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GLM 5.3 Flash vs GPT-5.4 Mini

Compare GLM 5.3 Flash 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 5.3 Flash
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OpenAIGPT-5.4 Mini
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Models in this comparison

GLM 5.3 Flash vs GPT-5.4 Mini on Vision Evals

GLM 5.3 Flash scores higher on 4 of the six Vision Evals tasks.

The widest gap is Object Detection, where GLM 5.3 Flash leads 33.1% to 16.1%.

Overall, GLM 5.3 Flash averages 66.3% (#22 of 33) against 63.5% (#28 of 33) for GPT-5.4 Mini.

GLM 5.3 Flash is cheaper ($0.0002 vs $0.0030 per sample), while GPT-5.4 Mini is faster (5.3s vs 6.8s per sample).

GLM 5.3 FlashGPT-5.4 Mini

GLM 5.3 Flash vs GPT-5.4 Mini Comparison Table

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

PropertyGLM 5.3 FlashGPT-5.4 Mini
OrganizationZ.aiOpenAI
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateAug 2026Mar 2026
Context Window1.0M400K
Parameters320B total, 18B active
LicenseMITProprietary
Pricing per 1M tokens
Input $/1M$0.750
Output $/1M$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
66.3%
63.5%
Avg cost / sample$0.0002$0.0030
Avg speed / sample6.78s5.35s
By task
Object Detection
33.1%
$0.0004
16.1%
$0.0044
Counting
55.4%
$0.0001
60.8%
$0.0019
Identification
84.4%
$0.0001
78.1%
$0.0013
OCR
90.6%
$0.0002
88.1%
$0.0042
Data Extraction
83.5%
$0.0001
82.5%
$0.0014
Reasoning (low)
51.0%
$0.0001
55.6%
$0.0023
Reasoning (high)
59.6%
$0.0001
62.9%
$0.0081

GLM 5.3 Flash vs GPT-5.4 Mini: Overview

GLM 5.3 Flash

GLM-5.3-Flash is the first natively multimodal model in Z.ai's GLM-5 series, a mixture-of-experts transformer with roughly 320 billion total parameters and 18 billion activated per token. It routes each token through 8 of 288 experts across 45 language layers that interleave KDA linear attention with sparse multi-head latent attention, and pairs them with a 24-layer vision encoder that handles image and video input. The checkpoint declares a maximum context length of 1,048,576 tokens, ships in native FP8, and includes a multi-token prediction draft layer for speculative decoding. Z.ai reports that the hybrid attention design reduces attention computation by 3.01x and KV cache size by 4.44x relative to GLM-5.3.

The model starts from a newly trained base built on a 30 trillion token multimodal pre-training corpus and adopts Manifold-Constrained Hyper-Connections to improve scaling efficiency. Vision is integrated into the coding and agent loop, so the model can inspect interfaces, rendered output, and images while operating across code, browsers, and graphical user interfaces. Z.ai reports scores of 84.3 on Terminal-Bench 2.1, 63.4 on DeepSWE 1.1, 55.3 on Humanity's Last Exam with tools, and 48.8 on AutomationBench, and the model exposes low, high, and max thinking modes.

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 5.3 Flash performed better. It scores higher on 4 of the six vision tasks and averages 66.3% (#22 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 5.3 Flash leads with 33.1% against 16.1%. This is the widest gap between the two models across the benchmark's tasks.

GLM 5.3 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0002 per sample against $0.0030. 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 6.8s. 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.