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GLM 5.3 Flash vs GPT-5.6 Terra

Compare GLM 5.3 Flash 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 5.3 Flash
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OpenAIGPT-5.6 Terra
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Models in this comparison

GLM 5.3 Flash vs GPT-5.6 Terra on Vision Evals

GLM 5.3 Flash scores higher on 3 of the six Vision Evals tasks.

The widest gap is Object Detection, where GPT-5.6 Terra leads 60.7% to 33.1%.

Overall, GLM 5.3 Flash averages 66.3% (#22 of 33) against 72.4% (#13 of 33) for GPT-5.6 Terra.

GLM 5.3 Flash is both cheaper ($0.0002 vs $0.0089 per sample) and faster (6.8s vs 7.2s per sample).

GLM 5.3 FlashGPT-5.6 Terra

GLM 5.3 Flash vs GPT-5.6 Terra Comparison Table

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

PropertyGLM 5.3 FlashGPT-5.6 Terra
OrganizationZ.aiOpenAI
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateAug 2026Jul 2026
Context Window1.0M1.1M
Parameters320B total, 18B active
LicenseMITProprietary
Pricing per 1M tokens
Input $/1M$2.00
Output $/1M$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
66.3%
72.4%
Avg cost / sample$0.0002$0.0089
Avg speed / sample6.78s7.15s
By task
Object Detection
33.1%
$0.0004
60.7%
$0.014
Counting
55.4%
$0.0001
67.6%
$0.0060
Identification
84.4%
$0.0001
78.1%
$0.0039
OCR
90.6%
$0.0002
88.8%
$0.013
Data Extraction
83.5%
$0.0001
79.4%
$0.0037
Reasoning (low)
51.0%
$0.0001
59.6%
$0.0050
Reasoning (high)
59.6%
$0.0001
64.2%
$0.0066

GLM 5.3 Flash vs GPT-5.6 Terra: 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 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 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 Terra leads with 60.7% 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 $0.0002 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 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.