Gemini 3.7 Flash vs Grok 4.6
Compare Gemini 3.7 Flash and Grok 4.6 side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.
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Models in this comparison
Gemini 3.7 Flash vs Grok 4.6 on Vision Evals
Gemini 3.7 Flash scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where Gemini 3.7 Flash leads 71.0% to 23.8%.
Overall, Gemini 3.7 Flash averages 85.2% (#6 of 61) against 68.7% (#33 of 61) for Grok 4.6.
Gemini 3.7 Flash is both cheaper ($0.0031 vs $0.0097 per sample) and faster (16.5s vs 17.5s per sample).
Gemini 3.7 Flash vs Grok 4.6 Comparison Table
Evals updated September 29, 2026Pricing updated September 29, 2026
| Property | Gemini 3.7 Flash | Grok 4.6 |
|---|---|---|
| Organization | SpaceXAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Aug 2026 | Aug 2026 |
| Context Window | 1.0M | 500K |
| Parameters | Undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.750 | $2.00 |
| Output $/1M | $3.75 | $6.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 85.2% | 68.7% |
| Avg cost / sample | $0.0031 | $0.0097 |
| Avg speed / sample | 16.50s | 17.55s |
| By task | ||
| Object Detection (low) | 71.0% ±0.9, Mean of 3 runs, range 69.8 to 71.6 | 23.8% ±2.8, Mean of 3 runs, range 20.2 to 25.9 |
| Object Detection (high) | 74.4% ±0.7, Mean of 3 runs, range 73.6 to 75.0 | 24.0% ±1.0, Mean of 3 runs, range 23.1 to 25.1 |
| Counting (low) | 78.4% ±1.4, Mean of 3 runs, range 77.0 to 79.7 | 65.8% ±4.1, Mean of 3 runs, range 62.2 to 70.3 |
| Counting (high) | 79.3% ±2.0, Mean of 3 runs, range 77.0 to 81.1 | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 84.4% ±3.1, Mean of 3 runs, range 81.3 to 87.5 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 85.4% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| OCR (low) | 88.2% ±1.6, Mean of 3 runs, range 86.9 to 90.0 | 91.8% ±0.3, Mean of 3 runs, range 91.5 to 92.1 |
| OCR (high) | 89.0% ±0.8, Mean of 3 runs, range 88.3 to 89.9 | 91.6% ±0.2, Mean of 3 runs, range 91.4 to 91.7 |
| Data Extraction (low) | 96.2% ±0.5, Mean of 3 runs, range 95.9 to 96.9 | 85.6% ±1.0, Mean of 3 runs, range 84.5 to 86.6 |
| Data Extraction (high) | 95.9% ±0.0, Mean of 3 runs, range 95.9 to 95.9 | 85.6% ±1.0, Mean of 3 runs, range 84.5 to 86.6 |
| Reasoning (low) | 80.8% ±2.0, Mean of 3 runs, range 78.8 to 82.8 | 61.1% ±1.3, Mean of 3 runs, range 59.6 to 62.3 |
| Reasoning (high) | 81.9% ±1.3, Mean of 3 runs, range 80.1 to 82.8 | 63.8% ±2.0, Mean of 3 runs, range 62.3 to 66.2 |
Gemini 3.7 Flash vs Grok 4.6: Overview
Gemini 3.7 Flash is a proprietary multimodal model from Google, positioned in the Flash branch of the Gemini 3 series that trades some of the capacity of the larger Pro models for lower latency and lower cost per token. It accepts interleaved text and image input alongside other modalities handled by the Gemini family and returns text, and it continues the series pattern of exposing a configurable thinking budget so that reasoning effort can be scaled up for harder problems or reduced for high throughput extraction, routing and classification work. The model is announced roughly three weeks after Gemini 3.6 Flash, part of an unusually fast iteration cadence within the Flash line.
Google reports gains concentrated in agentic coding and front end generation, citing a WebDev Arena Elo of 1588 for this release compared with 1538 for the preceding Flash model, and describes it as producing more functional layouts and more feature complete applications in fewer prompts. Weights are not published and the architecture, parameter count and training corpus are undisclosed, consistent with prior Gemini releases. Visual capability follows the Flash lineage, covering image and document understanding, chart and diagram interpretation, text recognition in images, and general visual question answering.
Grok 4.6 is a proprietary reasoning model from xAI aimed at long-running agentic workflows, coding, and knowledge work. It accepts text and image input and returns text, with a 500,000 token context window and a knowledge cutoff of February 1, 2026. The model exposes an adjustable reasoning budget with low, medium, high, and xhigh settings, where high is the default, and it supports function calling, structured outputs, web and X search, and code execution as documented tool behaviors. Its visual capability covers interpreting images supplied alongside text prompts, which places it in the visual question answering and document understanding family, and it can also return object detection boxes as text coordinates when prompted.
xAI characterizes Grok 4.6 as the result of an extended post-training run over the Grok 4.5 lineage rather than a new pretrained base. The described recipe combines curated model-generated reasoning and technical data, engineering data, a revised optimizer, regenerated supervised fine-tuning trajectories, and reinforcement learning across agent environments spanning knowledge work, coding, kernel optimization, web development, and computer-aided design. Parameter count and architecture specifics are not disclosed. Independent measurement from Artificial Analysis places the model at 61 on its Intelligence Index, five points above Grok 4.5.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.7 Flash performed better. It scores higher on 5 of the six vision tasks and averages 85.2% (#6 of 61) against 68.7% (#33 of 61) for Grok 4.6. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Object Detection benchmark at low effort, Gemini 3.7 Flash leads with 71.0% against 23.8%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.7 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0031 per sample against $0.0097. Gemini 3.7 Flash is priced at $0.75 per 1M input tokens and $3.75 per 1M output; Grok 4.6 is priced at $2.00 per 1M input tokens and $6.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3.7 Flash is faster. Across Roboflow's Vision Evals it averaged 16.5s per inference against 17.5s. 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.