{"id":129254,"date":"2025-03-12T21:30:04","date_gmt":"2025-03-12T21:30:04","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/is-googles-new-27b-model-better\/"},"modified":"2025-03-12T21:30:04","modified_gmt":"2025-03-12T21:30:04","slug":"is-googles-new-27b-model-better","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=129254","title":{"rendered":"Is Google’s New 27B Model Better?"},"content":{"rendered":"


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Google has just launched its latest state-of-the-art lightweight language model, Gemma 3. The model looks promising, outperforming Meta\u2019s Llama 3<\/a>, DeepSeek-V3<\/a>, and OpenAI\u2019s o3-mini<\/a> in standard benchmark tests. While Google claims that it\u2019s the \u201cworld\u2019s best single-accelerator model,\u201d let\u2019s see how well it actually performs against other popular models. In this Gemma 3 27B vs DeepSeek-R1 comparison we will look into the features, benchmarks, and performance of the new model and compare them with those of China\u2019s renowned DeepSeek-R1<\/a>.<\/p>\n

What is Gemma 3?<\/h2>\n

\u200bGemma 3 is Google\u2019s latest open-source AI model series, designed for seamless deployment across various devices, from handheld devices to enterprise-level workstations. Gemma 3 introduces multimodal capabilities, powered by PaliGemma 2, enabling it to process textual and visual content. It can also take in audio files and entire folders as contextual data input.<\/p>\n

While large models like Grok 3 utilizes the power of over 100,000 NVIDIA H100 GPUs, and DeepSeek-R1 uses 32 GPUs, Gemma 3 is estimated to work on just a single one. Despite that and its small size of just 27B parameters, it has shown to outperform much larger models like DeepSeek-V3, OpenAI\u2019s o3-mini, Llama3-405B, and Mistral Large.<\/p>\n

Key Features of Gemma 3<\/h3>\n

Here are some of the key features of Google\u2019s latest Gemma 3 model:<\/p>\n

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  1. Multiple Variations: <\/strong>Gemma 3 is available in various sizes \u2013 1B, 4B, 12B, and 27B \u2013 making it efficient and cost-effective for diverse use cases.<\/li>\n
  2. Small Size: <\/strong>The largest variant, Gemma 3 27B, is designed to deliver high performance while maintaining efficiency, owing to its 27B parameter size.<\/li>\n
  3. Single Accelerator Compatibility<\/strong>: The model is optimized to run on a single GPU or TPU, and is compatible with Nvidia GPUs as well. This makes it accessible for devices from smartphones to workstations.<\/li>\n
  4. Multimodality<\/strong>: Gemma 3 can analyze text, images, short videos, and audio files enabling applications such as visual question answering and image-based storytelling.<\/li>\n
  5. Google Integration: <\/strong>Since it\u2019s developed by Google, Gemma 3 lets users upload files directly from Google Drive.<\/li>\n
  6. Multilingual<\/strong>: Pre-trained in over 35 languages, with support for more than 140 languages, Gemma 3 facilitates tasks like translation and optical character recognition (OCR).<\/li>\n
  7. Large Context Window<\/strong>: It supports 32k tokens in the 1B model and up to 128k tokens in larger models, as opposed to just 8k tokens in Gemma 2.<\/li>\n
  8. ShieldGemma 2<\/strong>: An image safety classifier that filters explicit, dangerous, or violent content, enhancing the safety of generated outputs.<\/li>\n<\/ol>\n

    How to Access Gemma 3<\/h3>\n
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    Gemma 3 is available for use on Google AI Studio. Here\u2019s how you can access it:<\/p>\n

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    1. Open Google AI Studio<\/strong>\n

      Open Google AI Studio by clicking here<\/a>.
      \"Gemma<\/p>\n<\/li>\n

    2. Login or Sign Up<\/strong>\n

      Sign in using your Gmail credentials. Sign up for an account if you don\u2019t have one already.
      \"Gemma<\/p>\n<\/li>\n

    3. Select Gemma 3 27B<\/strong>\n

      Once signed in, go to the model selection dropdown list and scroll all the way down to find Gemma 3 27B. Simply select the model and start chatting with it.
      \"How<\/p>\n<\/li>\n<\/ol>\n<\/div>\n

