Gemini 3.1 Pro vs Gemini 4 Carbon
Gemini 4 Carbon and Gemini 3.1 Pro score 89.4% vs 76.8% averaged across five Vision Evals tasks (#1 and #16 of 68 models), so Gemini 4 Carbon leads overall.
- Gemini 4 Carbon wins 4 of the 5 tasks, with its widest lead on reasoning (91.4% vs 72.2%).
- Gemini 4 Carbon is about half the cost: $0.0061 vs $0.010 overall average cost per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 3.1 Pro vs Gemini 4 Carbon on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.1 Pro vs Gemini 4 Carbon Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Gemini 3.1 Pro | Gemini 4 Carbon |
|---|---|---|
| Organization | ||
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Nov 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Unknown | 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 | $12.00 | No published price |
| Vision Tasks | ||
| Promptable Concept Segmentation | Not listed | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 76.8% | 89.4% |
| Avg cost / sample | $0.010 | $0.0061 |
| Avg speed / sample | 8.44s | 8.71s |
| By task | ||
| Object Detection (low) | 67.4% | 84.5% ±0.3, Mean of 3 runs, range 84.2 to 84.8 |
| Object Detection (high) | – | 85.9% ±0.3, Mean of 3 runs, range 85.6 to 86.2 |
| Counting (low) | 71.6% | 90.1% ±1.4, Mean of 3 runs, range 89.2 to 91.9 |
| Counting (high) | – | 86.9% ±2.7, Mean of 3 runs, range 83.8 to 89.2 |
| Identification (low) | 100.0% | 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) | 72.6% | 87.2% |
| by category |
|
|
| OCR (high) | 73.1% | 89.7% |
| by category |
|
|
| Reasoning (low) | 72.2% | 91.4% ±1.7, Mean of 3 runs, range 90.1 to 93.4 |
| Reasoning (high) | 74.8% | 92.3% ±0.3, Mean of 3 runs, range 92.0 to 92.7 |
Gemini 3.1 Pro vs Gemini 4 Carbon: Overview
Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.
The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.
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 76.8% (#16 of 68) for Gemini 3.1 Pro. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Reasoning benchmark at low effort, Gemini 4 Carbon leads with 91.4% against 72.2%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 4 Carbon is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0061 per sample against $0.010. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3.1 Pro is faster. Across Roboflow's Vision Evals it averaged 8.4s per inference against 8.7s. 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 image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.