Gemini 4 Argon vs Gemma 4 31B
Compare Gemini 4 Argon and Gemma 4 31B side-by-side. See how these vision models stack up in Object Detection, Image Captioning, OCR, Classification, and Open Prompt.
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Models in this comparison
Gemini 4 Argon vs Gemma 4 31B on Vision Evals
Gemini 4 Argon scores higher on all 4 Vision Evals tasks.
The widest gap is Reasoning, where Gemini 4 Argon leads 89.4% to 52.8%.
Overall, Gemini 4 Argon averages 87.5% (#1 of 63) against 57.7% (#39 of 63) for Gemma 4 31B.
Gemma 4 31B is cheaper ($0.0015 vs $0.0058 per sample), while Gemini 4 Argon is faster (13.4s vs 34.4s per sample).
Gemini 4 Argon vs Gemma 4 31B Comparison Table
Evals updated October 13, 2026Pricing updated October 13, 2026
| Property | Gemini 4 Argon | Gemma 4 31B |
|---|---|---|
| Organization | ||
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Apr 2026 |
| Context Window | 1.0M | 256K |
| Parameters | Undisclosed | 31B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.090 |
| Output $/1M | $10.00 | $0.340 |
| 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 | 87.5% | 57.7% 4/5 tasks |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0058 | $0.0015 |
| Avg speed / sample | 13.40s | 34.36s |
| By task | ||
| Object Detection (low) | 82.5% ±0.5, Mean of 3 runs, range 81.9 to 83.0 | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 |
| Object Detection (high) | 85.1% ±0.6, Mean of 3 runs, range 84.3 to 85.5 | – |
| Counting (low) | 86.0% ±0.7, Mean of 3 runs, range 85.1 to 86.5 | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 |
| Counting (high) | 90.5% ±1.4, Mean of 3 runs, range 89.2 to 91.9 | – |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | – |
| OCR (low) | 85.7% | – |
| by category |
| |
| OCR (high) | 88.0% | – |
| by category |
| |
| Reasoning (low) | 89.4% ±1.3, Mean of 3 runs, range 88.1 to 90.7 | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 |
| Reasoning (high) | 91.4% ±0.7, Mean of 3 runs, range 90.7 to 92.0 | – |
Gemini 4 Argon vs Gemma 4 31B: Overview
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.
Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.
For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 4 Argon performed better. It scores higher on all 4 vision tasks and averages 87.5% (#1 of 63) against 57.7% (#39 of 63) for Gemma 4 31B. 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 4 Argon leads with 89.4% against 52.8%. This is the widest gap between the two models across the benchmark's tasks.
Gemma 4 31B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0015 per sample against $0.0058. Actual costs depend on your image sizes, prompts, and output length.
Gemini 4 Argon is faster. Across Roboflow's Vision Evals it averaged 13.4s per inference against 34.4s. 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 captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.