Comprehensive side-by-side LLM comparison
Gemini 3.1 Pro supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
Google DeepMind
Gemini 3.1 Pro is a multimodal language model from Google DeepMind, released in preview in February 2026 as a point-version upgrade to Gemini 3 Pro focused on improving reasoning depth, factual grounding, and coding and agentic task performance. The model accepts text, images, video, audio, and PDFs as inputs across a 1M token context window, extending the multimodal breadth of the Gemini 3 series with a companion endpoint specifically optimized for custom tool use in agentic pipelines. Google describes it as built to refine the reliability and performance of the Gemini 3 Pro series, reflecting an incremental engineering iteration rather than an architectural overhaul.
Google DeepMind
Gemini Diffusion is an experimental text and code generation model from Google DeepMind, announced at Google I/O in May 2025 as the first diffusion-based language model to achieve quality comparable to autoregressive models on standard benchmarks. Unlike transformer-based models that predict tokens sequentially left-to-right, it generates entire blocks of text by iteratively refining noise — the paradigm used in image and video generation models — enabling faster sampling speeds and stronger mid-generation error correction for code and mathematical editing tasks. At announcement it was available only as an experimental demo via waitlist, with no public API, marking it as a research milestone rather than a production deployment.
9 months newer

Gemini Diffusion
Google DeepMind
2025-05-20

Gemini 3.1 Pro
Google DeepMind
2026-02-19
Context window and performance specifications
Gemini 3.1 Pro
2025-01
Available providers and their performance metrics
Gemini 3.1 Pro
Gemini Diffusion
Gemini 3.1 Pro
Gemini Diffusion
Gemini 3.1 Pro
Gemini Diffusion