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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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QwenQwen3.5 35B A3B
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QwenQwen3.7 Plus
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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 A3BQwen3.7 Plus

Qwen3.5 35B A3B vs Qwen3.7 Plus Comparison Table

Evals updated October 8, 2026Pricing updated October 9, 2026

PropertyQwen3.5 35B A3BQwen3.7 Plus
OrganizationQwenQwen
Categoryopenclosed
Modalitymultimodal—
Release DateFeb 2026Jun 2026
Context Window262K—
Parameters35BUnknown
LicenseApache 2.0Unknown
Pricing per 1M tokens
Input $/1M$0.080$0.320
Output $/1M$0.750$1.28
Vision Tasks
CaptioningDemoDemo
ClassificationSupportedDemo
Object DetectionSupportedDemo
OCRDemoDemo
Visual Question AnsweringDemoDemo
Chart Question AnsweringSupportedNot listed
Document Question AnsweringSupportedNot listed
Image TaggingSupportedNot listed
Multi-Label ClassificationSupportedNot listed
Vision LanguageSupportedNot listed
Model Features
Foundation VisionSupportedNot listed
LLMs with Vision CapabilitiesSupportedNot listed
Multimodal VisionSupportedNot listed
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
62.5%
4/5 tasks
58.9%
Quantizationsself-hosted
FP859.8%GPTQ-INT462.5%hardware →
Avg cost / sample$0.0016$0.0008
Avg speed / sample31.88s7.77s
By task
Object Detection
52.9%
±3.2, Mean of 3 runs, range 49.5 to 55.9
$0
60.1%
$0.0013
Counting
62.6%
±2.0, Mean of 3 runs, range 60.8 to 64.9
$0
50.0%
$0.0004
Identification
80.2%
±1.6, Mean of 3 runs, range 78.1 to 81.3
$0
84.4%
$0.0003
OCR (low)–
60.3%
$0.0009
by category
Single value
53.5%
Transcription
86.7%
Structured JSON
75.8%
Text localization
23.1%
OCR (high)–
65.5%
$0.0042
by category
Single value
58.3%
Transcription
89.7%
Structured JSON
81.3%
Text localization
30.4%
Reasoning (low)
54.1%
±0.3, Mean of 3 runs, range 53.6 to 54.3
$0
39.7%
$0.0003
Reasoning (high)–
68.2%
$0.0043

Qwen3.5 35B A3B vs Qwen3.7 Plus: Overview

Qwen3.5 35B A3B

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.

Qwen3.7 Plus
No description available

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.