Qwen3.5 35B A3B vs SAM 3
Compare Qwen3.5 35B A3B and SAM 3 side-by-side.
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.
Models in this comparison
Qwen3.5 35B A3B vs SAM 3 Comparison Table
Evals updated September 22, 2026Pricing updated September 23, 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 | |
| License | Apache 2.0 | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $0.313 | |
| Output $/1M | $1.25 | |
| Vision Tasks | ||
| Object Detection | Demo | |
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | ||
| Document Question Answering | ||
| Image Tagging | ||
| Instance Segmentation | ||
| Multi-Label Classification | ||
| OCR | Demo | |
| Open Vocabulary Object Detection | ||
| Promptable Concept Segmentation | Demo | |
| Video Object Tracking | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Zero Shot Segmentation | ||
| Model Features | ||
| Foundation Vision | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
| Zero-shot Detection | ||
Vision Evalsground-truth scores across 6 vision tasks | ||
| Overall | 69.4% | 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 | 83.0% ±0.4, Mean of 3 runs, range 82.7 to 83.5 | – |
| Data Extraction | 83.5% ±2.6, Mean of 3 runs, range 80.4 to 85.6 | – |
| 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.
Frequently Asked Questions
SAM 3 has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
Qwen3.5 35B A3B is released under Apache 2.0, while SAM 3 uses Custom. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.
