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GLM 5.3 Flash vs GPT-5.6 Luna

Compare GLM 5.3 Flash and GPT-5.6 Luna 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-5.6 Luna
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Models in this comparison

GLM 5.3 Flash vs GPT-5.6 Luna on Vision Evals

GPT-5.6 Luna scores higher on 3 of the five Vision Evals tasks.

The widest gap is Object Detection, where GPT-5.6 Luna leads 61.0% to 33.1%.

Overall, GLM 5.3 Flash averages 55.8% (#42 of 61) against 64.7% (#27 of 61) for GPT-5.6 Luna.

GLM 5.3 Flash is cheaper ($0.0006 vs $0.0011 per sample), while GPT-5.6 Luna is faster (8.6s vs 9.3s per sample).

GLM 5.3 FlashGPT-5.6 Luna

GLM 5.3 Flash vs GPT-5.6 Luna Comparison Table

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

PropertyGLM 5.3 FlashGPT-5.6 Luna
OrganizationZ.aiOpenAI
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateAug 2026Jul 2026
Context Window1.0M1.5M
Parameters320B total, 18B activeUnknown
LicenseMITProprietary
Pricing per 1M tokens
Input $/1M$0.150$0.200
Output $/1M$0.500$1.20
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%
64.7%
Avg cost / sample$0.0006$0.0011
Avg speed / sample9.29s8.60s
By task
Object Detection (low)
33.1%
$0.0008
61.0%
±1.2, Mean of 3 runs, range 59.9 to 62.2
$0.0015
Object Detection (high)–
62.3%
±1.2, Mean of 3 runs, range 61.4 to 63.8
$0.0050
Counting (low)
55.4%
$0.0002
67.1%
±1.4, Mean of 3 runs, range 66.2 to 68.9
$0.0006
Counting (high)–
70.7%
±3.4, Mean of 3 runs, range 66.2 to 73.0
$0.0015
Identification (low)
84.4%
$0.0002
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0004
Identification (high)–
84.4%
±6.3, Mean of 3 runs, range 78.1 to 90.6
$0.0007
OCR (low)
55.4%
$0.0007
51.8%
$0.0011
by category
Single value
47.4%
Transcription
81.5%
Structured JSON
72.0%
Text localization
18.6%
Single value
29.6%
Transcription
75.5%
Structured JSON
73.6%
Text localization
39.7%
OCR (high)
55.3%
$0.0011
55.7%
$0.0041
by category
Single value
50.9%
Transcription
72.2%
Structured JSON
72.0%
Text localization
14.5%
Single value
33.0%
Transcription
83.5%
Structured JSON
77.0%
Text localization
43.8%
Reasoning (low)
51.0%
$0.0002
60.5%
±5.0, Mean of 3 runs, range 55.0 to 64.9
$0.0006
Reasoning (high)
59.6%
$0.0003
65.6%
±3.6, Mean of 3 runs, range 60.9 to 68.2
$0.0015

GLM 5.3 Flash vs GPT-5.6 Luna: 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-5.6 Luna

GPT-5.6 Luna is the fastest and most cost-efficient model in OpenAI's GPT-5.6 family, which also includes Sol (the flagship tier) and Terra (the balanced mid-tier). Introduced under a new naming convention where the generation number (5.6) and a durable capability tier name (Luna, Terra, Sol) together define each model, Luna occupies the lightweight end of the family and is designed for high-volume, latency-sensitive workloads such as summarization, drafting, autocomplete, classification, and routine automation. The GPT-5.6 family as a whole advances capabilities in software engineering, computer use, professional knowledge work, scientific research, and cybersecurity, with all three tiers rated at the "High" capability level under OpenAI's Preparedness Framework for both cybersecurity and biological/chemical risk domains.

GPT-5.6 Luna supports multimodal input and function calling, and shares the family's 1.5 million token context window. On Terminal-Bench 2.1, Luna scores 82.5%, and on the Artificial Analysis Coding Agent Index it outperforms comparable models at roughly one-quarter the estimated cost of higher-tier alternatives. Luna supports the GPT-5.6 prompt caching scheme, which introduces explicit cache breakpoints and a 30-minute minimum cache life. The model was previewed on June 26, 2026 to a limited group of trusted partners via the OpenAI API and Codex, with general availability rolling out on July 9, 2026 across ChatGPT, Codex, and the API.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-5.6 Luna performed better. It scores higher on 3 of the five vision tasks and averages 64.7% (#27 of 61) against 55.8% (#42 of 61) for GLM 5.3 Flash. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Object Detection benchmark at low effort, GPT-5.6 Luna leads with 61.0% against 33.1%. 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 $0.0006 per sample against $0.0011. GLM 5.3 Flash is priced at $0.15 per 1M input tokens and $0.50 per 1M output; GPT-5.6 Luna is priced at $0.20 per 1M input tokens and $1.20 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

GPT-5.6 Luna is faster. Across Roboflow's Vision Evals it averaged 8.6s 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.