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GLM 5.3 Flash vs GPT-5.6 Sol

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

GLM 5.3 Flash vs GPT-5.6 Sol on Vision Evals

GPT-5.6 Sol scores higher on 4 of the six Vision Evals tasks.

The widest gap is Object Detection, where GPT-5.6 Sol leads 68.2% to 33.1%.

Overall, GLM 5.3 Flash averages 66.3% (#22 of 33) against 76.9% (#10 of 33) for GPT-5.6 Sol.

GLM 5.3 Flash is both cheaper ($0.0002 vs $0.0089 per sample) and faster (6.8s vs 11.7s per sample).

GLM 5.3 FlashGPT-5.6 Sol

GLM 5.3 Flash vs GPT-5.6 Sol Comparison Table

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

PropertyGLM 5.3 FlashGPT-5.6 Sol
OrganizationZ.aiOpenAI
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateAug 2026Jul 2026
Context Window1.0M1.5M
Parameters320B total, 18B active
LicenseMITProprietary
Pricing per 1M tokens
Input $/1M$2.00
Output $/1M$10.00
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%
76.9%
Avg cost / sample$0.0002$0.0089
Avg speed / sample6.78s11.72s
By task
Object Detection
33.1%
$0.0004
68.2%
$0.016
Counting
55.4%
$0.0001
73.0%
$0.0048
Identification
84.4%
$0.0001
81.3%
$0.0027
OCR
90.6%
$0.0002
90.7%
$0.011
Data Extraction
83.5%
$0.0001
82.5%
$0.0033
Reasoning (low)
51.0%
$0.0001
65.6%
$0.0042
Reasoning (high)
59.6%
$0.0001
72.2%
$0.0057

GLM 5.3 Flash vs GPT-5.6 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. 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 Sol

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.

GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-5.6 Sol performed better. It scores higher on 4 of the six vision tasks and averages 76.9% (#10 of 33) against 66.3% (#22 of 33) 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, GPT-5.6 Sol leads with 68.2% 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 $0.0002 per sample against $0.0089. Actual costs depend on your image sizes, prompts, and output length.

GLM 5.3 Flash is faster. Across Roboflow's Vision Evals it averaged 6.8s per inference against 11.7s. 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.