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Gemini 3.8 Flash vs Qwen3.5 9b

Compare Gemini 3.8 Flash and Qwen3.5 9b side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

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GoogleGemini 3.8 Flash
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QwenQwen3.5 9b
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Models in this comparison

Gemini 3.8 Flash vs Qwen3.5 9b on Vision Evals

Gemini 3.8 Flash scores higher on all 4 Vision Evals tasks.

The widest gap is Reasoning, where Gemini 3.8 Flash leads 81.2% to 45.9%.

Overall, Gemini 3.8 Flash averages 79.8% (#7 of 63) against 56.0% (#42 of 63) for Qwen3.5 9b.

Qwen3.5 9b is cheaper ($0.0021 vs $0.0045 per sample), while Gemini 3.8 Flash is faster (12.8s vs 41.4s per sample).

Gemini 3.8 FlashQwen3.5 9b

Gemini 3.8 Flash vs Qwen3.5 9b Comparison Table

Evals updated October 13, 2026Pricing updated October 17, 2026

PropertyGemini 3.8 FlashQwen3.5 9b
OrganizationGoogleQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateSep 2026Mar 2026
Context Window1.0M262K
ParametersUnknown9B
LicenseProprietaryApache 2.0
Commercial usecommercial use per vendor termsfree for commercial use
Pricing per 1M tokens
Input $/1M$0.825$0.100
Output $/1M$4.13$0.150
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoSupported
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoSupported
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
79.8%
56.0%
4/5 tasks
Quantizationsself-hosted
BF1656.0%FP854.8%AWQ-INT457.4%hardware →
Avg cost / sample$0.0045$0.0021
Avg speed / sample12.76s41.36s
By task
Object Detection (low)
68.1%
±0.8, Mean of 3 runs, range 67.3 to 69.0
$0.0045
38.1%
±5.7, Mean of 3 runs, range 33.5 to 44.9
$0
Object Detection (high)
74.8%
±1.1, Mean of 3 runs, range 73.4 to 75.6
$0.023
–
Counting (low)
78.8%
±2.7, Mean of 3 runs, range 75.7 to 81.1
$0.0040
56.8%
±1.4, Mean of 3 runs, range 55.4 to 58.1
$0
Counting (high)
79.3%
±1.4, Mean of 3 runs, range 78.4 to 81.1
$0.027
–
Identification (low)
97.9%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.0016
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0037
–
OCR (low)
72.8%
$0.0049
–
by category
Single value
73.5%
Transcription
91.3%
Structured JSON
86.1%
Text localization
26.4%
OCR (high)
78.7%
$0.039
–
by category
Single value
70.9%
Transcription
94.1%
Structured JSON
90.9%
Text localization
58.6%
Reasoning (low)
81.2%
±0.3, Mean of 3 runs, range 80.8 to 81.5
$0.0038
45.9%
±1.7, Mean of 3 runs, range 44.4 to 47.7
$0
Reasoning (high)
84.5%
±1.0, Mean of 3 runs, range 83.4 to 85.4
$0.023
–

Gemini 3.8 Flash vs Qwen3.5 9b: Overview

Gemini 3.8 Flash

Gemini 3.8 Flash is a natively multimodal reasoning model in Google's Gemini 3 series, positioned as the speed and cost oriented Flash tier while targeting long-horizon software engineering, autonomous agents, and enterprise workflows. It accepts text, images, video, audio, and PDF documents in a single request and returns text, with an input limit of 1,048,576 tokens and an output limit of 65,536 tokens. Thinking is configurable at low, medium, and high levels, and the model supports function calling, code execution, structured outputs, context caching, search and Maps grounding, file search, and computer use in preview. Image generation, audio generation, and the Live API are not supported.

On vision oriented evaluations the model reports 86.2% on CharXiv Reasoning for chart and figure synthesis and 87.8% on LVBench for long video understanding in agentic mode, alongside 90.8% on Terminal-Bench 2.1 and 61.6% on SWE-Bench Pro for coding. Following Gemini API conventions, it can localize objects by emitting bounding boxes as [ymin, xmin, ymax, xmax] integers normalized to a 0 to 1000 range, which supports prompt driven detection and grounding in addition to captioning, document parsing, and visual question answering. The knowledge cutoff is March 2026, though coverage in some domains reflects the January 2025 cutoff shared across the Gemini 3 family.

Qwen3.5 9b

Qwen3.5-9B is a 9-billion-parameter multimodal foundation model developed by Alibaba Cloud's Qwen team, released on March 2, 2026 as part of the Qwen3.5 model family. Designed for efficient multimodal reasoning and long-context language tasks, it notably outperforms the older Qwen3-30B, a model more than three times its size, on key benchmarks including GPQA Diamond, IFEval, and LongBench.

The model supports vision-language inputs through an early-fusion multimodal architecture built on a dense hybrid foundation of Gated Delta Networks and Gated Attention. It can also operate in a text-only mode by skipping the vision encoder during inference. It provides a 262,144-token context window (extensible to ~1M tokens via YaRN) and is released under the Apache License 2.0. Within the current AI landscape, Qwen3.5-9B offers a strong balance of capability and efficiency, making it well-suited for multimodal assistants, document analysis, long-context reasoning, and developer-deployed agentic systems.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 3.8 Flash performed better. It scores higher on all 4 vision tasks and averages 79.8% (#7 of 63) against 56.0% (#42 of 63) for Qwen3.5 9b. 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, Gemini 3.8 Flash leads with 81.2% against 45.9%. This is the widest gap between the two models across the benchmark's tasks.

Qwen3.5 9b is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0021 per sample against $0.0045. Actual costs depend on your image sizes, prompts, and output length.

Gemini 3.8 Flash is faster. Across Roboflow's Vision Evals it averaged 12.8s per inference against 41.4s. 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.