Gemma 4 26B A4B vs Qwen3.6 35B A3B
Qwen3.6 35B A3B and Gemma 4 26B A4B score 64.8% vs 54.1% averaged across four Vision Evals tasks (#30 and #52 of 65 models), so Qwen3.6 35B A3B leads overall.
- Qwen3.6 35B A3B wins all 4 tasks, with its widest lead on counting (65.3% vs 43.2%).
- Qwen3.6 35B A3B is about half the cost: $0.0012 vs $0.0019 overall average cost per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemma 4 26B A4B vs Qwen3.6 35B A3B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemma 4 26B A4B vs Qwen3.6 35B A3B Comparison Table
Evals updated October 26, 2026Pricing updated November 8, 2026
| Property | Gemma 4 26B A4B | Qwen3.6 35B A3B |
|---|---|---|
| Organization | Qwen | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Apr 2026 |
| Context Window | 256K | 262K |
| Parameters | 25.2B | 35B total, 3B active |
| License | Apache 2.0 | Apache 2.0 |
| Commercial use | free for commercial use | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.068 | $0.100 |
| Output $/1M | $0.225 | $0.900 |
| Vision Tasks | ||
| Phrase Grounding | Not listed | Supported |
| Video Classification | Not listed | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks | ||
| Overall | 54.1%* | 64.8%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0019 | $0.0012 |
| Avg speed / sample | 27.84s | 27.10s |
| By task | ||
| Object Detection | 44.2% ±0.7, Mean of 3 runs, range 43.5 to 44.8 | 57.0% ±1.3, Mean of 3 runs, range 56.1 to 58.7 |
| Counting | 43.2% ±2.0, Mean of 3 runs, range 41.9 to 46.0 | 65.3% ±2.7, Mean of 3 runs, range 62.2 to 67.6 |
| Identification | 81.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 | 82.3% ±6.3, Mean of 3 runs, range 75.0 to 87.5 |
| OCR | – | – |
| by category | ||
| Reasoning | 47.7% ±2.0, Mean of 3 runs, range 45.0 to 49.0 | 54.8% ±1.3, Mean of 3 runs, range 53.0 to 55.6 |
Gemma 4 26B A4B vs Qwen3.6 35B A3B: 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.
Qwen3.6-35B-A3B is a sparse Mixture-of-Experts (MoE) multimodal language model developed by the Qwen team at Alibaba Group. It carries 35 billion total parameters but activates only approximately 3 billion per forward pass via a learned routing mechanism, giving it the representational capacity of a large dense model at a fraction of the inference compute. The model is natively multimodal, processing images, documents, and video alongside text as a core architectural capability rather than an add-on. It supports a native context window of 262,144 tokens, extensible up to 1,010,000 tokens via YaRN. A key design feature is the unified thinking/non-thinking mode framework: users can switch between deliberate chain-of-thought reasoning and fast direct responses within a single model, and a "thinking preservation" option retains reasoning context across multi-turn agentic workflows to reduce redundant computation.
The model is specifically optimized for agentic coding tasks, including repository-level reasoning, frontend workflow generation, multi-step tool use, and MCP (Model Context Protocol) integration. For vision-language tasks it achieves 92.0 on RefCOCO, 89.9 on OmniDocBench 1.5, and 83.7 on VideoMMMU. The model also supports Multi-Token Prediction (MTP) for speculative decoding. All Qwen3.6 open-weight models are released under the Apache 2.0 license.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.6 35B A3B performed better. It scores higher on all 4 vision tasks and averages 64.8% (#30 of 65) against 54.1% (#52 of 65) 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, Qwen3.6 35B A3B leads with 65.3% against 43.2%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.6 35B A3B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0012 per sample against $0.0019. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.6 35B A3B is faster. Across Roboflow's Vision Evals it averaged 27.1s per inference against 27.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
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.