Gemma 4 26B A4B vs Qwen3.8 27B
Qwen3.8 27B and Gemma 4 26B A4B score 69.5% vs 54.1% averaged across four Vision Evals tasks (#28 and #56 of 69 models), so Qwen3.8 27B leads overall.
- Qwen3.8 27B wins all 4 tasks, with its widest lead on counting (64.9% vs 43.2%).
- Qwen3.8 27B is about half the cost: $0.0009 vs $0.0019 overall average cost per sample.
- Qwen3.8 27B is faster: 18.0s vs 27.8s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemma 4 26B A4B vs Qwen3.8 27B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemma 4 26B A4B vs Qwen3.8 27B Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Gemma 4 26B A4B | Qwen3.8 27B |
|---|---|---|
| Organization | Qwen | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Aug 2026 |
| Context Window | 256K | 262K |
| Parameters | 25.2B | 27.78B |
| License | Apache 2.0 | Apache 2.0 |
| Commercial use | free for commercial use | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.090 | $0.150 |
| Output $/1M | $0.300 | $1.88 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 54.1%* | 69.5%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0019 | $0.0009 |
| Avg speed / sample | 27.84s | 17.99s |
| By task | ||
| Object Detection (low) | 44.2% ±0.7, Mean of 3 runs, range 43.5 to 44.8 | 65.7% ±1.0, Mean of 3 runs, range 64.6 to 66.5 |
| Object Detection (high) | – | 66.1% ±1.4, Mean of 3 runs, range 64.9 to 67.8 |
| Counting (low) | 43.2% ±2.0, Mean of 3 runs, range 41.9 to 46.0 | 64.9% ±4.1, Mean of 3 runs, range 60.8 to 68.9 |
| Counting (high) | – | 68.0% ±2.0, Mean of 3 runs, range 66.2 to 70.3 |
| Identification (low) | 81.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 | 85.4% ±4.7, Mean of 3 runs, range 81.3 to 90.6 |
| Identification (high) | – | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR | – | – |
| by category | ||
| Reasoning (low) | 47.7% ±2.0, Mean of 3 runs, range 45.0 to 49.0 | 62.0% ±2.0, Mean of 3 runs, range 60.3 to 64.2 |
| Reasoning (high) | – | 66.0% ±0.7, Mean of 3 runs, range 65.6 to 66.9 |
Gemma 4 26B A4B vs Qwen3.8 27B: 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.
Qwen3.8-27B is a dense vision-language model of roughly 27.78 billion parameters from Alibaba's Qwen team, built on the architectural foundation established by the Qwen3.5 generation. It takes interleaved text, image, and video input through an integrated vision encoder and produces text, covering document and chart understanding, visual question answering, front-end code generation from visual references, and agentic computer-use workflows. The decoder is a hybrid stack of 64 layers that repeats a pattern of three Gated DeltaNet linear-attention blocks, each followed by a feed-forward block, then one grouped-query full-attention block, so only a quarter of the layers maintain a conventional key-value cache. Native context length is 262,144 tokens.
Post-training targets long-horizon agentic work, and the model exposes per-request thinking control that lets callers switch reasoning traces on or off and choose a reasoning effort level. Multi-token prediction weights are trained alongside the main network for speculative decoding.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.8 27B performed better. It scores higher on all 4 vision tasks and averages 69.5% (#28 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 Counting benchmark at low effort, Qwen3.8 27B leads with 64.9% against 43.2%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.8 27B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0009 per sample against $0.0019. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.8 27B is faster. Across Roboflow's Vision Evals it averaged 18.0s 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.