Gemma 4 26B A4B vs Mistral Small 3.1 24B
Gemma 4 26B A4B: 54.1% average across four Vision Evals tasks (#56 of 69 models). We have not run Mistral Small 3.1 24B through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
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Gemma 4 26B A4B vs Mistral Small 3.1 24B Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Gemma 4 26B A4B | Mistral Small 3.1 24B |
|---|---|---|
| Organization | Mistral | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Mar 2025 |
| Context Window | 256K | 128K |
| Parameters | 25.2B | 24B |
| 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 | $0.351 |
| Output $/1M | $0.300 | $0.555 |
| Vision Tasks | ||
| Classification | Demo | Supported |
| Object Detection | Demo | Not listed |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks | ||
| Overall | 54.1%* | Not evaluated |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0019 | – |
| Avg speed / sample | 27.84s | – |
| By task | ||
| Object Detection | 44.2% ±0.7, Mean of 3 runs, range 43.5 to 44.8 | – |
| Counting | 43.2% ±2.0, Mean of 3 runs, range 41.9 to 46.0 | – |
| Identification | 81.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 | – |
| OCR | – | – |
| by category | ||
| Reasoning | 47.7% ±2.0, Mean of 3 runs, range 45.0 to 49.0 | – |
Gemma 4 26B A4B vs Mistral Small 3.1 24B: 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.
Mistral Small 3.1 24B, released on March 17, 2025, is an open-weight multimodal model from Mistral AI, distributed under the Apache-2.0 license. With around 24B parameters and a 128K token context window, it is available in both base and instruction-tuned (“Instruct”) variants. The model introduces vision support alongside text, enabling tasks like multimodal reasoning, captioning, and image-based Q&A.
It is multilingual, supporting many languages, and is optimized for fast responses, function calling, structured dialogue, and long-context reasoning. Despite its size, the model can be run locally in quantized formats, fitting on machines with ~32GB RAM, making it accessible to developers outside large cloud setups. However, the output length is smaller than the 128K input window, meaning long generations may require chaining. In addition, using full vision features or the maximum context window significantly increases compute costs, and performance on highly complex reasoning or enterprise-scale tasks still trails larger proprietary frontier models.