Roboflow

GPT-6.1 Terra vs Grok 4.7

GPT-6.1 Terra and Grok 4.7 score 85.8% vs 63.5% averaged across five Vision Evals tasks (#2 and #33 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 40.4%).
  • GPT-6.1 Terra is about 1/6 of the cost: $0.0033 vs $0.020 overall average cost per sample.
  • GPT-6.1 Terra is faster: 10.1s vs 33.3s per sample.

Compare GPT-6.1 Terra vs Grok 4.7 live

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

These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.

GPT-6.1 Terra vs Grok 4.7 on Vision Evals

Score by taskGPT-6.1 TerraGrok 4.7
Object Detection
84.7
40.4
Reasoning
85.4
64.2
Counting
80.2
61.7
OCR
81.9
63.6
Identification
96.9
87.5

Roboflow Vision Evals. Full breakdown below.

GPT-6.1 Terra vs Grok 4.7 Comparison Table

Evals updated October 26, 2026Pricing updated October 27, 2026

PropertyGPT-6.1 TerraGrok 4.7
OrganizationOpenAISpaceXAI
Categoryclosedclosed
Modalitymultimodal—
Release DateOct 2026Sep 2026
Context Window1.1M500K
ParametersundisclosedUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$1.00$2.00
Output $/1M$5.00$6.00
Vision Tasks
CaptioningSupportedDemo
ClassificationSupportedDemo
Object DetectionSupportedDemo
OCRSupportedDemo
Visual Question AnsweringSupportedDemo
Both support 5 more tasks
Model Features
Foundation VisionSupportedNot listed
LLMs with Vision CapabilitiesSupportedNot listed
Multimodal VisionSupportedNot listed
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
85.8%
63.5%
Avg cost / sample$0.0033$0.020
Avg speed / sample10.05s33.31s
By task
Object Detection (low)
84.7%
±0.1, Mean of 3 runs, range 84.7 to 84.8
$0.0051
40.4%
±0.6, Mean of 3 runs, range 39.8 to 41.0
$0.021
Object Detection (high)
84.7%
±0.4, Mean of 3 runs, range 84.2 to 84.9
$0.0081
41.2%
±1.6, Mean of 3 runs, range 39.6 to 42.8
$0.028
Counting (low)
80.2%
±2.0, Mean of 3 runs, range 78.4 to 82.4
$0.0018
61.7%
±1.3, Mean of 3 runs, range 60.8 to 63.5
$0.011
Counting (high)
84.2%
±3.4, Mean of 3 runs, range 81.1 to 87.8
$0.0029
60.8%
±1.3, Mean of 3 runs, range 59.5 to 62.2
$0.017
Identification (low)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0013
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0063
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0014
80.2%
±1.6, Mean of 3 runs, range 78.1 to 81.3
$0.0093
OCR (low)
81.9%
$0.0033
63.6%
$0.023
by category
Single value
70.9%
Transcription
96.7%
Structured JSON
91.5%
Text localization
77.6%
Single value
54.4%
Transcription
89.5%
Structured JSON
86.4%
Text localization
14.8%
OCR (high)
84.4%
$0.0066
–
by category
Single value
74.8%
Transcription
97.0%
Structured JSON
93.4%
Text localization
79.2%
Reasoning (low)
85.4%
±1.7, Mean of 3 runs, range 83.4 to 86.8
$0.0016
64.2%
±2.3, Mean of 3 runs, range 62.3 to 66.9
$0.015
Reasoning (high)
87.0%
±1.3, Mean of 3 runs, range 85.4 to 88.1
$0.0023
66.9%
±1.3, Mean of 3 runs, range 65.6 to 68.2
$0.024

GPT-6.1 Terra vs Grok 4.7: Overview

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.

Grok 4.7

Grok 4.7 is a proprietary multimodal reasoning model from SpaceXAI that accepts images alongside text and returns text-only output. On visual inputs it supports image captioning, visual question answering, OCR, document and chart question answering, and image classification and tagging, with all results expressed as generated text rather than bounding boxes or masks. Its 500,000 token context window leaves room for several images, long documents, or extended conversations about visual content in a single request.

The model exposes a configurable reasoning effort setting with low, medium, high, and xhigh levels (high by default), letting callers trade latency for the amount of deliberation spent on a prompt, including multi-step questions about an image. Built on a larger base model than Grok 4.6 with extended reinforcement learning on harder tasks, it works longer on difficult problems and checks its own work more carefully at the same serving speed. SpaceXAI's launch materials focus on coding and agentic knowledge work and report no image benchmark results.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-6.1 Terra performed better. It scores higher on all five vision tasks and averages 85.8% (#2 of 65) against 63.5% (#33 of 65) for Grok 4.7. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals Object Detection benchmark at low effort, GPT-6.1 Terra leads with 84.7% against 40.4%. This is the widest gap between the two models across the benchmark's tasks.

GPT-6.1 Terra is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0033 per sample against $0.020. GPT-6.1 Terra is priced at $1.00 per 1M input tokens and $5.00 per 1M output; Grok 4.7 is priced at $2.00 per 1M input tokens and $6.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

GPT-6.1 Terra is faster. Across Roboflow's Vision Evals it averaged 10.1s per inference against 33.3s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.