Gemini 3 Flash vs Gemini 4 Argon
Compare Gemini 3 Flash and Gemini 4 Argon side-by-side. See how these vision models stack up in Object Detection, Classification, Open Prompt, OCR, and Image Captioning.
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Models in this comparison
Gemini 3 Flash vs Gemini 4 Argon on Vision Evals
Gemini 4 Argon scores higher on 4 of the five Vision Evals tasks.
The widest gap is Object Detection, where Gemini 4 Argon leads 82.5% to 38.6%.
Overall, Gemini 3 Flash averages 65.7% (#27 of 63) against 87.5% (#1 of 63) for Gemini 4 Argon.
Gemini 3 Flash is both cheaper ($0.0024 vs $0.0058 per sample) and faster (5.8s vs 13.4s per sample).
Gemini 3 Flash vs Gemini 4 Argon Comparison Table
Evals updated October 13, 2026Pricing updated October 13, 2026
| Property | Gemini 3 Flash | Gemini 4 Argon |
|---|---|---|
| Organization | ||
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Dec 2025 | Oct 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 | $0.500 | $2.00 |
| Output $/1M | $3.00 | $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 |
| 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 | 65.7% | 87.5% |
| Avg cost / sample | $0.0024 | $0.0058 |
| Avg speed / sample | 5.83s | 13.40s |
| By task | ||
| Object Detection (low) | 38.6% | 82.5% ±0.5, Mean of 3 runs, range 81.9 to 83.0 |
| Object Detection (high) | – | 85.1% ±0.6, Mean of 3 runs, range 84.3 to 85.5 |
| Counting (low) | 67.6% | 86.0% ±0.7, Mean of 3 runs, range 85.1 to 86.5 |
| Counting (high) | – | 90.5% ±1.4, Mean of 3 runs, range 89.2 to 91.9 |
| Identification (low) | 93.8% | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 |
| Identification (high) | – | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| OCR (low) | 63.9% | 85.7% |
| by category |
|
|
| OCR (high) | 73.1% | 88.0% |
| by category |
|
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| Reasoning (low) | 64.9% | 89.4% ±1.3, Mean of 3 runs, range 88.1 to 90.7 |
| Reasoning (high) | 74.2% | 91.4% ±0.7, Mean of 3 runs, range 90.7 to 92.0 |
Gemini 3 Flash vs Gemini 4 Argon: Overview
Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.
The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.
Gemini 4 Argon is Google DeepMind's frontier Gemini 4 model, built to sustain extended reasoning across long-horizon workflows in software engineering, enterprise knowledge work such as legal and finance, scientific analysis, and defensive cybersecurity. It accepts text, image, video, and document input and returns text, operating over a one million token context window. The model raises the maximum output length to one million tokens, compared with a 64K ceiling on earlier Gemini releases, so a single reasoning trajectory can run for hundreds of thousands of tokens before terminating.
On the visual side, Argon targets multimodal document comprehension, professional chart analysis, and retrieval of specific details from long recordings, reporting 91.7% on LVBench for long-video understanding and 71.6% on Chartography for chart reasoning. Google also reports 77.9% on DeepSWE v1.1, 51.3% on AutomationBench, 68% on CWE-bench v1, and first place on the Vals Index, which aggregates finance, coding, legal, and tax tasks. Following Gemini API conventions, it localizes objects by emitting bounding boxes as [ymin, xmin, ymax, xmax] integers normalized to a 0 to 1000 range, which supports prompt driven detection and grounding alongside captioning, document parsing, and visual question answering. Google describes adversarial training and automated red teaming for indirect prompt injection resistance, plus chain-of-thought and action monitors that halt execution when behavior drifts outside user intent.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 4 Argon performed better. It scores higher on 4 of the five vision tasks and averages 87.5% (#1 of 63) against 65.7% (#27 of 63) for Gemini 3 Flash. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Gemini 4 Argon leads with 82.5% against 38.6%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0024 per sample against $0.0058. Gemini 3 Flash is priced at $0.50 per 1M input tokens and $3.00 per 1M output; Gemini 4 Argon is priced at $2.00 per 1M input tokens and $10.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3 Flash is faster. Across Roboflow's Vision Evals it averaged 5.8s per inference against 13.4s. 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 object detection and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.