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GLM 5.3 Flash vs GPT-6 Sol

GPT-6 Sol and GLM 5.3 Flash score 76.9% vs 55.8% averaged across five Vision Evals tasks (#15 and #49 of 68 models), so GPT-6 Sol leads overall.

  • GPT-6 Sol wins all 5 tasks, with its widest lead on object detection (74.7% vs 33.1%).
  • GLM 5.3 Flash is under a tenth of the cost: $0.0001 vs $0.0074 overall average cost per sample.
  • GLM 5.3 Flash is faster: 9.3s vs 10.2s per sample.
OpenAI

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Z.aiGLM 5.3 Flash
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GLM 5.3 Flash vs GPT-6 Sol on Vision Evals

Score by taskGLM 5.3 FlashGPT-6 Sol
Object Detection
33.1
74.7
Reasoning
51.0
72.6
Counting
55.4
76.6
OCR
55.4
66.7
Identification
84.4
93.8

Roboflow Vision Evals. Full breakdown below.

GLM 5.3 Flash vs GPT-6 Sol Comparison Table

Evals updated November 10, 2026Pricing updated November 11, 2026

PropertyGLM 5.3 FlashGPT-6 Sol
OrganizationZ.aiOpenAI
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateAug 2026Sep 2026
Context Window1.0M1.1M
Parameters320B total, 18B activeundisclosed
LicenseMITProprietary
Commercial usefree for commercial usecommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.040$2.00
Output $/1M$0.078$10.00
Vision Tasks
Promptable Concept SegmentationNot listedDemo
Both support 10 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
55.8%
76.9%
Avg cost / sample$0.0001$0.0074
Avg speed / sample9.29s10.20s
By task
Object Detection (low)
33.1%
$0.0002
74.7%
±0.7, Mean of 3 runs, range 74.1 to 75.5
$0.011
Object Detection (high)–
76.4%
±1.2, Mean of 3 runs, range 75.0 to 77.4
$0.022
Counting (low)
55.4%
$0.0001
76.6%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.0039
Counting (high)–
77.5%
±1.3, Mean of 3 runs, range 75.7 to 78.4
$0.0070
Identification (low)
84.4%
$0.0001
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
$0.0027
Identification (high)–
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0036
OCR (low)
55.4%
$0.0002
66.7%
$0.0077
by category
Single value
47.4%
Transcription
81.5%
Structured JSON
72.0%
Text localization
18.6%
Single value
45.6%
Transcription
88.3%
Structured JSON
83.4%
Text localization
65.7%
OCR (high)
55.3%
$0.0002
68.8%
$0.018
by category
Single value
50.9%
Transcription
72.2%
Structured JSON
72.0%
Text localization
14.5%
Single value
47.4%
Transcription
91.0%
Structured JSON
85.3%
Text localization
68.7%
Reasoning (low)
51.0%
$0.0001
72.6%
±3.6, Mean of 3 runs, range 69.5 to 76.8
$0.0037
Reasoning (high)
59.6%
$0.0001
77.7%
±1.0, Mean of 3 runs, range 76.8 to 78.8
$0.0061

GLM 5.3 Flash vs GPT-6 Sol: 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.

GPT-6 Sol

GPT-6 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.

The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-6 Sol performed better. It scores higher on all five vision tasks and averages 76.9% (#15 of 68) against 55.8% (#49 of 68) 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-6 Sol leads with 74.7% 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 $0.0074. GLM 5.3 Flash is priced at $0.04 per 1M input tokens and $0.08 per 1M output; GPT-6 Sol is priced at $2.00 per 1M input tokens and $10.00 per 1M output. 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 10.2s. 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.