Qwen3.5 35B A3B vs SAM 3
Qwen3.5 35B A3B: 62.5% average across four Vision Evals tasks (#34 of 65 models). We have not run SAM 3 through Roboflow's Vision Evals, so there is no head-to-head score.
Compare Qwen3.5 35B A3B vs SAM 3 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Qwen3.5 35B A3B vs SAM 3 Comparison Table
Evals updated October 26, 2026Pricing updated November 8, 2026
| Property | Qwen3.5 35B A3B | SAM 3 |
|---|---|---|
| Organization | Qwen | Meta |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Nov 2025 |
| Context Window | 262K | — |
| Parameters | 35B | Unknown |
| License | Apache 2.0 | Custom |
| Commercial use | free for commercial use | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.140 | No published price |
| Output $/1M | $1.00 | No published price |
| Vision Tasks | ||
| Object Detection | Supported | Demo |
| Captioning | Demo | Not listed |
| Chart Question Answering | Supported | Not listed |
| Classification | Supported | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Instance Segmentation | Not listed | Supported |
| Multi-Label Classification | Supported | Not listed |
| OCR | Demo | Not listed |
| Open Vocabulary Object Detection | Not listed | Supported |
| Promptable Concept Segmentation | Not listed | Demo |
| Video Object Tracking | Not listed | Supported |
| Vision Language | Supported | Not listed |
| Visual Question Answering | Demo | Not listed |
| Zero Shot Segmentation | Not listed | Supported |
| Model Features | ||
| LLMs with Vision Capabilities | Supported | Not listed |
| Zero-shot Detection | Not listed | Supported |
Both support 2 more features | ||
Vision Evalsground-truth scores across 5 vision tasks | ||
| Overall | 62.5%* | Not evaluated |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0016 | – |
| Avg speed / sample | 31.88s | – |
| By task | ||
| Object Detection | 52.9% ±3.2, Mean of 3 runs, range 49.5 to 55.9 | – |
| Counting | 62.6% ±2.0, Mean of 3 runs, range 60.8 to 64.9 | – |
| Identification | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | – |
| OCR | – | – |
| by category | ||
| Reasoning | 54.1% ±0.3, Mean of 3 runs, range 53.6 to 54.3 | – |
Qwen3.5 35B A3B vs SAM 3: 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.
Released on November 19th, 2025, Segment Anything 3 (SAM 3) is a zero-shot image segmentation model that “detects, segments, and tracks objects in images and videos based on concept prompts.” This model was developed by Meta as the third model in the Segment Anything series.
Unlike its previous SAM models (Segment Anything and Segment Anything 2), you can provide SAM 3 with the prompt “shipping container” and it will generate precise segmentation masks for all shipping containers in an image. SAM 3 generates segmentation masks that correspond to the location of the objects found with a text prompt.
