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GLM 5V Turbo vs GPT-5.6 Sol

Compare GLM 5V Turbo 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 5V Turbo
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OpenAIGPT-5.6 Sol
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Models in this comparison

GLM 5V Turbo vs GPT-5.6 Sol on Vision Evals

GPT-5.6 Sol scores higher on all five Vision Evals tasks.

The widest gap is Reasoning, where GPT-5.6 Sol leads 66.0% to 31.8%.

Overall, GLM 5V Turbo averages 54.6% (#47 of 61) against 72.4% (#16 of 61) for GPT-5.6 Sol.

GLM 5V Turbo is both cheaper ($0.0037 vs $0.0096 per sample) and faster (5.9s vs 12.2s per sample).

GLM 5V TurboGPT-5.6 Sol

GLM 5V Turbo vs GPT-5.6 Sol Comparison Table

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

PropertyGLM 5V TurboGPT-5.6 Sol
OrganizationZ.aiOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateApr 2026Jul 2026
Context Window200K1.5M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.20$2.00
Output $/1M$4.00$10.00
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
54.6%
72.4%
Avg cost / sample$0.0037$0.0096
Avg speed / sample5.89s12.16s
By task
Object Detection (low)
56.5%
$0.0052
68.4%
±0.7, Mean of 3 runs, range 67.9 to 69.3
$0.015
Object Detection (high)–
68.4%
±0.8, Mean of 3 runs, range 67.7 to 69.3
$0.035
Counting (low)
48.6%
$0.0017
74.3%
±1.4, Mean of 3 runs, range 73.0 to 75.7
$0.0049
Counting (high)–
76.1%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.0078
Identification (low)
84.4%
$0.0015
89.6%
±4.7, Mean of 3 runs, range 84.4 to 93.8
$0.0028
Identification (high)–
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0030
OCR (low)
51.5%
$0.0039
63.6%
$0.0095
by category
Single value
43.0%
Transcription
78.0%
Structured JSON
67.5%
Text localization
17.7%
Single value
43.5%
Transcription
91.7%
Structured JSON
82.0%
Text localization
52.2%
OCR (high)
56.0%
$0.0080
65.3%
$0.024
by category
Single value
44.4%
Transcription
77.8%
Structured JSON
69.6%
Text localization
38.7%
Single value
46.1%
Transcription
92.3%
Structured JSON
83.9%
Text localization
51.6%
Reasoning (low)
31.8%
$0.0017
66.0%
±2.6, Mean of 3 runs, range 63.6 to 68.9
$0.0043
Reasoning (high)
49.7%
$0.0069
71.7%
±1.3, Mean of 3 runs, range 70.2 to 72.8
$0.0061

GLM 5V Turbo vs GPT-5.6 Sol: Overview

GLM 5V Turbo

GLM-5V-Turbo is a native multimodal model from Z.ai that extends the GLM family with joint image, video, and text input aimed at vision-centered coding and agent workflows. The model reads screenshots, design drafts, document layouts, and interface captures and generates runnable code from them, covering tasks such as turning a visual design into a working front end, diagnosing rendering and layout defects from screen captures, and operating graphical user interfaces during long-horizon agent runs. It accepts roughly 200,000 input tokens and can emit up to 131,072 output tokens in a single response, which supports sessions that hold specifications, source files, logs, and visual references at the same time.

Training includes a joint reinforcement learning stage spanning more than 30 tasks simultaneously, an approach Z.ai describes as a way to counter the trade-off in which improving visual recognition degrades programming ability and the reverse. Reported evaluations cover pure-text coding on the backend, frontend, and repository exploration tracks of CC-Bench-V2, together with agent execution suites such as PinchBench, ClawEval, and ZClawBench, indicating that text coding behavior is retained after visual input is added.

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 all five vision tasks and averages 72.4% (#16 of 61) against 54.6% (#47 of 61) for GLM 5V Turbo. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Reasoning benchmark at low effort, GPT-5.6 Sol leads with 66.0% against 31.8%. This is the widest gap between the two models across the benchmark's tasks.

GLM 5V Turbo is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0037 per sample against $0.0096. GLM 5V Turbo is priced at $1.20 per 1M input tokens and $4.00 per 1M output; GPT-5.6 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 5V Turbo is faster. Across Roboflow's Vision Evals it averaged 5.9s per inference against 12.2s. 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.