Gemini 2.5 Flash-Lite vs Gemini 4 Carbon
Gemini 4 Carbon: 89.4% average across five Vision Evals tasks (#1 of 68 models). We have not run Gemini 2.5 Flash-Lite 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 2.5 Flash-Lite vs Gemini 4 Carbon Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Gemini 2.5 Flash-Lite | Gemini 4 Carbon |
|---|---|---|
| Organization | ||
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2025 | 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 | $0.100 | No published price |
| Output $/1M | $0.400 | 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 | Not evaluated | 89.4% |
| 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 2.5 Flash-Lite vs Gemini 4 Carbon: Overview
Gemini 2.5 Flash-Lite, released for general availability on July 22, 2025, is the most cost-efficient model in the Gemini 2.5 family, designed for high-volume and latency-sensitive tasks. It is multimodal, supporting text, images, video, audio, and PDFs as inputs, with text as its primary output. The model handles up to 1 million input tokens and generates outputs up to 64K tokens, making it suitable for large-scale document or media processing at low cost. It is built on a Sparse Mixture-of-Experts architecture with native multimodal support, though exact parameter counts are undisclosed.
Flash-Lite offers the lowest usage cost among Gemini 2.5 models. It introduces developer controls for “thinking mode,” allowing fine-tuning of reasoning depth vs. efficiency. It also integrates native tools such as code execution, search grounding, and URL context. While strong on translation, classification, coding, and general multimodal reasoning, it lacks support for image or audio generation in its stable release and is less capable than Gemini 2.5 Flash or Pro on complex reasoning-heavy workflows.
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.