      Alternatively, you can access Gemma 3 directly on its Hugging Face space<\/a>. You may also use it for building models on Keras, JAX, and Ollama.<\/p>\n

      Gemma 3 vs DeepSeek-R1: Features Comparison<\/h2>\n

      Now let\u2019s begin with the Gemma 3 vs DeepSeek-R1 comparisons. We\u2019ll first have a look at their features and see what each model has to offer.<\/p>\n

      \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
      Feature<\/strong><\/td>\nGemma 3<\/strong><\/td>\nDeepSeek-R1<\/strong><\/td>\n<\/tr>\n
      Model Sizes<\/strong><\/td>\n1B, 4B, 12B, 27B parameters<\/td>\n671B total (37B active per query)<\/td>\n<\/tr>\n
      Context Window<\/strong><\/td>\nUp to 128K tokens in 27B model, 32K in 1B model<\/td>\nUp to 128K tokens<\/td>\n<\/tr>\n
      GPU Needs<\/strong><\/td>\nRuns on single GPU\/TPU<\/td>\nNeeds high-end GPUs (H800\/H100)<\/td>\n<\/tr>\n
      Image Generation<\/strong><\/td>\n\u274c No<\/td>\n\u274c No<\/td>\n<\/tr>\n
      Image Analysis<\/strong><\/td>\n\u2705 Yes (via SigLIP)<\/td>\n\u274c No<\/td>\n<\/tr>\n
      Video Analysis<\/strong><\/td>\n\u2705 Yes (short clips)<\/td>\n\u274c No<\/td>\n<\/tr>\n
      Multimodality<\/strong><\/td>\n\u2705 Text, images, videos<\/td>\n\u274c Mainly text-based; can do text-extraction from images<\/td>\n<\/tr>\n
      File Uploads<\/strong><\/td>\n\u2705 Text, images, videos<\/td>\n\u274c Mostly text input<\/td>\n<\/tr>\n
      Web Search<\/strong><\/td>\n\u274c No<\/td>\n\u2705 Yes<\/td>\n<\/tr>\n
      Languages<\/strong><\/td>\n35+ supported, trained in 140+<\/td>\nBest for English & Chinese<\/td>\n<\/tr>\n
      Safety<\/strong><\/td>\n\u2705 Strong safety by ShieldGemma 2<\/td>\n\u274c Weaker safety, jailbreak risks<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

      Also Read: QwQ-32B vs DeepSeek-R1: Can a 32B Model Challenge a 671B Parameter Model?<\/a><\/em><\/p>\n

      Gemma 3 vs DeepSeek-R1: Performance Comparison<\/h2>\n

      Now that we know what Gemma 3 and DeepSeek-R1 are capable of doing, let\u2019s test out some of their common features and compare their performance. For this comparison, we\u2019ll be testing the models\u2019 performance on the following three tasks:<\/p>\n

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      1. Coding:<\/strong> creating an animation<\/li>\n
      2. Logical Reasoning:<\/strong> solving a puzzle<\/li>\n
      3. STEM Problem-solving: <\/strong>solving a Physics problem<\/li>\n<\/ol>\n

        For each task, we\u2019ll try out the same prompt on both the models and evaluate their responses based on the speed of generation and quality of the output.<\/p>\n

        If you wish to join me and try out some prompts for the comparison yourself, you can access DeepSeek-R1 by enabling the \u2018DeepThink\u2019 feature on the chat interface<\/a>.<\/p>\n

        Task 1: Coding<\/h3>\n

        Let\u2019s start off by testing the coding capabilities of both the models. For this task, I am going to ask Gemma 3 and DeepSeek-R1 to write a Python code for a physics-based animation. We\u2019ll run the code generated by both the models on Google Colab and compare their outputs.<\/p>\n

        Prompt:<\/strong> \u201dWrite a python program that shows a ball bouncing inside a spinning pentagon, following the laws of Physics, increasing its speed every time it bounces off an edge.\u201d<\/em><\/p>\n

        Output by Gemma 3\u2019s Code<\/h4>\n
        \"Gemma<\/figure>\n

        Output by DeepSeek-R1\u2019s Code<\/h4>\n

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