Gemini 4 Argon vs GPT-5.6 Sol
Compare Gemini 4 Argon and GPT-5.6 Sol 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 4 Argon vs GPT-5.6 Sol on Vision Evals
Gemini 4 Argon scores higher on all five Vision Evals tasks.
The widest gap is Reasoning, where Gemini 4 Argon leads 89.4% to 66.0%.
Overall, Gemini 4 Argon averages 87.5% (#1 of 63) against 72.4% (#18 of 63) for GPT-5.6 Sol.
Gemini 4 Argon is cheaper ($0.0058 vs $0.0096 per sample), while GPT-5.6 Sol is faster (12.2s vs 13.4s per sample).
Gemini 4 Argon vs GPT-5.6 Sol Comparison Table
Evals updated October 13, 2026Pricing updated October 13, 2026
| Property | Gemini 4 Argon | GPT-5.6 Sol |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Jul 2026 |
| Context Window | 1.0M | 1.5M |
| Parameters | Undisclosed | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | No published price | $2.00 |
| Output $/1M | No published price | $10.00 |
| 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 |
| 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 | 87.5% | 72.4% |
| Avg cost / sample | $0.0058 | $0.0096 |
| Avg speed / sample | 13.40s | 12.16s |
| By task | ||
| Object Detection (low) | 82.5% ±0.5, Mean of 3 runs, range 81.9 to 83.0 | 68.4% ±0.7, Mean of 3 runs, range 67.9 to 69.3 |
| Object Detection (high) | 85.1% ±0.6, Mean of 3 runs, range 84.3 to 85.5 | 68.4% ±0.8, Mean of 3 runs, range 67.7 to 69.3 |
| Counting (low) | 86.0% ±0.7, Mean of 3 runs, range 85.1 to 86.5 | 74.3% ±1.4, Mean of 3 runs, range 73.0 to 75.7 |
| Counting (high) | 90.5% ±1.4, Mean of 3 runs, range 89.2 to 91.9 | 76.1% ±2.0, Mean of 3 runs, range 74.3 to 78.4 |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 89.6% ±4.7, Mean of 3 runs, range 84.4 to 93.8 |
| 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) | 85.7% | 63.6% |
| by category |
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| OCR (high) | 88.0% | 65.3% |
| by category |
|
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| Reasoning (low) | 89.4% ±1.3, Mean of 3 runs, range 88.1 to 90.7 | 66.0% ±2.6, Mean of 3 runs, range 63.6 to 68.9 |
| Reasoning (high) | 91.4% ±0.7, Mean of 3 runs, range 90.7 to 92.0 | 71.7% ±1.3, Mean of 3 runs, range 70.2 to 72.8 |
Gemini 4 Argon vs GPT-5.6 Sol: Overview
Gemini 4 Argon is Google DeepMind's frontier Gemini 4 model, built to sustain extended reasoning across long-horizon workflows in software engineering, enterprise knowledge work such as legal and finance, scientific analysis, and defensive cybersecurity. It accepts text, image, video, and document input and returns text, operating over a one million token context window. The model raises the maximum output length to one million tokens, compared with a 64K ceiling on earlier Gemini releases, so a single reasoning trajectory can run for hundreds of thousands of tokens before terminating.
On the visual side, Argon targets multimodal document comprehension, professional chart analysis, and retrieval of specific details from long recordings, reporting 91.7% on LVBench for long-video understanding and 71.6% on Chartography for chart reasoning. Google also reports 77.9% on DeepSWE v1.1, 51.3% on AutomationBench, 68% on CWE-bench v1, and first place on the Vals Index, which aggregates finance, coding, legal, and tax tasks. Following Gemini API conventions, it localizes 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 alongside captioning, document parsing, and visual question answering. Google describes adversarial training and automated red teaming for indirect prompt injection resistance, plus chain-of-thought and action monitors that halt execution when behavior drifts outside user intent.
GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.
GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 4 Argon performed better. It scores higher on all five vision tasks and averages 87.5% (#1 of 63) against 72.4% (#18 of 63) for GPT-5.6 Sol. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Gemini 4 Argon leads with 89.4% against 66.0%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 4 Argon is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0058 per sample against $0.0096. Actual costs depend on your image sizes, prompts, and output length.
GPT-5.6 Sol is faster. Across Roboflow's Vision Evals it averaged 12.2s per inference against 13.4s. 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 object detection and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.