Gemma 4 26B A4B vs GPT-6 Sol
Compare Gemma 4 26B A4B and GPT-6 Sol side-by-side.
Compare Gemma 4 26B A4B vs GPT-6 Sol live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
Gemma 4 26B A4B vs GPT-6 Sol on Vision Evals
GPT-6 Sol scores higher on all six Vision Evals tasks.
The widest gap is Counting, where GPT-6 Sol leads 74.8% to 43.2%.
Overall, Gemma 4 26B A4B averages 63.6% (#46 of 57) against 80.7% (#10 of 57) for GPT-6 Sol.
Gemma 4 26B A4B is cheaper ($0.0019 vs $0.0065 per sample), while GPT-6 Sol is faster (8.1s vs 27.8s per sample).
Gemma 4 26B A4B vs GPT-6 Sol Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Gemma 4 26B A4B | GPT-6 Sol |
|---|---|---|
| Organization | OpenAI | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Sep 2026 |
| Context Window | 256K | 1.1M |
| Parameters | 25.2B | undisclosed |
| License | Apache 2.0 | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.090 | |
| Output $/1M | $0.300 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 63.6% | 80.7% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0019 | $0.0065 |
| Avg speed / sample | 27.84s | 8.15s |
| By task | ||
| Object Detection (low) | 44.2% ±0.7, Mean of 3 runs, range 43.5 to 44.8 | 73.6% ±0.6, Mean of 3 runs, range 72.9 to 74.2 |
| Object Detection (high) | – | 75.2% ±0.9, Mean of 3 runs, range 74.1 to 75.9 |
| Counting (low) | 43.2% ±2.0, Mean of 3 runs, range 41.9 to 46.0 | 74.8% ±2.7, Mean of 3 runs, range 71.6 to 77.0 |
| Counting (high) | – | 76.1% ±3.4, Mean of 3 runs, range 71.6 to 78.4 |
| Identification (low) | 81.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 | 91.7% ±3.1, Mean of 3 runs, range 87.5 to 93.8 |
| Identification (high) | – | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 88.7% ±1.3, Mean of 3 runs, range 87.6 to 90.2 | 91.7% ±0.4, Mean of 3 runs, range 91.3 to 92.1 |
| OCR (high) | – | 91.9% ±0.3, Mean of 3 runs, range 91.6 to 92.2 |
| Data Extraction (low) | 76.6% ±0.5, Mean of 3 runs, range 76.3 to 77.3 | 80.4% ±0.0, Mean of 3 runs, range 80.4 to 80.4 |
| Data Extraction (high) | – | 82.5% ±1.0, Mean of 3 runs, range 81.4 to 83.5 |
| Reasoning (low) | 47.7% ±2.0, Mean of 3 runs, range 45.0 to 49.0 | 72.2% ±1.7, Mean of 3 runs, range 70.9 to 74.2 |
| Reasoning (high) | – | 77.9% ±2.6, Mean of 3 runs, range 75.5 to 80.8 |
Gemma 4 26B A4B vs GPT-6 Sol: Overview
Gemma 4 26B A4B is the Mixture-of-Experts variant in Google's Gemma 4 family, with 25.2B total parameters but only 3.8B active per token. Built from the same Gemini 3 research as the 31B dense sibling and released as open weights under the Apache 2.0 license, it supports a 256K token context window with text and image input and configurable thinking mode. The "A4B" in the name refers to its approximately 4B active parameters. The MoE design makes it significantly faster at inference than the dense 31B, running nearly as fast as a 4B-parameter model while delivering roughly 97% of the dense model's quality.
For vision tasks, the 26B A4B shares the same multimodal capabilities as the 31B image understanding with variable aspect ratios and resolutions, and structured bounding box output for UI element detection. The tradeoff versus the 31B dense model is a small quality reduction in exchange for much faster inference and lower hardware requirements, fitting in 18GB of VRAM at 4-bit quantization. It ranked #6 among open models on the Arena AI text leaderboard at launch.
GPT-6 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.
The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. OpenAI reports results including 33.2% on AutomationBench at xhigh effort and 56.4% on Agents' Last Exam at max effort, while its reported DeepSWE and OSWorld 2.0 figures of 68.8% and 64.4% fall below those of the earlier GPT-5.6 Sol. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6 Sol performed better. It scores higher on all six vision tasks and averages 80.7% (#10 of 57) against 63.6% (#46 of 57) for Gemma 4 26B A4B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Counting benchmark at low effort, GPT-6 Sol leads with 74.8% against 43.2%. This is the widest gap between the two models across the benchmark's tasks.
Gemma 4 26B A4B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0019 per sample against $0.0065. Actual costs depend on your image sizes, prompts, and output length.
GPT-6 Sol is faster. Across Roboflow's Vision Evals it averaged 8.1s per inference against 27.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.