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Gemini 3.1 Pro vs GLM 5V Turbo

Compare Gemini 3.1 Pro and GLM 5V Turbo side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.

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GoogleGemini 3.1 Pro
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Z.aiGLM 5V Turbo
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Models in this comparison

Gemini 3.1 Pro vs GLM 5V Turbo on Vision Evals

Gemini 3.1 Pro scores higher on all five Vision Evals tasks.

The widest gap is Reasoning, where Gemini 3.1 Pro leads 72.2% to 31.8%.

Overall, Gemini 3.1 Pro averages 76.8% (#11 of 61) against 54.6% (#47 of 61) for GLM 5V Turbo.

GLM 5V Turbo is both cheaper ($0.0037 vs $0.010 per sample) and faster (5.9s vs 8.4s per sample).

Gemini 3.1 ProGLM 5V Turbo

Gemini 3.1 Pro vs GLM 5V Turbo Comparison Table

Evals updated October 8, 2026Pricing updated October 10, 2026

PropertyGemini 3.1 ProGLM 5V Turbo
OrganizationGoogleZ.ai
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateFeb 2026Apr 2026
Context Window1.0M200K
ParametersUnknownUnknown
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.00$1.20
Output $/1M$12.00$4.00
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
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
76.8%
54.6%
Avg cost / sample$0.010$0.0037
Avg speed / sample8.44s5.89s
By task
Object Detection
67.4%
$0.010
56.5%
$0.0052
Counting
71.6%
$0.0071
48.6%
$0.0017
Identification
100.0%
$0.0070
84.4%
$0.0015
OCR (low)
72.6%
$0.010
51.5%
$0.0039
by category
Single value
67.8%
Transcription
88.5%
Structured JSON
82.0%
Text localization
52.0%
Single value
43.0%
Transcription
78.0%
Structured JSON
67.5%
Text localization
17.7%
OCR (high)
73.1%
$0.026
56.0%
$0.0080
by category
Single value
68.7%
Transcription
90.8%
Structured JSON
83.9%
Text localization
47.1%
Single value
44.4%
Transcription
77.8%
Structured JSON
69.6%
Text localization
38.7%
Reasoning (low)
72.2%
$0.012
31.8%
$0.0017
Reasoning (high)
74.8%
$0.021
49.7%
$0.0069

Gemini 3.1 Pro vs GLM 5V Turbo: Overview

Gemini 3.1 Pro

Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.

The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.

GLM 5V Turbo

GLM-5V-Turbo is a native multimodal model from Z.ai that extends the GLM family with joint image, video, and text input aimed at vision-centered coding and agent workflows. The model reads screenshots, design drafts, document layouts, and interface captures and generates runnable code from them, covering tasks such as turning a visual design into a working front end, diagnosing rendering and layout defects from screen captures, and operating graphical user interfaces during long-horizon agent runs. It accepts roughly 200,000 input tokens and can emit up to 131,072 output tokens in a single response, which supports sessions that hold specifications, source files, logs, and visual references at the same time.

Training includes a joint reinforcement learning stage spanning more than 30 tasks simultaneously, an approach Z.ai describes as a way to counter the trade-off in which improving visual recognition degrades programming ability and the reverse. Reported evaluations cover pure-text coding on the backend, frontend, and repository exploration tracks of CC-Bench-V2, together with agent execution suites such as PinchBench, ClawEval, and ZClawBench, indicating that text coding behavior is retained after visual input is added.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 3.1 Pro performed better. It scores higher on all five vision tasks and averages 76.8% (#11 of 61) against 54.6% (#47 of 61) for GLM 5V Turbo. 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 3.1 Pro leads with 72.2% against 31.8%. This is the widest gap between the two models across the benchmark's tasks.

GLM 5V Turbo is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0037 per sample against $0.010. Gemini 3.1 Pro is priced at $2.00 per 1M input tokens and $12.00 per 1M output; GLM 5V Turbo is priced at $1.20 per 1M input tokens and $4.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

GLM 5V Turbo is faster. Across Roboflow's Vision Evals it averaged 5.9s per inference against 8.4s. 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 open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.