Gemini 3.5 Flash-Lite vs GPT-5.6 Luna
Compare Gemini 3.5 Flash-Lite and GPT-5.6 Luna side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, Object Detection, and OCR.
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Models in this comparison
Gemini 3.5 Flash-Lite vs GPT-5.6 Luna on Vision Evals
GPT-5.6 Luna scores higher on 3 of the five Vision Evals tasks.
The widest gap is Counting, where GPT-5.6 Luna leads 67.1% to 52.7%.
Overall, Gemini 3.5 Flash-Lite averages 61.7% (#33 of 63) against 64.7% (#29 of 63) for GPT-5.6 Luna.
GPT-5.6 Luna is cheaper ($0.0011 vs $0.0014 per sample), while Gemini 3.5 Flash-Lite is faster (3.9s vs 8.6s per sample).
Gemini 3.5 Flash-Lite vs GPT-5.6 Luna Comparison Table
Evals updated October 13, 2026Pricing updated October 20, 2026
| Property | Gemini 3.5 Flash-Lite | GPT-5.6 Luna |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Jul 2026 |
| Context Window | 1.0M | 1.5M |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | $0.200 |
| Output $/1M | $2.50 | $1.20 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Video Classification | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 61.7% | 64.7% |
| Avg cost / sample | $0.0014 | $0.0011 |
| Avg speed / sample | 3.92s | 8.60s |
| By task | ||
| Object Detection (low) | 57.5% | 61.0% ±1.2, Mean of 3 runs, range 59.9 to 62.2 |
| Object Detection (high) | – | 62.3% ±1.2, Mean of 3 runs, range 61.4 to 63.8 |
| Counting (low) | 52.7% | 67.1% ±1.4, Mean of 3 runs, range 66.2 to 68.9 |
| Counting (high) | – | 70.7% ±3.4, Mean of 3 runs, range 66.2 to 73.0 |
| Identification (low) | 84.4% | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | – | 84.4% ±6.3, Mean of 3 runs, range 78.1 to 90.6 |
| OCR (low) | 65.8% | 51.8% |
| by category |
|
|
| OCR (high) | 71.2% | 55.7% |
| by category |
|
|
| Reasoning (low) | 48.3% | 60.5% ±5.0, Mean of 3 runs, range 55.0 to 64.9 |
| Reasoning (high) | 68.9% | 65.6% ±3.6, Mean of 3 runs, range 60.9 to 68.2 |
Gemini 3.5 Flash-Lite vs GPT-5.6 Luna: Overview
Gemini 3.5 Flash-Lite is a natively multimodal reasoning model developed by Google DeepMind, released on July 21, 2026 as part of the Gemini 3.5 model family. It is the fastest model in the 3.5 series, designed for both low-latency tasks and high-throughput production workloads such as agentic search, document processing, receipt translation, and large-scale data extraction. The model accepts text, images, audio, and video as inputs, with a context window of up to 1 million tokens, and produces text output. It supports configurable thinking levels, allowing developers to tune the balance between response quality, cost, and latency depending on workload requirements.
On agentic and coding benchmarks, Gemini 3.5 Flash-Lite significantly outperforms its predecessor, Gemini 3.1 Flash-Lite, including on Terminal-Bench 2.1 (54% vs. 31%), GDM-MRCR v2 long-context (72.2% vs. 60.1%), and real-world task execution as measured by GDPval-AA v2 (1140 vs. 642). It also surpasses Gemini 3 Flash on SWE-Bench Pro (54.2% vs. 49.6%) and OSWorld-Verified (74.0% vs. 65.1%). According to the Artificial Analysis Index, the model generates output at approximately 350 tokens per second. It is built on the Gemini 3.5 Flash foundation and is evaluated across reasoning, coding, multimodal understanding, multilingual performance, and long-context tasks. The model is developed under Google's Frontier Safety Framework.
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, GPT-5.6 Luna performed better. It scores higher on 3 of the five vision tasks and averages 64.7% (#29 of 63) against 61.7% (#33 of 63) for Gemini 3.5 Flash-Lite. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Counting benchmark at low effort, GPT-5.6 Luna leads with 67.1% against 52.7%. 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 $0.0014. Gemini 3.5 Flash-Lite is priced at $0.30 per 1M input tokens and $2.50 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.
Gemini 3.5 Flash-Lite is faster. Across Roboflow's Vision Evals it averaged 3.9s per inference against 8.6s. 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.