What are the strengths of this model?

Lightweight, suitable for local operation Commercial use allowed under Gemma License (conditional) Based on Google's technology Active community

What are the weaknesses of this model?

Gemma License includes non-commercial restrictions Limited Japanese capabilities Large performance gap vs frontier models No API available (self-hosted only)

What are the best use cases?

AI utilization in local environments Privacy-focused applications Model fine-tuning Research and experimental use

Google DeepMindConditional Open

Gemma 4 31B

Name: Gemma 4 31B
Author: Google DeepMind

Compare this model

The latest version of Google DeepMind's lightweight open model. With 31B parameters, it provides efficient performance, and commercial use is possible under the Gemma license (with conditions). It is practical for operation in local environments, making it suitable for use in settings with strict privacy requirements.

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Parameters

31B

Context Window

128K

License

Gemma License

Release Date

2026-04-06

Japanese Language Capability

✅High-Quality JP

Multilingual model with strong Japanese language processing capabilities.

API Pricing

API pricing for this model is not yet available

Strengths

・Lightweight, suitable for local operation
・Commercial use allowed under Gemma License (conditional)
・Based on Google's technology
・Active community

Weaknesses

・Gemma License includes non-commercial restrictions
・Limited Japanese capabilities
・Large performance gap vs frontier models
・No API available (self-hosted only)

Use Cases

・AI utilization in local environments
・Privacy-focused applications
・Model fine-tuning
・Research and experimental use

Deep Analysis

Arena Elo

1451

Overall text rank #3 among open models

GPQA Diamond

84.3%

Strong scientific reasoning

LiveCodeBench v6

80.0%

Excellent coding performance

Input Price

$0.14/1M

Via API providers, free to self-host

Context Window

256K tokens

Effective long-context performance

Parameters

31B dense

Active for every inference step

Strengths

・Outstanding reasoning and coding benchmarks for a 31B parameter model
・Apache 2.0 license allows unrestricted commercial use and fine-tuning
・Strong multimodal capabilities with native thinking/reasoning mode

Weaknesses

・Slower inference speed (6-8 tok/s locally) compared to MoE models
・No audio support (only text and image modalities)
・Requires significant VRAM (20GB+ for quantized, 34GB+ for 8-bit)

Competitor Comparison

Model	Arena	SWE	GPQA	Price
Claude 3.5 Sonnet	~1500 (estimated)	79.6%	~85% (estimated)	$3/$15 per 1M tokens
Llama 4 Maverick (400B)	N/A	N/A	N/A	Free self-host, $0 API via providers
Mistral Large 2	~1480 (estimated)	~80% (estimated)	~82% (estimated)	$2/$6 per 1M tokens

Overview

Gemma 4 31B represents Google DeepMind's flagship open-weight model released April 2, 2026, delivering frontier-level performance in a 31B dense architecture. The model achieves remarkable benchmark scores—89.2% on AIME 2026 math, 80% on LiveCodeBench v6 coding, and 84.3% on GPQA Diamond scientific reasoning—marking generational improvements over Gemma 3 (which scored 20.8% on AIME). Under Apache 2.0 licensing, it enables unrestricted commercial use, fine-tuning, and deployment without MAU restrictions. Designed for developers needing strong reasoning, coding, and multimodal capabilities, the model supports text and image inputs with configurable chain-of-thought reasoning. While its dense architecture makes it slower than Mixture-of-Experts alternatives (6-8 tok/s locally vs 50+ tok/s for Gemma 4 26B A4B MoE), it offers the highest quality ceiling within the Gemma family. The 256K context window with improved retrieval reliability (66.4% on multi-needle tests) enables practical long-document processing. The model positions itself as the premier open-weight alternative to commercial API models, particularly for teams requiring data sovereignty, custom fine-tuning, or cost-controlled self-hosting. Recent community adoption shows strong interest in coding assistance, research applications, and agentic workflows, though hardware requirements limit casual local use.

