Gemma 4 31B
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GoogleGoogleGemmaReleased April 2, 2026

Gemma 4 31B

262K context$0.130/M input$0.380/M output30.7B

Gemma 4 31B Instruct is a dense multimodal model developed by Google DeepMind with 30.7 billion parameters. It supports both text and image inputs and provides text outputs, featuring a 256K token context window, configurable reasoning modes, native function calling, and multilingual support across 140+ languages. The model excels in coding, reasoning, and document understanding tasks.

What is Gemma 4 31B?

Gemma 4 31B is an AI model from Google that Agent Mag tracks for pricing, context window, modalities, benchmarks, and API compatibility. Builders can use this page to compare Gemma 4 31B against other models for agent workflows and production deployments.

Model ID

Gemma 4 31B Instruct is a dense multimodal model developed by Google DeepMind with 30.7 billion parameters. It supports both text and image inputs and provides text outputs, featuring a 256K token context window, configurable reasoning modes, native function calling, and multilingual support across 140+ languages. The model excels in coding, reasoning, and document understanding tasks.

Architecture & Specifications
Architecture
Dense Transformer
Parameters
30.7B
Tokenizer
Gemma
License
Apache 2.0
Released
April 2, 2026
Modalities
Input
imagetextvideo
Output
text
Supported Parameters
frequency_penaltyinclude_reasoninglogit_biaslogprobsmax_tokensmin_ppresence_penaltyreasoningrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
Strengths
  • Supports text and image inputs with text outputs
  • 256K token context window
  • Multilingual support across 140+ languages
  • Excels in coding, reasoning, and document understanding tasks
  • Configurable reasoning modes and native function calling
Limitations
  • Low performance on research-level physics reasoning (CritPt: 1.4%)
  • Moderate hallucination rate in knowledge tasks (18.4%)
  • Limited accuracy in economically valuable tasks (GDPval-AA: 30.9%)
  • Relatively low omniscience accuracy (19.9%)
  • Performance varies significantly across benchmarks
Recommended Use Cases
Coding and software development
Document understanding and summarization
Multilingual communication and translation
Scientific reasoning and analysis
Legal and financial document processing

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Data enriched Apr 24, 2026. Pricing from OpenRouter API.