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Gemini 4 Carbon vs GPT-6 Luna

Gemini 4 Carbon and GPT-6 Luna score 89.4% vs 69.7% averaged across five Vision Evals tasks (#1 and #25 of 68 models), so Gemini 4 Carbon leads overall.

  • Gemini 4 Carbon wins all 5 tasks, with its widest lead on OCR (87.2% vs 59.8%).
  • GPT-6 Luna is under a tenth of the cost: $0.0004 vs $0.0061 overall average cost per sample.
  • Gemini 4 Carbon is faster: 8.7s vs 10.2s per sample.

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GoogleGemini 4 Carbon
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OpenAIGPT-6 Luna
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Gemini 4 Carbon vs GPT-6 Luna on Vision Evals

Score by taskGemini 4 CarbonGPT-6 Luna
OCR
87.2
59.8
Reasoning
91.4
64.2
Object Detection
84.5
65.5
Counting
90.1
71.6
Identification
93.8
87.5

Roboflow Vision Evals. Full breakdown below.

Gemini 4 Carbon vs GPT-6 Luna Comparison Table

Evals updated November 10, 2026Pricing updated November 10, 2026

PropertyGemini 4 CarbonGPT-6 Luna
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateNov 2026Sep 2026
Context Window1.0M1.1M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1MNo published price$0.100
Output $/1MNo published price$0.500
Vision Tasks
Both support 11 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
89.4%
69.7%
Avg cost / sample$0.0061$0.0004
Avg speed / sample8.71s10.21s
By task
Object Detection (low)
84.5%
±0.3, Mean of 3 runs, range 84.2 to 84.8
$0.011
65.5%
±0.4, Mean of 3 runs, range 65.2 to 66.0
$0.0006
Object Detection (high)
85.9%
±0.3, Mean of 3 runs, range 85.6 to 86.2
$0.102
68.0%
±0.7, Mean of 3 runs, range 67.3 to 68.6
$0.0014
Counting (low)
90.1%
±1.4, Mean of 3 runs, range 89.2 to 91.9
$0.0024
71.6%
±0.0, Mean of 3 runs, range 71.6 to 71.6
$0.0002
Counting (high)
86.9%
±2.7, Mean of 3 runs, range 83.8 to 89.2
$0.075
72.1%
±0.7, Mean of 3 runs, range 71.6 to 73.0
$0.0004
Identification (low)
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
$0.0024
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0002
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.019
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0002
OCR (low)
87.2%
$0.0053
59.8%
$0.0004
by category
Single value
80.4%
Transcription
96.6%
Structured JSON
93.0%
Text localization
84.2%
Single value
41.7%
Transcription
80.0%
Structured JSON
78.2%
Text localization
47.4%
OCR (high)
89.7%
$0.102
62.5%
$0.0013
by category
Single value
85.2%
Transcription
99.1%
Structured JSON
93.9%
Text localization
85.2%
Single value
44.4%
Transcription
84.0%
Structured JSON
81.0%
Text localization
50.0%
Reasoning (low)
91.4%
±1.7, Mean of 3 runs, range 90.1 to 93.4
$0.0025
64.2%
±3.0, Mean of 3 runs, range 60.3 to 66.2
$0.0003
Reasoning (high)
92.3%
±0.3, Mean of 3 runs, range 92.0 to 92.7
$0.057
71.1%
±2.3, Mean of 3 runs, range 68.2 to 72.8
$0.0004

Gemini 4 Carbon vs GPT-6 Luna: Overview

Gemini 4 Carbon

Gemini 4 Carbon is a multimodal model from Google. It builds on Gemini 4 Argon and supports reasoning over text and images. Google describes improvements in coding and long-running tasks. Model weights are not publicly released.

GPT-6 Luna

GPT-6 Luna is the fast, cost-efficient tier of OpenAI's GPT-6 model family, sitting below GPT-6 Sol and the larger GPT-6 Astra model that opened the generation. It is a proprietary multimodal transformer that accepts text and image input and returns text, and it exposes an adjustable reasoning effort setting so the same model can run in a low-latency mode or spend additional inference compute on harder problems. OpenAI positions it for high-volume and latency-sensitive workloads such as conversational assistants, classification, and lightweight agentic pipelines, while noting that at higher reasoning effort it handles software engineering and computer-use tasks that previously required a Sol-tier model.

The model supports a context window of roughly 1,050,000 input tokens with a maximum output of 128,000 tokens, which allows long documents, extended agent traces, and large code repositories to be processed in a single request. OpenAI describes the GPT-6 generation as improving factual reliability and adopting a more concise communication style relative to the GPT-5.6 series, and attributes the efficiency of the Sol and Luna tiers to gains in caching and inference rather than to reduced capability. Architecture details, parameter counts, and training data are not published.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 4 Carbon performed better. It scores higher on all five vision tasks and averages 89.4% (#1 of 68) against 69.7% (#25 of 68) for GPT-6 Luna. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals OCR benchmark at low effort, Gemini 4 Carbon leads with 87.2% against 59.8%. This is the widest gap between the two models across the benchmark's tasks.

GPT-6 Luna is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0004 per sample against $0.0061. Actual costs depend on your image sizes, prompts, and output length.

Gemini 4 Carbon is faster. Across Roboflow's Vision Evals it averaged 8.7s per inference against 10.2s. 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 segmentation in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.