Gemini 2.5 Pro vs GPT-6 Astra
Compare Gemini 2.5 Pro and GPT-6 Astra side-by-side. See how these vision models stack up in Object Detection, Open Prompt, Classification, OCR, and Image Captioning.
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Models in this comparison
Gemini 2.5 Pro vs GPT-6 Astra on Vision Evals
GPT-6 Astra scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where GPT-6 Astra leads 82.1% to 33.7%.
Overall, Gemini 2.5 Pro averages 66.0% (#34 of 53) against 86.6% (#1 of 53) for GPT-6 Astra.
Gemini 2.5 Pro is both cheaper ($0.0050 vs $0.030 per sample) and faster (6.1s vs 6.7s per sample).
Gemini 2.5 Pro vs GPT-6 Astra Comparison Table
Evals updated September 5, 2026Pricing updated September 5, 2026
| Property | Gemini 2.5 Pro | GPT-6 Astra |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | Sep 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | Undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | $10.00 |
| Output $/1M | $10.00 | $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 |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 66.0% | 86.6% |
| Avg cost / sample | $0.0050 | $0.030 |
| Avg speed / sample | 6.11s | 6.67s |
| By task | ||
| Object Detection (low) | 33.7% | 82.1% ±0.8, Mean of 3 runs, range 81.0 to 82.7 |
| Object Detection (high) | – | 83.6% ±0.8, Mean of 3 runs, range 82.8 to 84.5 |
| Counting (low) | 52.7% | 80.2% ±1.4, Mean of 3 runs, range 78.4 to 81.1 |
| Counting (high) | – | 81.1% ±1.4, Mean of 3 runs, range 79.7 to 82.4 |
| Identification (low) | 93.8% | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | – | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 88.8% | 91.9% ±0.2, Mean of 3 runs, range 91.6 to 92.1 |
| OCR (high) | – | 91.5% ±0.2, Mean of 3 runs, range 91.3 to 91.7 |
| Data Extraction (low) | 84.5% | 88.7% ±1.0, Mean of 3 runs, range 87.6 to 89.7 |
| Data Extraction (high) | – | 91.1% ±1.0, Mean of 3 runs, range 89.7 to 91.8 |
| Reasoning (low) | 42.4% | 87.2% ±1.0, Mean of 3 runs, range 86.1 to 88.1 |
| Reasoning (high) | 62.3% | 91.2% ±0.3, Mean of 3 runs, range 90.7 to 91.4 |
Gemini 2.5 Pro vs GPT-6 Astra: Overview
Gemini 2.5 Pro, released on June 17, 2025, is Google DeepMind’s most capable model in the Gemini 2.5 family, optimized for deep reasoning, coding, and complex multimodal tasks. It accepts text, images, audio, video, and PDFs as input and outputs text. The model supports 1 million input tokens with an output capacity of up to 65K tokens, enabling large-scale comprehension of datasets, codebases, and technical documents. Its training knowledge extends to January 2025.
Pro outperforms earlier Gemini 2.0 models across benchmarks, including agentic coding tasks where it achieved ~63.8% on SWE-Bench Verified. It supports structured outputs, function calling, code execution, search grounding, and URL context, making it well-suited for enterprise, STEM, and developer workflows. However, it does not currently support image or audio generation in its stable release, and its higher computational cost and latency make it less efficient than Flash or Flash-Lite. It is available via the Gemini API, Google AI Studio, and Vertex AI.
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 5 of the six vision tasks and averages 86.6% (#1 of 53) against 66.0% (#34 of 53) for Gemini 2.5 Pro. 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 33.7%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 2.5 Pro is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0050 per sample against $0.030. Gemini 2.5 Pro is priced at $1.25 per 1M input tokens and $10.00 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.
Gemini 2.5 Pro is faster. Across Roboflow's Vision Evals it averaged 6.1s per inference against 6.7s. 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 object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.