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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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Run the same image across every model that supports a task and compare their outputs side-by-side.

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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 3 of the five Vision Evals tasks.

The widest gap is Object Detection, where GLM 5.3 Flash leads 33.1% to 15.8%.

Overall, GLM 5.3 Flash averages 55.8% (#42 of 61) against 53.2% (#49 of 61) for GPT-5.4 Mini.

GLM 5.3 Flash is cheaper ($0.0006 vs $0.0034 per sample), while GPT-5.4 Mini is faster (5.5s vs 9.3s per sample).

GLM 5.3 FlashGPT-5.4 Mini

GLM 5.3 Flash vs GPT-5.4 Mini Comparison Table

Evals updated October 8, 2026Pricing updated October 9, 2026

PropertyGLM 5.3 FlashGPT-5.4 Mini
OrganizationZ.aiOpenAI
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateAug 2026Mar 2026
Context Window1.0M400K
Parameters320B total, 18B activeUnknown
LicenseMITProprietary
Pricing per 1M tokens
Input $/1M$0.150$0.750
Output $/1M$0.500$4.50
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
55.8%
53.2%
Avg cost / sample$0.0006$0.0034
Avg speed / sample9.29s5.51s
By task
Object Detection (low)
33.1%
$0.0008
15.8%
±0.4, Mean of 3 runs, range 15.3 to 16.1
$0.0044
Object Detection (high)–
16.6%
±0.8, Mean of 3 runs, range 15.8 to 17.4
$0.030
Counting (low)
55.4%
$0.0002
58.6%
±2.0, Mean of 3 runs, range 56.8 to 60.8
$0.0019
Counting (high)–
64.9%
±2.0, Mean of 3 runs, range 63.5 to 67.6
$0.0073
Identification (low)
84.4%
$0.0002
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0013
Identification (high)–
82.3%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0.0055
OCR (low)
55.4%
$0.0007
51.1%
$0.0035
by category
Single value
47.4%
Transcription
81.5%
Structured JSON
72.0%
Text localization
18.6%
Single value
41.3%
Transcription
77.3%
Structured JSON
73.6%
Text localization
4.1%
OCR (high)
55.3%
$0.0011
55.8%
$0.027
by category
Single value
50.9%
Transcription
72.2%
Structured JSON
72.0%
Text localization
14.5%
Single value
47.0%
Transcription
78.5%
Structured JSON
79.0%
Text localization
6.1%
Reasoning (low)
51.0%
$0.0002
57.0%
±3.3, Mean of 3 runs, range 54.3 to 60.9
$0.0022
Reasoning (high)
59.6%
$0.0003
64.0%
±1.3, Mean of 3 runs, range 62.9 to 65.6
$0.0096

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 3 of the five vision tasks and averages 55.8% (#42 of 61) against 53.2% (#49 of 61) 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 at low effort, GLM 5.3 Flash leads with 33.1% against 15.8%. 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 $0.0006 per sample against $0.0034. GLM 5.3 Flash is priced at $0.15 per 1M input tokens and $0.50 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 5.5s per inference against 9.3s. 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.