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GLM 5V Turbo vs GPT-5.4 Mini

Compare GLM 5V Turbo and GPT-5.4 Mini side-by-side. See how these vision models stack up in Image Captioning, OCR, Classification, Object Detection, and Open Prompt.

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Z.aiGLM 5V Turbo
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OpenAIGPT-5.4 Mini
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Models in this comparison

GLM 5V Turbo vs GPT-5.4 Mini on Vision Evals

GLM 5V Turbo scores higher on 3 of the six Vision Evals tasks.

The widest gap is Object Detection, where GLM 5V Turbo leads 56.5% to 16.1%.

Overall, GLM 5V Turbo averages 65.3% (#25 of 33) against 63.5% (#28 of 33) for GPT-5.4 Mini.

GPT-5.4 Mini is both cheaper ($0.0030 vs $0.0031 per sample) and faster (5.3s vs 6.3s per sample).

GLM 5V TurboGPT-5.4 Mini

GLM 5V Turbo vs GPT-5.4 Mini Comparison Table

Evals updated August 26, 2026Pricing updated August 26, 2026

PropertyGLM 5V TurboGPT-5.4 Mini
OrganizationZ.aiOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateApr 2026Mar 2026
Context Window200K400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.20$0.750
Output $/1M$4.00$4.50
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
65.3%
63.5%
Avg cost / sample$0.0031$0.0030
Avg speed / sample6.35s5.35s
By task
Object Detection
56.5%
$0.0052
16.1%
$0.0044
Counting
48.6%
$0.0017
60.8%
$0.0019
Identification
84.4%
$0.0015
78.1%
$0.0013
OCR
89.3%
$0.0030
88.1%
$0.0042
Data Extraction
81.4%
$0.0018
82.5%
$0.0014
Reasoning (low)
31.8%
$0.0017
55.6%
$0.0023
Reasoning (high)
49.7%
$0.0069
62.9%
$0.0081

GLM 5V Turbo vs GPT-5.4 Mini: Overview

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.

GPT-5.4 Mini

GPT-5.4 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.

Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.

Frequently Asked Questions

On Roboflow's Vision Evals, GLM 5V Turbo performed slightly better overall. The two split the six vision tasks 3 to 3, but GLM 5V Turbo averages 65.3% (#25 of 33) against 63.5% (#28 of 33) for GPT-5.4 Mini. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals Object Detection benchmark, GLM 5V Turbo leads with 56.5% against 16.1%. This is the widest gap between the two models across the benchmark's tasks.

GPT-5.4 Mini is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0030 per sample against $0.0031. GLM 5V Turbo is priced at $1.20 per 1M input tokens and $4.00 per 1M output; GPT-5.4 Mini is priced at $0.75 per 1M input tokens and $4.50 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

GPT-5.4 Mini is faster. Across Roboflow's Vision Evals it averaged 5.3s per inference against 6.3s. 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.