Gemini 4 Carbon vs GPT-5 Mini
Gemini 4 Carbon: 89.4% average across five Vision Evals tasks (#1 of 68 models). We have not run GPT-5 Mini 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 4 Carbon vs GPT-5 Mini Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Gemini 4 Carbon | GPT-5 Mini |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Aug 2025 |
| Context Window | 1.0M | 400K |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | No published price | $0.250 |
| Output $/1M | No published price | $2.00 |
| Vision Tasks | ||
| Promptable Concept Segmentation | Demo | Not listed |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 89.4% | Not evaluated |
| Avg cost / sample | $0.0061 | – |
| Avg speed / sample | 8.71s | – |
| By task | ||
| Object Detection (low) | 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) | 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) | 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) | 87.2% | – |
| by category |
| |
| OCR (high) | 89.7% | – |
| by category |
| |
| Reasoning (low) | 91.4% ±1.7, Mean of 3 runs, range 90.1 to 93.4 | – |
| Reasoning (high) | 92.3% ±0.3, Mean of 3 runs, range 92.0 to 92.7 | – |
Gemini 4 Carbon vs GPT-5 Mini: Overview
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.
GPT-5 Mini, released by OpenAI on August 7, 2025, is a mid-tier variant of the GPT-5 family that balances cost, speed, and capability. It is multimodal, supporting both text and image inputs, and offers a substantial input context window of ~400,000 tokens with output lengths up to ~128,000 tokens. While less powerful than the full GPT-5, it inherits its safety tuning, instruction-following improvements, and multimodal reasoning, making it a practical choice for developers who need large context handling without the expense of premium models.
GPT-5 Mini is optimized for affordability while retaining strong reasoning performance. Benchmarks show it outperforming earlier models such as GPT-4o on many multimodal and medical VQA tasks, though it lags behind GPT-5 on the most complex problems. Ideal use cases include prototyping, scalable content generation, document analysis, and mid-range reasoning tasks where efficiency and context capacity matter more than top-tier accuracy.