Qwen3.5 35B A3B vs Qwen3.6 27B
Qwen3.6 27B and Qwen3.5 35B A3B score 67.1% vs 62.5% averaged across four Vision Evals tasks (#28 and #36 of 67 models), so Qwen3.6 27B leads overall.
- Qwen3.6 27B wins all 4 tasks, with its widest lead on object detection (59.7% vs 52.9%).
- Qwen3.5 35B A3B is cheaper: $0.0016 vs $0.0021 overall average cost per sample.
- Qwen3.5 35B A3B is faster: 31.9s vs 42.1s per sample.
Roboflow Vision Evals. Full breakdown below.
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Qwen3.5 35B A3B vs Qwen3.6 27B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Qwen3.5 35B A3B vs Qwen3.6 27B Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Qwen3.5 35B A3B | Qwen3.6 27B |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Apr 2026 |
| Context Window | 262K | 262K |
| Parameters | 35B | 27B |
| License | Apache 2.0 | Apache 2.0 |
| Commercial use | free for commercial use | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.140 | $0.320 |
| Output $/1M | $1.00 | $3.20 |
| Vision Tasks | ||
| Object Detection | Supported | Not listed |
| Video Classification | Not listed | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks | ||
| Overall | 62.5%* | 67.1%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0016 | $0.0021 |
| Avg speed / sample | 31.88s | 42.09s |
| By task | ||
| Object Detection | 52.9% ±3.2, Mean of 3 runs, range 49.5 to 55.9 | 59.7% ±0.9, Mean of 3 runs, range 59.0 to 60.8 |
| Counting | 62.6% ±2.0, Mean of 3 runs, range 60.8 to 64.9 | 67.1% ±4.7, Mean of 3 runs, range 62.2 to 71.6 |
| Identification | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | 82.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| OCR | – | – |
| by category | ||
| Reasoning | 54.1% ±0.3, Mean of 3 runs, range 53.6 to 54.3 | 59.2% ±1.7, Mean of 3 runs, range 57.6 to 60.9 |
Qwen3.5 35B A3B vs Qwen3.6 27B: 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.
Qwen3.6-27B is a dense 27-billion-parameter multimodal language model developed by Alibaba's Qwen team and released on April 22, 2026. It combines a causal language model with an integrated vision encoder, supporting text, image, and video inputs natively. The architecture employs a hybrid attention design that interleaves Gated DeltaNet linear attention blocks with standard Gated Attention layers across 64 transformer layers with a hidden dimension of 5,120. Unlike Mixture-of-Experts variants in the Qwen3.6 family, all 27 billion parameters are active on every inference pass, simplifying deployment and quantization. The model supports a native context window of 262,144 tokens, extensible to approximately 1,010,000 tokens via YaRN scaling. It is released under the Apache 2.0 license with open weights available on Hugging Face and ModelScope.
The model introduces two notable capabilities relative to prior Qwen releases: enhanced agentic coding support covering frontend workflows and repository-level reasoning, and a Thinking Preservation mechanism that retains chain-of-thought reasoning context across multi-turn conversation history to reduce redundant token generation in iterative agent sessions. It supports both a thinking mode for multi-step reasoning and a non-thinking mode for faster responses within a single model. Vision capabilities include chart understanding (CharXiv RQ: 78.4), OCR (CC-OCR: 81.2), and video understanding (VideoMME with subtitles: 87.7).
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.6 27B performed better. It scores higher on all 4 vision tasks and averages 67.1% (#28 of 67) against 62.5% (#36 of 67) for Qwen3.5 35B A3B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Qwen3.6 27B leads with 59.7% against 52.9%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.5 35B A3B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0016 per sample against $0.0021. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.5 35B A3B is faster. Across Roboflow's Vision Evals it averaged 31.9s per inference against 42.1s. 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.