Roboflow

Gemini 2.5 Pro vs Qwen3 VL 235B A22B Instruct

Gemini 2.5 Pro and Qwen3 VL 235B A22B Instruct score 57.6% vs 55.2% averaged across five Vision Evals tasks (#46 and #54 of 69 models), so Gemini 2.5 Pro leads overall.

  • Gemini 2.5 Pro wins 4 of the 5 tasks, but the widest gap is object detection, where Qwen3 VL 235B A22B Instruct leads 52.1% vs 33.7%.
  • Qwen3 VL 235B A22B Instruct is about a fifth of the cost: $0.0011 vs $0.0051 overall average cost per sample.
  • Gemini 2.5 Pro is faster: 6.8s vs 15.9s per sample.

Compare Gemini 2.5 Pro vs Qwen3 VL 235B A22B Instruct live

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GoogleGemini 2.5 Pro
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Gemini 2.5 Pro vs Qwen3 VL 235B A22B Instruct on Vision Evals

Score by taskGemini 2.5 ProQwen3 VL 235B A22B Instruct
Object Detection
33.7
52.1
Reasoning
42.4
29.8
OCR
65.2
56.4
Counting
52.7
47.3
Identification
93.8
90.6

Roboflow Vision Evals. Full breakdown below.

Gemini 2.5 Pro vs Qwen3 VL 235B A22B Instruct Comparison Table

Evals updated November 12, 2026Pricing updated November 13, 2026

PropertyGemini 2.5 ProQwen3 VL 235B A22B Instruct
OrganizationGoogleQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateJun 2025Sep 2025
Context Window1.0M256K
ParametersUnknown235B
LicenseProprietaryApache 2.0
Commercial usecommercial use per vendor termsfree for commercial use
Pricing per 1M tokens
Input $/1M$1.25$0.210
Output $/1M$10.00$1.90
Vision Tasks
CaptioningDemoSupported
ClassificationDemoSupported
Object DetectionDemoSupported
OCRDemoSupported
Visual Question AnsweringDemoSupported
Both support 5 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
57.6%
55.2%
Avg cost / sample$0.0051$0.0011
Avg speed / sample6.83s15.87s
By task
Object Detection
33.7%
$0.010
52.1%
$0.0014
Counting
52.7%
$0.0012
47.3%
$0.0002
Identification
93.8%
$0.0012
90.6%
$0.0002
OCR (low)
65.2%
$0.0046
56.4%
$0.0013
by category
Single value
60.9%
Transcription
86.8%
Structured JSON
76.3%
Text localization
35.9%
Single value
48.3%
Transcription
84.3%
Structured JSON
71.0%
Text localization
24.0%
OCR (high)
64.6%
$0.025
55.5%
$0.0013
by category
Single value
63.0%
Transcription
87.3%
Structured JSON
78.3%
Text localization
20.6%
Single value
46.5%
Transcription
84.1%
Structured JSON
71.4%
Text localization
21.6%
Reasoning (low)
42.4%
$0.0013
29.8%
$0.0002
Reasoning (high)
62.3%
$0.011
33.8%
$0.0002

Gemini 2.5 Pro vs Qwen3 VL 235B A22B Instruct: Overview

Gemini 2.5 Pro

Gemini 2.5 Pro, released on June 17, 2025, is Google DeepMind’s most capable model in the Gemini 2.5 family, optimized for deep reasoning, coding, and complex multimodal tasks. It accepts text, images, audio, video, and PDFs as input and outputs text. The model supports 1 million input tokens with an output capacity of up to 65K tokens, enabling large-scale comprehension of datasets, codebases, and technical documents. Its training knowledge extends to January 2025.

It supports structured outputs, function calling, code execution, search grounding, and URL context, making it well-suited for enterprise, STEM, and developer workflows. However, it does not currently support image or audio generation in its stable release, and its higher computational cost and latency make it less efficient than Flash or Flash-Lite. It is available via the Gemini API, Google AI Studio, and Vertex AI.

Qwen3 VL 235B A22B Instruct

Qwen3 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.

The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.