Google releases Gemma 4 31B with 256K context and configurable reasoning mode

Google Releases Gemma 4 31B Multimodal Model

Google DeepMind has released Gemma 4 31B, a 30.7-billion-parameter dense multimodal model designed for both text and image input processing. The model launched on April 2, 2026, and is available through OpenRouter at $0.14 per million input tokens and $0.40 per million output tokens.

Key Specifications

The Gemma 4 31B Instruct variant includes a 256,144-token context window—among the largest for models in its parameter class. The model supports configurable thinking/reasoning mode, enabling step-by-step reasoning for complex tasks. It includes native function calling capabilities and multilingual support across 140+ languages.

Google is releasing the model under the Apache 2.0 open license, allowing commercial and research use with minimal restrictions.

Capabilities and Performance

According to Google DeepMind, Gemma 4 31B demonstrates particular strength in three areas: coding tasks, reasoning-heavy problems, and document understanding. The configurable reasoning mode allows developers to trade latency for reasoning depth—enabling the model to show its internal thought process before producing final answers.

The multimodal architecture supports both text and image input, though the model outputs text only. This positions it as a document analysis and visual question-answering tool.

What This Means

Gemma 4 31B enters a crowded market of open 30B-class models from Meta (Llama), Mistral, and others, but differentiates on three fronts: the massive 256K context window (useful for long document processing), the explicit reasoning mode (reflecting broader industry trend toward chain-of-thought capabilities), and the Apache 2.0 license (lowest legal friction for commercial deployment).

The pricing—$0.14/$0.40 input/output—is competitive with similar-scale open models. The 256K context window is particularly notable; it enables processing of entire codebases or lengthy documents in a single request, reducing the need for context management and retrieval systems.

For organizations deploying locally or on proprietary infrastructure, the open-source weights and permissive license remove API dependency concerns. The 30.7B parameter count positions it as deployable on consumer-grade hardware (though requiring 60GB+ VRAM for full precision).