Gemini 3.6 Flash vs Gemini 4 Carbon
Gemini 4 Carbon and Gemini 3.6 Flash score 89.4% vs 77.1% averaged across five Vision Evals tasks (#1 and #14 of 68 models), so Gemini 4 Carbon leads overall.
- Gemini 4 Carbon wins 4 of the 5 tasks, with its widest lead on object detection (84.5% vs 57.1%).
- Gemini 3.6 Flash is about half the cost: $0.0033 vs $0.0061 overall average cost per sample.
- Gemini 4 Carbon is faster: 8.7s vs 10.4s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 3.6 Flash vs Gemini 4 Carbon on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.6 Flash vs Gemini 4 Carbon Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Gemini 3.6 Flash | Gemini 4 Carbon |
|---|---|---|
| Organization | ||
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 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 | $0.750 | No published price |
| Output $/1M | $3.75 | No published price |
| Vision Tasks | ||
| Promptable Concept Segmentation | Not listed | Demo |
| Video Classification | Supported | Not listed |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 77.1% | 89.4% |
| Avg cost / sample | $0.0033 | $0.0061 |
| Avg speed / sample | 10.43s | 8.71s |
| By task | ||
| Object Detection (low) | 57.1% ±1.7, Mean of 3 runs, range 55.9 to 59.4 | 84.5% ±0.3, Mean of 3 runs, range 84.2 to 84.8 |
| Object Detection (high) | 70.7% ±0.4, Mean of 3 runs, range 70.3 to 71.2 | 85.9% ±0.3, Mean of 3 runs, range 85.6 to 86.2 |
| Counting (low) | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 90.1% ±1.4, Mean of 3 runs, range 89.2 to 91.9 |
| Counting (high) | 79.3% ±2.7, Mean of 3 runs, range 77.0 to 82.4 | 86.9% ±2.7, Mean of 3 runs, range 83.8 to 89.2 |
| Identification (low) | 99.0% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 |
| Identification (high) | 100.0% ±0.0, Mean of 3 runs, range 100.0 to 100.0 | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| OCR (low) | 71.8% | 87.2% |
| by category |
|
|
| OCR (high) | 77.4% | 89.7% |
| by category |
|
|
| Reasoning (low) | 77.7% ±2.0, Mean of 3 runs, range 76.2 to 80.1 | 91.4% ±1.7, Mean of 3 runs, range 90.1 to 93.4 |
| Reasoning (high) | 81.0% ±2.0, Mean of 3 runs, range 79.5 to 83.4 | 92.3% ±0.3, Mean of 3 runs, range 92.0 to 92.7 |
Gemini 3.6 Flash vs Gemini 4 Carbon: Overview
Gemini 3.6 Flash is a multimodal language model from Google DeepMind, positioned as the workhorse tier in the Gemini 3.x family. It accepts text, image, video, audio, and PDF inputs with a 1 million token context window and produces up to 64,000 output tokens. The model builds directly on Gemini 3.5 Flash, incorporating developer and customer feedback to improve token efficiency, coding quality, and knowledge work performance. According to the Artificial Analysis Index, it consumes 17% fewer output tokens than its predecessor, and on some benchmarks such as DeepSWE, token reduction reaches up to 65%. It supports function calling, structured output, search as a tool, and code execution, and includes computer use as a built-in capability in the Gemini API and Gemini Enterprise.
On coding benchmarks, Gemini 3.6 Flash scores 49% on DeepSWE versus 37% for 3.5 Flash, and 63.9% on MLE Bench versus 49.7%. The model carries a knowledge cutoff of March 2026 and ships with enhanced Frontier Safety safeguards covering chemical, biological, radiological, nuclear, and cyber offense domains, with training to minimize refusals for beneficial uses. It is a proprietary, closed-weights model available in preview through the Gemini API via Google AI Studio, Android Studio, Google Antigravity, Gemini Enterprise, and the Gemini app.
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 77.1% (#14 of 68) for Gemini 3.6 Flash. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Gemini 4 Carbon leads with 84.5% against 57.1%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.6 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0033 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 10.4s. 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 open prompts and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.