Roboflow

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.

Compare GLM 5V Turbo vs GPT-5.6 Terra live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Detect and compare bounding boxes across models on the same image.

Open Object Detection in the full playground
Z.aiGLM 5V Turbo
Run to compare this model.
OpenAIGPT-5.6 Terra
Run to compare this model.

Models in this comparison

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

GLM 5V Turbo scores higher on 3 of the six Vision Evals tasks.

The widest gap is Reasoning, where GPT-5.6 Terra leads 59.6% to 31.8%.

Overall, GLM 5V Turbo averages 65.3% (#25 of 33) against 72.4% (#13 of 33) for GPT-5.6 Terra.

GLM 5V Turbo is both cheaper ($0.0031 vs $0.0089 per sample) and faster (6.3s vs 7.2s per sample).

GLM 5V TurboGPT-5.6 Terra

GLM 5V Turbo vs GPT-5.6 Terra Comparison Table

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

PropertyGLM 5V TurboGPT-5.6 Terra
OrganizationZ.aiOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateApr 2026Jul 2026
Context Window200K1.1M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.20$2.00
Output $/1M$4.00$12.00
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
65.3%
72.4%
Avg cost / sample$0.0031$0.0089
Avg speed / sample6.35s7.15s
By task
Object Detection
56.5%
$0.0052
60.7%
$0.014
Counting
48.6%
$0.0017
67.6%
$0.0060
Identification
84.4%
$0.0015
78.1%
$0.0039
OCR
89.3%
$0.0030
88.8%
$0.013
Data Extraction
81.4%
$0.0018
79.4%
$0.0037
Reasoning (low)
31.8%
$0.0017
59.6%
$0.0050
Reasoning (high)
49.7%
$0.0069
64.2%
$0.0066

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 slightly better overall. The two split the six vision tasks 3 to 3, but GPT-5.6 Terra averages 72.4% (#13 of 33) against 65.3% (#25 of 33) 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 59.6% 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 $0.0031 per sample against $0.0089. 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 6.3s per inference against 7.2s. 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.