Gemini 3.6 Flash vs GPT-6 Astra
Compare Gemini 3.6 Flash and GPT-6 Astra side-by-side. See how these vision models stack up in Open Prompt, Classification, Image Captioning, OCR, and Object Detection.
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Models in this comparison
Gemini 3.6 Flash vs GPT-6 Astra on Vision Evals
GPT-6 Astra scores higher on 3 of the six Vision Evals tasks.
The widest gap is Object Detection, where GPT-6 Astra leads 82.1% to 57.1%.
Overall, Gemini 3.6 Flash averages 83.0% (#7 of 53) against 86.6% (#1 of 53) for GPT-6 Astra.
Gemini 3.6 Flash is cheaper ($0.0032 vs $0.030 per sample), while GPT-6 Astra is faster (6.7s vs 14.7s per sample).
Gemini 3.6 Flash vs GPT-6 Astra Comparison Table
Evals updated September 5, 2026Pricing updated September 5, 2026
| Property | Gemini 3.6 Flash | GPT-6 Astra |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Sep 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | Undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.750 | $10.00 |
| Output $/1M | $3.75 | $50.00 |
| 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 |
| Video Classification | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 83.0% | 86.6% |
| Avg cost / sample | $0.0032 | $0.030 |
| Avg speed / sample | 14.66s | 6.67s |
| By task | ||
| Object Detection (low) | 57.1% ±1.7, Mean of 3 runs, range 55.9 to 59.4 | 82.1% ±0.8, Mean of 3 runs, range 81.0 to 82.7 |
| Object Detection (high) | 70.7% ±0.4, Mean of 3 runs, range 70.3 to 71.2 | 83.6% ±0.8, Mean of 3 runs, range 82.8 to 84.5 |
| Counting (low) | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 80.2% ±1.4, Mean of 3 runs, range 78.4 to 81.1 |
| Counting (high) | 79.3% ±2.7, Mean of 3 runs, range 77.0 to 82.4 | 81.1% ±1.4, Mean of 3 runs, range 79.7 to 82.4 |
| Identification (low) | 99.0% ±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) | 100.0% ±0.0, Mean of 3 runs, range 100.0 to 100.0 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 88.2% ±0.3, Mean of 3 runs, range 87.9 to 88.4 | 91.9% ±0.2, Mean of 3 runs, range 91.6 to 92.1 |
| OCR (high) | 89.5% ±0.0, Mean of 3 runs, range 89.5 to 89.6 | 91.5% ±0.2, Mean of 3 runs, range 91.3 to 91.7 |
| Data Extraction (low) | 95.9% ±1.0, Mean of 3 runs, range 94.8 to 96.9 | 88.7% ±1.0, Mean of 3 runs, range 87.6 to 89.7 |
| Data Extraction (high) | 94.8% ±1.0, Mean of 3 runs, range 93.8 to 95.9 | 91.1% ±1.0, Mean of 3 runs, range 89.7 to 91.8 |
| Reasoning (low) | 77.7% ±2.0, Mean of 3 runs, range 76.2 to 80.1 | 87.2% ±1.0, Mean of 3 runs, range 86.1 to 88.1 |
| Reasoning (high) | 81.0% ±2.0, Mean of 3 runs, range 79.5 to 83.4 | 91.2% ±0.3, Mean of 3 runs, range 90.7 to 91.4 |
Gemini 3.6 Flash vs GPT-6 Astra: 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%. Computer use performance on OSWorld-Verified improves from 78.4% to 83%, and knowledge work scores on GDPval-AA v2 rise from 1349 to 1421. 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.
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 3 of the six vision tasks and averages 86.6% (#1 of 53) against 83.0% (#7 of 53) 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, GPT-6 Astra leads with 82.1% 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 $0.0032 per sample against $0.030. Gemini 3.6 Flash is priced at $0.75 per 1M input tokens and $3.75 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 6.7s per inference against 14.7s. 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.