Gemini 2.5 Flash vs GPT-6 Sol
Compare Gemini 2.5 Flash and GPT-6 Sol side-by-side. See how these vision models stack up in Open Prompt, OCR, Classification, Image Captioning, and Object Detection.
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Gemini 2.5 Flash vs GPT-6 Sol Comparison Table
Evals updated October 8, 2026Pricing updated October 9, 2026
| Property | Gemini 2.5 Flash | GPT-6 Sol |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2025 | Sep 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | Unknown | undisclosed |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | $2.00 |
| Output $/1M | $2.50 | $10.00 |
| 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 |
| Promptable Concept Segmentation | Not listed | Demo |
| 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 | Not evaluated | 76.9% |
| Avg cost / sample | – | $0.0074 |
| Avg speed / sample | – | 10.20s |
| By task | ||
| Object Detection (low) | – | 74.7% ±0.7, Mean of 3 runs, range 74.1 to 75.5 |
| Object Detection (high) | – | 76.4% ±1.2, Mean of 3 runs, range 75.0 to 77.4 |
| Counting (low) | – | 76.6% ±2.0, Mean of 3 runs, range 74.3 to 78.4 |
| Counting (high) | – | 77.5% ±1.3, Mean of 3 runs, range 75.7 to 78.4 |
| Identification (low) | – | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 |
| Identification (high) | – | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | – | 66.7% |
| by category |
| |
| OCR (high) | – | 68.8% |
| by category |
| |
| Reasoning (low) | – | 72.6% ±3.6, Mean of 3 runs, range 69.5 to 76.8 |
| Reasoning (high) | – | 77.7% ±1.0, Mean of 3 runs, range 76.8 to 78.8 |
Gemini 2.5 Flash vs GPT-6 Sol: Overview
Gemini 2.5 Flash, released on June 17, 2025, is Google DeepMind’s production-ready, efficiency-focused model in the Gemini 2.5 family. It is multimodal, accepting text, images, video, and audio as inputs, with text as the primary output format. The model supports 1 million input tokens and up to 65K output tokens, enabling it to process very large contexts such as books, long video transcripts, or extensive datasets. Its training knowledge extends to January 2025.
Designed as a price-performance leader, Gemini 2.5 Flash balances speed and reasoning power, making it suitable for everyday enterprise and developer use cases without the higher latency and cost of Pro models. It supports advanced workflows like function calling, code execution, search grounding, URL context ingestion, and structured outputs. While efficient and scalable, output length is still limited compared to its input capacity, and multimodal outputs (e.g. image or audio generation) remain restricted to specialized or preview variants.
GPT-6 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.
The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. OpenAI reports results including 33.2% on AutomationBench at xhigh effort and 56.4% on Agents' Last Exam at max effort, while its reported DeepSWE and OSWorld 2.0 figures of 68.8% and 64.4% fall below those of the earlier GPT-5.6 Sol. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.
Frequently Asked Questions
Gemini 2.5 Flash has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
Yes. The comparison demo on this page runs both models on the same image side by side for open prompts and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.