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GLM 5.3 Flash vs GPT-6.1 Terra

GPT-6.1 Terra and GLM 5.3 Flash score 85.8% vs 55.8% averaged across five Vision Evals tasks (#2 and #46 of 65 models), so GPT-6.1 Terra leads overall.

  • GPT-6.1 Terra wins all 5 tasks, with its widest lead on object detection (84.7% vs 33.1%).
  • GLM 5.3 Flash is about a fifth of the cost: $0.0006 vs $0.0033 overall average cost per sample.
  • GLM 5.3 Flash is faster: 9.3s vs 10.1s per sample.

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Z.aiGLM 5.3 Flash
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GLM 5.3 Flash vs GPT-6.1 Terra on Vision Evals

Score by taskGLM 5.3 FlashGPT-6.1 Terra
Object Detection
33.1
84.7
Reasoning
51.0
85.4
OCR
55.4
81.9
Counting
55.4
80.2
Identification
84.4
96.9

Roboflow Vision Evals. Full breakdown below.

GLM 5.3 Flash vs GPT-6.1 Terra Comparison Table

Evals updated October 26, 2026Pricing updated November 7, 2026

PropertyGLM 5.3 FlashGPT-6.1 Terra
OrganizationZ.aiOpenAI
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateAug 2026Oct 2026
Context Window1.0M1.1M
Parameters320B total, 18B activeundisclosed
LicenseMITProprietary
Commercial usefree for commercial usecommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.150$1.00
Output $/1M$0.500$5.00
Vision Tasks
Promptable Concept SegmentationNot listedDemo
Both support 10 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
55.8%
85.8%
Avg cost / sample$0.0006$0.0033
Avg speed / sample9.29s10.05s
By task
Object Detection (low)
33.1%
$0.0008
84.7%
±0.1, Mean of 3 runs, range 84.7 to 84.8
$0.0051
Object Detection (high)–
84.7%
±0.4, Mean of 3 runs, range 84.2 to 84.9
$0.0081
Counting (low)
55.4%
$0.0002
80.2%
±2.0, Mean of 3 runs, range 78.4 to 82.4
$0.0018
Counting (high)–
84.2%
±3.4, Mean of 3 runs, range 81.1 to 87.8
$0.0029
Identification (low)
84.4%
$0.0002
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0013
Identification (high)–
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0014
OCR (low)
55.4%
$0.0007
81.9%
$0.0033
by category
Single value
47.4%
Transcription
81.5%
Structured JSON
72.0%
Text localization
18.6%
Single value
70.9%
Transcription
96.7%
Structured JSON
91.5%
Text localization
77.6%
OCR (high)
55.3%
$0.0011
84.4%
$0.0066
by category
Single value
50.9%
Transcription
72.2%
Structured JSON
72.0%
Text localization
14.5%
Single value
74.8%
Transcription
97.0%
Structured JSON
93.4%
Text localization
79.2%
Reasoning (low)
51.0%
$0.0002
85.4%
±1.7, Mean of 3 runs, range 83.4 to 86.8
$0.0016
Reasoning (high)
59.6%
$0.0003
87.0%
±1.3, Mean of 3 runs, range 85.4 to 88.1
$0.0023

GLM 5.3 Flash vs GPT-6.1 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.

GPT-6.1 Terra

GPT-6.1 Terra is an OpenAI model released on October 26, 2026 for visual reasoning, computer use and writing. It accepts text and image inputs and produces text, with a 1,050,000-token context window and up to 128,000 output tokens. OpenAI lists applications including matching product photographs to catalog entries, interpreting technical drawings and operating application screens.

The API exposes six reasoning effort settings: none, low, medium, high, xhigh and max. Medium is the default. The model supports structured outputs and tool calling through the Responses API. Vision Evals reports its Low and High results for text understanding, counting, identification, reasoning and object detection, using native OpenAI API inference. Terra is a proprietary model; its weights are not released.