combination of a compact active parameter count and frontier-level visual comprehension makes it a versatile tool for developers requiring a balance of high-throughput speed and sophisticated visual reasoning for long-context workflows.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.5 35B A3B performed slightly better overall. The two split the 4 vision tasks 2 to 2, but Qwen3.5 35B A3B averages 62.5% (#30 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.
Yes. On the Vision Evals Reasoning benchmark at low effort, Qwen3.5 35B A3B leads with 54.1% 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 $0.0016. 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 31.9s. 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.