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GLM 5V Turbo vs GPT-5.6 Terra

Compare GLM 5V Turbo and GPT-5.6 Terra 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 Terra
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Models in this comparison

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

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

The widest gap is Reasoning, where GPT-5.6 Terra leads 60.9% to 31.8%.

Overall, GLM 5V Turbo averages 54.6% (#47 of 61) against 66.0% (#24 of 61) for GPT-5.6 Terra.

GLM 5V Turbo is both cheaper ($0.0037 vs $0.010 per sample) and faster (5.9s vs 9.5s per sample).

GLM 5V TurboGPT-5.6 Terra

GLM 5V Turbo vs GPT-5.6 Terra Comparison Table

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

PropertyGLM 5V TurboGPT-5.6 Terra
OrganizationZ.aiOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateApr 2026Jul 2026
Context Window200K1.1M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.20$2.00
Output $/1M$4.00$12.00
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%
66.0%
Avg cost / sample$0.0037$0.010
Avg speed / sample5.89s9.52s
By task
Object Detection (low)
56.5%
$0.0052
60.6%
±0.2, Mean of 3 runs, range 60.3 to 60.7
$0.014
Object Detection (high)–
61.3%
±0.4, Mean of 3 runs, range 60.8 to 61.6
$0.026
Counting (low)
48.6%
$0.0017
65.8%
±2.0, Mean of 3 runs, range 63.5 to 67.6
$0.0056
Counting (high)–
62.6%
±2.7, Mean of 3 runs, range 59.5 to 64.9
$0.0076
Identification (low)
84.4%
$0.0015
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0034
Identification (high)–
86.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0038
OCR (low)
51.5%
$0.0039
56.3%
$0.011
by category
Single value
43.0%
Transcription
78.0%
Structured JSON
67.5%
Text localization
17.7%
Single value
36.1%
Transcription
79.9%
Structured JSON
76.8%
Text localization
42.3%
OCR (high)
56.0%
$0.0080
56.9%
$0.021
by category
Single value
44.4%
Transcription
77.8%
Structured JSON
69.6%
Text localization
38.7%
Single value
37.4%
Transcription
80.2%
Structured JSON
77.3%
Text localization
41.9%
Reasoning (low)
31.8%
$0.0017
60.9%
±2.0, Mean of 3 runs, range 59.6 to 63.6
$0.0051
Reasoning (high)
49.7%
$0.0069
65.3%
±1.0, Mean of 3 runs, range 64.2 to 66.2
$0.0067

GLM 5V Turbo vs GPT-5.6 Terra: 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 Terra

GPT-5.6 Terra is the mid-tier reasoning model in OpenAI's GPT-5.6 family, which also includes the flagship Sol and the lightweight Luna. Introduced in a limited preview on June 26, 2026, and made broadly available on July 9, 2026, Terra accepts text and image input and produces text output, supporting vision, function calling, tool use, and agentic workflows. It is designed as a balanced option for everyday professional and production workloads — including coding assistance, document analysis, customer support, and multi-step agent tasks — where both output quality and cost efficiency matter. OpenAI positions Terra as delivering performance competitive with GPT-5.5 at approximately half the price, with a context window of around 1,050,000 tokens. On Terminal-Bench 2.1, Terra scores 84.3%, matching Claude Fable 5 on that benchmark. Under OpenAI's Preparedness Framework, Terra is rated High for cybersecurity and biological capabilities, meaning it demonstrates meaningful capability in those domains without reaching the Critical threshold.

GPT-5.6 introduces a new naming convention in which the generation number (5.6) is paired with a durable capability tier name (Sol, Terra, or Luna), allowing each tier to advance on its own schedule. Terra carries the API identifier gpt-5.6-terra and supports the same reasoning effort controls available across the family, including adjustable reasoning depth. The model includes prompt caching with explicit cache breakpoints and a 30-minute minimum cache life, with cache writes billed at 1.25x the uncached input rate and cache reads receiving a 90% discount. GPT-5.6 Terra is a proprietary, closed-weights model served through the OpenAI API, Codex, and ChatGPT.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-5.6 Terra performed better. It scores higher on all five vision tasks and averages 66.0% (#24 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 Terra leads with 60.9% 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 $0.010. GLM 5V Turbo is priced at $1.20 per 1M input tokens and $4.00 per 1M output; GPT-5.6 Terra is priced at $2.00 per 1M input tokens and $12.00 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 9.5s. 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.