Gemini 2.5 Flash vs Gemini 3.7 Flash
Gemini 3.7 Flash: 80.1% average across five Vision Evals tasks (#10 of 69 models). We have not run Gemini 2.5 Flash through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
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Gemini 2.5 Flash vs Gemini 3.7 Flash Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Gemini 2.5 Flash | Gemini 3.7 Flash |
|---|---|---|
| Organization | ||
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2025 | Aug 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Unknown | Undisclosed |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | $0.750 |
| Output $/1M | $2.50 | $3.75 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 80.1% |
| Avg cost / sample | – | $0.0033 |
| Avg speed / sample | – | 11.04s |
| By task | ||
| Object Detection (low) | – | 70.5% ±1.1, Mean of 3 runs, range 69.4 to 71.5 |
| Object Detection (high) | – | 74.3% ±0.8, Mean of 3 runs, range 73.3 to 75.0 |
| Counting (low) | – | 78.4% ±1.4, Mean of 3 runs, range 77.0 to 79.7 |
| Counting (high) | – | 79.3% ±2.0, Mean of 3 runs, range 77.0 to 81.1 |
| Identification (low) | – | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| Identification (high) | – | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| OCR (low) | – | 73.9% |
| by category |
| |
| OCR (high) | – | 78.8% |
| by category |
| |
| Reasoning (low) | – | 80.8% ±2.0, Mean of 3 runs, range 78.8 to 82.8 |
| Reasoning (high) | – | 81.9% ±1.3, Mean of 3 runs, range 80.1 to 82.8 |
Gemini 2.5 Flash vs Gemini 3.7 Flash: 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.
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.