Gemini 4 Argon vs Gemini 4 Carbon
Gemini 4 Carbon and Gemini 4 Argon score 89.4% vs 87.5% averaged across five Vision Evals tasks (#1 and #2 of 68 models), so Gemini 4 Carbon leads overall.
- Gemini 4 Carbon wins 4 of the 5 tasks, with its widest lead on counting (90.1% vs 86.0%).
- Gemini 4 Carbon is faster: 8.7s vs 13.4s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 4 Argon vs Gemini 4 Carbon on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 4 Argon vs Gemini 4 Carbon Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Gemini 4 Argon | Gemini 4 Carbon |
|---|---|---|
| Organization | ||
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Nov 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Undisclosed | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | No published price |
| Output $/1M | $10.00 | No published price |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 87.5% | 89.4% |
| Avg cost / sample | $0.0058 | $0.0061 |
| Avg speed / sample | 13.40s | 8.71s |
| By task | ||
| Object Detection (low) | 82.5% ±0.5, Mean of 3 runs, range 81.9 to 83.0 | 84.5% ±0.3, Mean of 3 runs, range 84.2 to 84.8 |
| Object Detection (high) | 85.1% ±0.6, Mean of 3 runs, range 84.3 to 85.5 | 85.9% ±0.3, Mean of 3 runs, range 85.6 to 86.2 |
| Counting (low) | 86.0% ±0.7, Mean of 3 runs, range 85.1 to 86.5 | 90.1% ±1.4, Mean of 3 runs, range 89.2 to 91.9 |
| Counting (high) | 90.5% ±1.4, Mean of 3 runs, range 89.2 to 91.9 | 86.9% ±2.7, Mean of 3 runs, range 83.8 to 89.2 |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 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 | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| OCR (low) | 85.7% | 87.2% |
| by category |
|
|
| OCR (high) | 88.0% | 89.7% |
| by category |
|
|
| Reasoning (low) | 89.4% ±1.3, Mean of 3 runs, range 88.1 to 90.7 | 91.4% ±1.7, Mean of 3 runs, range 90.1 to 93.4 |
| Reasoning (high) | 91.4% ±0.7, Mean of 3 runs, range 90.7 to 92.0 | 92.3% ±0.3, Mean of 3 runs, range 92.0 to 92.7 |
Gemini 4 Argon vs Gemini 4 Carbon: 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.
Gemini 4 Carbon is a multimodal model from Google. It builds on Gemini 4 Argon and supports reasoning over text and images. Google describes improvements in coding and long-running tasks. Model weights are not publicly released.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 4 Carbon performed better. It scores higher on 4 of the five vision tasks and averages 89.4% (#1 of 68) against 87.5% (#2 of 68) for Gemini 4 Argon. 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, Gemini 4 Carbon leads with 90.1% against 86.0%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 4 Argon is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0058 per sample against $0.0061. Actual costs depend on your image sizes, prompts, and output length.
Gemini 4 Carbon is faster. Across Roboflow's Vision Evals it averaged 8.7s 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 captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.