Gemma 4 26B A4B vs GPT-6.1 Sol
GPT-6.1 Sol averages 81.7% across five Vision Evals tasks and Gemma 4 26B A4B 54.1% across four (#9 and #56 of 69 models), so GPT-6.1 Sol leads overall.
- GPT-6.1 Sol wins all 4 tasks both ran, with its widest lead on object detection (80.8% vs 44.2%).
- Gemma 4 26B A4B is about a quarter of the cost: $0.0019 vs $0.0068 overall average cost per sample.
- GPT-6.1 Sol is faster: 12.8s vs 27.8s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemma 4 26B A4B vs GPT-6.1 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemma 4 26B A4B vs GPT-6.1 Sol Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Gemma 4 26B A4B | GPT-6.1 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 |
| Commercial use | free for commercial use | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.090 | $2.00 |
| Output $/1M | $0.300 | $10.00 |
| Vision Tasks | ||
| Promptable Concept Segmentation | Not listed | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 54.1%* | 81.7% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0019 | $0.0068 |
| Avg speed / sample | 27.84s | 12.82s |
| By task | ||
| Object Detection (low) | 44.2% ±0.7, Mean of 3 runs, range 43.5 to 44.8 | 80.8% ±0.1, Mean of 3 runs, range 80.7 to 80.9 |
| Object Detection (high) | – | 81.6% ±0.4, Mean of 3 runs, range 81.1 to 82.0 |
| Counting (low) | 43.2% ±2.0, Mean of 3 runs, range 41.9 to 46.0 | 78.8% ±3.4, Mean of 3 runs, range 75.7 to 82.4 |
| Counting (high) | – | 80.2% ±3.4, Mean of 3 runs, range 77.0 to 83.8 |
| Identification (low) | 81.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | – | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | – | 75.8% |
| by category |
| |
| OCR (high) | – | 75.8% |
| by category |
| |
| Reasoning (low) | 47.7% ±2.0, Mean of 3 runs, range 45.0 to 49.0 | 83.7% ±1.3, Mean of 3 runs, range 82.1 to 84.8 |
| Reasoning (high) | – | 88.7% ±2.0, Mean of 3 runs, range 87.4 to 91.4 |
Gemma 4 26B A4B vs GPT-6.1 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-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.
OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6.1 Sol performed better. It scores higher on all 4 vision tasks and averages 81.7% (#9 of 69) against 54.1% (#56 of 69) 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 Object Detection benchmark at low effort, GPT-6.1 Sol leads with 80.8% against 44.2%. 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 $0.0068. Actual costs depend on your image sizes, prompts, and output length.
GPT-6.1 Sol is faster. Across Roboflow's Vision Evals it averaged 12.8s per inference against 27.8s. 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 image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.