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Gemini 4 Hydrogen vs GLM 5.3 Flash

Gemini 4 Hydrogen and GLM 5.3 Flash score 87.3% vs 55.8% averaged across five Vision Evals tasks (#3 and #50 of 69 models), so Gemini 4 Hydrogen leads overall.

  • Gemini 4 Hydrogen wins all 5 tasks, with its widest lead on object detection (81.0% vs 33.1%).
  • GLM 5.3 Flash is under a tenth of the cost: $0.0001 vs $0.0094 overall average cost per sample.
  • GLM 5.3 Flash is faster: 9.3s vs 13.2s per sample.

Compare Gemini 4 Hydrogen vs GLM 5.3 Flash live

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GoogleGemini 4 Hydrogen
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Z.aiGLM 5.3 Flash
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Gemini 4 Hydrogen vs GLM 5.3 Flash on Vision Evals

Score by taskGemini 4 HydrogenGLM 5.3 Flash
Object Detection
81.0
33.1
Reasoning
88.1
51.0
OCR
86.7
55.4
Counting
83.8
55.4
Identification
96.9
84.4

Roboflow Vision Evals. Full breakdown below.

Gemini 4 Hydrogen vs GLM 5.3 Flash Comparison Table

Evals updated November 12, 2026Pricing updated November 13, 2026

PropertyGemini 4 HydrogenGLM 5.3 Flash
OrganizationGoogleZ.ai
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateNov 2026Aug 2026
Context Window1.0M1.0M
ParametersUnknown320B total, 18B active
LicenseProprietaryMIT
Commercial usecommercial use per vendor termsfree for commercial use
Pricing per 1M tokens
Input $/1MNo published price$0.040
Output $/1MNo published price$0.078
Vision Tasks
Chart Question AnsweringNot listedSupported
Document Question AnsweringNot listedSupported
Image TaggingNot listedSupported
Multi-Label ClassificationNot listedSupported
Promptable Concept SegmentationDemoNot listed
Both support 6 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
87.3%
55.8%
Avg cost / sample$0.0094$0.0001
Avg speed / sample13.23s9.29s
By task
Object Detection (low)
81.0%
±0.8, Mean of 3 runs, range 80.3 to 81.9
$0.016
33.1%
$0.0002
Object Detection (high)
83.3%
±0.7, Mean of 3 runs, range 82.4 to 83.8
$0.022
–
Counting (low)
83.8%
±2.0, Mean of 3 runs, range 82.4 to 86.5
$0.0041
55.4%
$0.0001
Counting (high)
84.2%
±2.7, Mean of 3 runs, range 82.4 to 87.8
$0.012
–
Identification (low)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0030
84.4%
$0.0001
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0039
–
OCR (low)
86.7%
$0.0089
55.4%
$0.0002
by category
Single value
78.7%
Transcription
96.1%
Structured JSON
93.6%
Text localization
83.9%
Single value
47.4%
Transcription
81.5%
Structured JSON
72.0%
Text localization
18.6%
OCR (high)
87.2%
$0.026
55.3%
$0.0002
by category
Single value
80.9%
Transcription
95.3%
Structured JSON
93.1%
Text localization
83.7%
Single value
50.9%
Transcription
72.2%
Structured JSON
72.0%
Text localization
14.5%
Reasoning (low)
88.1%
±0.7, Mean of 3 runs, range 87.4 to 88.7
$0.0046
51.0%
$0.0001
Reasoning (high)
91.8%
±0.3, Mean of 3 runs, range 91.4 to 92.0
$0.013
59.6%
$0.0001

Gemini 4 Hydrogen vs GLM 5.3 Flash: Overview

Gemini 4 Hydrogen

Gemini 4 Hydrogen is a multimodal model from Google, positioned as a successor to Gemini 3.8 Flash with stronger agent and coding capabilities at Flash-class speed.

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.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 4 Hydrogen performed better. It scores higher on all five vision tasks and averages 87.3% (#3 of 69) against 55.8% (#50 of 69) for GLM 5.3 Flash. 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, Gemini 4 Hydrogen leads with 81.0% 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 $0.0001 per sample against $0.0094. Actual costs depend on your image sizes, prompts, and output length.

GLM 5.3 Flash is faster. Across Roboflow's Vision Evals it averaged 9.3s per inference against 13.2s. 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.