Gemini 3.8 Flash vs GPT-6 Astra
Compare Gemini 3.8 Flash and GPT-6 Astra side-by-side. See how these vision models stack up in Object Detection, Image Captioning, OCR, Classification, and Open Prompt.
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Models in this comparison
Gemini 3.8 Flash vs GPT-6 Astra on Vision Evals
GPT-6 Astra scores higher on 4 of the five Vision Evals tasks.
The widest gap is Object Detection, where GPT-6 Astra leads 82.1% to 68.1%.
Overall, Gemini 3.8 Flash averages 79.8% (#7 of 63) against 83.4% (#2 of 63) for GPT-6 Astra.
Gemini 3.8 Flash is cheaper ($0.0045 vs $0.034 per sample), while GPT-6 Astra is faster (9.9s vs 12.8s per sample).
Gemini 3.8 Flash vs GPT-6 Astra Comparison Table
Evals updated October 13, 2026Pricing updated October 20, 2026
| Property | Gemini 3.8 Flash | GPT-6 Astra |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Sep 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | Unknown | Undisclosed |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.825 | $10.00 |
| Output $/1M | $4.13 | $50.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Promptable Concept Segmentation | Not listed | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 79.8% | 83.4% |
| Avg cost / sample | $0.0045 | $0.034 |
| Avg speed / sample | 12.76s | 9.94s |
| By task | ||
| Object Detection (low) | 68.1% ±0.8, Mean of 3 runs, range 67.3 to 69.0 | 82.1% ±0.8, Mean of 3 runs, range 81.0 to 82.7 |
| Object Detection (high) | 74.8% ±1.1, Mean of 3 runs, range 73.4 to 75.6 | 83.6% ±0.8, Mean of 3 runs, range 82.8 to 84.5 |
| Counting (low) | 78.8% ±2.7, Mean of 3 runs, range 75.7 to 81.1 | 80.2% ±1.4, Mean of 3 runs, range 78.4 to 81.1 |
| Counting (high) | 79.3% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | 81.1% ±1.4, Mean of 3 runs, range 79.7 to 82.4 |
| Identification (low) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 72.8% | 77.8% |
| by category |
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| OCR (high) | 78.7% | 77.6% |
| by category |
|
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| Reasoning (low) | 81.2% ±0.3, Mean of 3 runs, range 80.8 to 81.5 | 87.2% ±1.0, Mean of 3 runs, range 86.1 to 88.1 |
| Reasoning (high) | 84.5% ±1.0, Mean of 3 runs, range 83.4 to 85.4 | 91.2% ±0.3, Mean of 3 runs, range 90.7 to 91.4 |
Gemini 3.8 Flash vs GPT-6 Astra: Overview
Gemini 3.8 Flash is a natively multimodal reasoning model in Google's Gemini 3 series, positioned as the speed and cost oriented Flash tier while targeting long-horizon software engineering, autonomous agents, and enterprise workflows. It accepts text, images, video, audio, and PDF documents in a single request and returns text, with an input limit of 1,048,576 tokens and an output limit of 65,536 tokens. Thinking is configurable at low, medium, and high levels, and the model supports function calling, code execution, structured outputs, context caching, search and Maps grounding, file search, and computer use in preview. Image generation, audio generation, and the Live API are not supported.
On vision oriented evaluations the model reports 86.2% on CharXiv Reasoning for chart and figure synthesis and 87.8% on LVBench for long video understanding in agentic mode, alongside 90.8% on Terminal-Bench 2.1 and 61.6% on SWE-Bench Pro for coding. Following Gemini API conventions, it can localize objects by emitting bounding boxes as [ymin, xmin, ymax, xmax] integers normalized to a 0 to 1000 range, which supports prompt driven detection and grounding in addition to captioning, document parsing, and visual question answering. The knowledge cutoff is March 2026, though coverage in some domains reflects the January 2025 cutoff shared across the Gemini 3 family.
GPT-6 Astra is a proprietary multimodal reasoning model from OpenAI that accepts text and image input and produces text output. It is positioned as the company's flagship system for long-horizon end-to-end work spanning complex reasoning, software engineering, computer use, browsing, research and document creation. The model exposes a graduated reasoning effort control with low, medium, high, xhigh and max settings, and it accepts a change to that setting partway through a conversation rather than only at request time. It launches as a single tier with no smaller mini or nano variants, carries a context window of roughly 1.05 million tokens with a maximum output of 128,000 tokens, and reports a knowledge cutoff of April 30, 2026.
OpenAI reports evaluation results across agentic, scientific and security benchmarks, including 96.0% on GPQA Diamond, 64.6% on Terminal-Bench Science, 72.6% on OSWorld 2.0, and a perfect score on ExploitBench, along with near saturation on FrontierMath Tier 4 and ARC-AGI-3. The model supports computer use, structured outputs, streaming, programmatic tool calling, multi-agent orchestration, prompt caching and persisted reasoning, and it keeps earlier context windows searchable so it can recover requirements or tool outputs from previous turns. OpenAI describes Astra as the first of its models to cross the Critical cybersecurity capability threshold under its Preparedness Framework.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6 Astra performed better. It scores higher on 4 of the five vision tasks and averages 83.4% (#2 of 63) against 79.8% (#7 of 63) for Gemini 3.8 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, GPT-6 Astra leads with 82.1% against 68.1%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.8 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0045 per sample against $0.034. Gemini 3.8 Flash is priced at $0.83 per 1M input tokens and $4.13 per 1M output; GPT-6 Astra is priced at $10.00 per 1M input tokens and $50.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
GPT-6 Astra is faster. Across Roboflow's Vision Evals it averaged 9.9s per inference against 12.8s. 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 object detection and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.