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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Models in this comparison
Gemini 3.7 Flash vs GPT-5.6 Luna on Vision Evals
Gemini 3.7 Flash scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Gemini 3.7 Flash leads 80.8% to 60.5%.
Overall, Gemini 3.7 Flash averages 85.2% (#6 of 61) against 73.8% (#23 of 61) for GPT-5.6 Luna.
GPT-5.6 Luna is both cheaper ($0.0010 vs $0.0031 per sample) and faster (7.4s vs 16.5s per sample).
Gemini 3.7 Flash vs GPT-5.6 Luna Comparison Table
Evals updated September 29, 2026Pricing updated September 29, 2026
| Property | Gemini 3.7 Flash | GPT-5.6 Luna |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Aug 2026 | Jul 2026 |
| Context Window | 1.0M | 1.5M |
| Parameters | Undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.750 | $0.200 |
| Output $/1M | $3.75 | $1.20 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 85.2% | 73.8% |
| Avg cost / sample | $0.0031 | $0.0010 |
| Avg speed / sample | 16.50s | 7.38s |
| By task | ||
| Object Detection (low) | 71.0% ±0.9, Mean of 3 runs, range 69.8 to 71.6 | 61.0% ±1.2, Mean of 3 runs, range 59.9 to 62.2 |
| Object Detection (high) | 74.4% ±0.7, Mean of 3 runs, range 73.6 to 75.0 | 62.3% ±1.2, Mean of 3 runs, range 61.4 to 63.8 |
| Counting (low) | 78.4% ±1.4, Mean of 3 runs, range 77.0 to 79.7 | 67.1% ±1.4, Mean of 3 runs, range 66.2 to 68.9 |
| Counting (high) | 79.3% ±2.0, Mean of 3 runs, range 77.0 to 81.1 | 70.7% ±3.4, Mean of 3 runs, range 66.2 to 73.0 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 84.4% ±6.3, Mean of 3 runs, range 78.1 to 90.6 |
| OCR (low) | 88.2% ±1.6, Mean of 3 runs, range 86.9 to 90.0 | 90.7% ±1.8, Mean of 3 runs, range 88.4 to 92.0 |
| OCR (high) | 89.0% ±0.8, Mean of 3 runs, range 88.3 to 89.9 | 91.5% ±0.3, Mean of 3 runs, range 91.2 to 91.7 |
| Data Extraction (low) | 96.2% ±0.5, Mean of 3 runs, range 95.9 to 96.9 | 80.4% ±2.1, Mean of 3 runs, range 78.3 to 82.5 |
| Data Extraction (high) | 95.9% ±0.0, Mean of 3 runs, range 95.9 to 95.9 | 81.8% ±0.5, Mean of 3 runs, range 81.4 to 82.5 |
| Reasoning (low) | 80.8% ±2.0, Mean of 3 runs, range 78.8 to 82.8 | 60.5% ±5.0, Mean of 3 runs, range 55.0 to 64.9 |
| Reasoning (high) | 81.9% ±1.3, Mean of 3 runs, range 80.1 to 82.8 | 65.6% ±3.6, Mean of 3 runs, range 60.9 to 68.2 |
Gemini 3.7 Flash vs GPT-5.6 Luna: Overview
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 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 is priced at $1 per million input tokens and $6 per million output tokens, with cached input reads at $0.10 per million tokens under 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 5 of the six vision tasks and averages 85.2% (#6 of 61) against 73.8% (#23 of 61) 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 Reasoning benchmark at low effort, Gemini 3.7 Flash leads with 80.8% against 60.5%. 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 $0.0010 per sample against $0.0031. 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 7.4s per inference against 16.5s. 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.