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Gemini 3.7 Flash vs GPT-5.6 Luna

Compare Gemini 3.7 Flash and GPT-5.6 Luna side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.

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GoogleGemini 3.7 Flash
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OpenAIGPT-5.6 Luna
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Models in this comparison

Gemini 3.7 Flash vs GPT-5.6 Luna on Vision Evals

Gemini 3.7 Flash scores higher on all five Vision Evals tasks.

The widest gap is OCR, where Gemini 3.7 Flash leads 73.9% to 51.8%.

Overall, Gemini 3.7 Flash averages 80.1% (#6 of 63) against 64.7% (#29 of 63) for GPT-5.6 Luna.

GPT-5.6 Luna is both cheaper ($0.0011 vs $0.0033 per sample) and faster (8.6s vs 11.0s per sample).

Gemini 3.7 FlashGPT-5.6 Luna

Gemini 3.7 Flash vs GPT-5.6 Luna Comparison Table

Evals updated October 13, 2026Pricing updated October 16, 2026

PropertyGemini 3.7 FlashGPT-5.6 Luna
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateAug 2026Jul 2026
Context Window1.0M1.5M
ParametersUndisclosedUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.750$0.200
Output $/1M$3.75$1.20
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
80.1%
64.7%
Avg cost / sample$0.0033$0.0011
Avg speed / sample11.04s8.60s
By task
Object Detection (low)
70.5%
±1.1, Mean of 3 runs, range 69.4 to 71.5
$0.0047
61.0%
±1.2, Mean of 3 runs, range 59.9 to 62.2
$0.0015
Object Detection (high)
74.3%
±0.8, Mean of 3 runs, range 73.3 to 75.0
$0.0089
62.3%
±1.2, Mean of 3 runs, range 61.4 to 63.8
$0.0050
Counting (low)
78.4%
±1.4, Mean of 3 runs, range 77.0 to 79.7
$0.0025
67.1%
±1.4, Mean of 3 runs, range 66.2 to 68.9
$0.0006
Counting (high)
79.3%
±2.0, Mean of 3 runs, range 77.0 to 81.1
$0.0056
70.7%
±3.4, Mean of 3 runs, range 66.2 to 73.0
$0.0015
Identification (low)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0013
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0004
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0021
84.4%
±6.3, Mean of 3 runs, range 78.1 to 90.6
$0.0007
OCR (low)
73.9%
$0.0032
51.8%
$0.0011
by category
Single value
74.8%
Transcription
91.1%
Structured JSON
86.7%
Text localization
29.4%
Single value
29.6%
Transcription
75.5%
Structured JSON
73.6%
Text localization
39.7%
OCR (high)
78.8%
$0.010
55.7%
$0.0041
by category
Single value
71.7%
Transcription
91.9%
Structured JSON
90.6%
Text localization
59.2%
Single value
33.0%
Transcription
83.5%
Structured JSON
77.0%
Text localization
43.8%
Reasoning (low)
80.8%
±2.0, Mean of 3 runs, range 78.8 to 82.8
$0.0022
60.5%
±5.0, Mean of 3 runs, range 55.0 to 64.9
$0.0006
Reasoning (high)
81.9%
±1.3, Mean of 3 runs, range 80.1 to 82.8
$0.0050
65.6%
±3.6, Mean of 3 runs, range 60.9 to 68.2
$0.0015

Gemini 3.7 Flash vs GPT-5.6 Luna: Overview

Gemini 3.7 Flash

Gemini 3.7 Flash is a proprietary multimodal model from Google, positioned in the Flash branch of the Gemini 3 series that trades some of the capacity of the larger Pro models for lower latency and lower cost per token. It accepts interleaved text and image input alongside other modalities handled by the Gemini family and returns text, and it continues the series pattern of exposing a configurable thinking budget so that reasoning effort can be scaled up for harder problems or reduced for high throughput extraction, routing and classification work. The model is announced roughly three weeks after Gemini 3.6 Flash, part of an unusually fast iteration cadence within the Flash line.

Google reports gains concentrated in agentic coding and front end generation, citing a WebDev Arena Elo of 1588 for this release compared with 1538 for the preceding Flash model, and describes it as producing more functional layouts and more feature complete applications in fewer prompts. Weights are not published and the architecture, parameter count and training corpus are undisclosed, consistent with prior Gemini releases. Visual capability follows the Flash lineage, covering image and document understanding, chart and diagram interpretation, text recognition in images, and general visual question answering.

GPT-5.6 Luna

GPT-5.6 Luna is the fastest and most cost-efficient model in OpenAI's GPT-5.6 family, which also includes Sol (the flagship tier) and Terra (the balanced mid-tier). Introduced under a new naming convention where the generation number (5.6) and a durable capability tier name (Luna, Terra, Sol) together define each model, Luna occupies the lightweight end of the family and is designed for high-volume, latency-sensitive workloads such as summarization, drafting, autocomplete, classification, and routine automation. The GPT-5.6 family as a whole advances capabilities in software engineering, computer use, professional knowledge work, scientific research, and cybersecurity, with all three tiers rated at the "High" capability level under OpenAI's Preparedness Framework for both cybersecurity and biological/chemical risk domains.

GPT-5.6 Luna supports multimodal input and function calling, and shares the family's 1.5 million token context window. On Terminal-Bench 2.1, Luna scores 82.5%, and on the Artificial Analysis Coding Agent Index it outperforms comparable models at roughly one-quarter the estimated cost of higher-tier alternatives. Luna supports the GPT-5.6 prompt caching scheme, which introduces explicit cache breakpoints and a 30-minute minimum cache life. The model was previewed on June 26, 2026 to a limited group of trusted partners via the OpenAI API and Codex, with general availability rolling out on July 9, 2026 across ChatGPT, Codex, and the API.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 3.7 Flash performed better. It scores higher on all five vision tasks and averages 80.1% (#6 of 63) against 64.7% (#29 of 63) for GPT-5.6 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 3.7 Flash leads with 73.9% against 51.8%. This is the widest gap between the two models across the benchmark's tasks.

GPT-5.6 Luna is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0011 per sample against $0.0033. Gemini 3.7 Flash is priced at $0.75 per 1M input tokens and $3.75 per 1M output; GPT-5.6 Luna is priced at $0.20 per 1M input tokens and $1.20 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

GPT-5.6 Luna is faster. Across Roboflow's Vision Evals it averaged 8.6s per inference against 11.0s. 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 open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.