Benchmarks & Performance

### Benchmark Performance | Benchmark | Gemma 4 31B | Gemma 3 27B | Improvement | |-----------|-------------|-------------|-------------| | **MMLU Pro** | 85.2% | 67.6% | +17.6% | | **AIME 2026 (no tools)** | 89.2% | 20.8% | +68.4% | | **LiveCodeBench v6** | 80.0% | 29.1% | +50.9% | | **Codeforces ELO** | 2150 | 110 | +2040 | | **GPQA Diamond** | 84.3% | 42.4% | +41.9% | | **τ2-bench (agentic)** | 76.9% | 16.2% | +60.7% | | **MMMLU** | 88.4% | 70.7% | +17.7% | | **MMMU Pro (vision)** | 76.9% | 49.7% | +27.2% | | **Long-context (MRCR 8-needle 128K)** | 66.4% | 13.5% | +52.9% | **Arena Performance (Chatbot Arena):** - **Overall Text Elo:** 1451 - **Coding Elo:** 1498 - **Math Elo:** 1468 - **Hard Prompts (English) Elo:** 1485 - **Multi-turn Elo:** 1461 **Vision Benchmarks:** - **MATH-Vision:** 85.6% - **OmniDocBench 1.5:** 0.131 edit distance (lower is better) - **MedXPertQA MM:** 61.3% **Long-Context Performance:** - **256K context support** with 66.4% on 8-needle retrieval at 128K tokens - **Significant improvement** over Gemma 3's 128K context (13.5% → 66.4%) **Speed Benchmarks (NVIDIA DGX Spark):** - **Text generation:** 3.40 tok/s (dense) - **Thinking mode:** 3.40 tok/s - **Vision (multimodal):** 3.22 tok/s - **Function calling:** 3.44 tok/s Note: The dense 31B model is significantly slower than the 26B A4B MoE model (~53 tok/s) but offers the highest quality ceiling in the family.

Detailed Comparison

### Head-to-Head Comparisons **vs Claude 3.5 Sonnet (API Model)** - **Pricing:** Gemma 4 31B is free to self-host (~$429/month for 50K req/day); Claude costs $3/$15 per 1M tokens - **Context Window:** Both support ~200-256K tokens - **Strengths:** Gemma 4 offers data sovereignty, fine-tuning, Apache 2.0 license; Claude offers stronger complex reasoning, better instruction following - **Weaknesses:** Gemma 4 requires hardware investment; Claude has usage costs and API dependency - **Best For:** Gemma 4 for cost-sensitive, privacy-focused applications; Claude for highest quality reasoning without hardware constraints **vs Llama 4 Maverick (Open-Weight MoE)** - **Architecture:** Gemma 4 31B (dense) vs Llama 4 Maverick (400B total, 17B active MoE) - **Context Window:** Gemma 4: 256K; Llama 4 Scout: 10M tokens - **Licensing:** Both open-weight, but Llama has 700M MAU clause; Gemma 4 uses permissive Apache 2.0 - **Hardware:** Gemma 4 31B: 20-34GB VRAM; Llama 4: 24GB+ VRAM - **Best For:** Gemma 4 for balanced performance and licensing; Llama 4 for extreme context needs **vs Gemma 4 26B A4B MoE (Family Comparison)** - **Speed:** 31B: ~3.4 tok/s locally; 26B A4B: ~53 tok/s (15x faster) - **Quality:** 31B achieves 89.2% AIME vs 26B's 88.3%; 84.3% GPQA vs 82.3% - **Memory:** 31B requires ~34GB (8-bit); 26B A4B requires ~28GB - **Best For:** 31B for maximum quality, fine-tuning; 26B A4B for production speed and efficiency

Community Feedback

### Developer and Researcher Sentiment **Adoption Patterns:** - **Self-hosting community:** Strong adoption among developers wanting local, privacy-preserving AI (525K+ Hugging Face downloads in first month) - **Coding community:** Widely used for code generation, with 1498 Elo in coding-specific Arena benchmarks - **Research community:** Valued as a strong base model for fine-tuning due to dense architecture and Apache 2.0 license **Notable Reactions:** - **"Exceptional for its size":** Developers note the model punches above its weight class, matching or exceeding much larger models on specific tasks - **"Local AI milestone":** The combination of strong performance and unrestricted licensing seen as a breakthrough for on-premise AI - **"Fine-tuning favorite":** The dense 31B architecture preferred over MoE models for custom training due to more predictable gradient behavior **Community Concerns:** - **Speed limitations:** Local inference speeds (3-8 tok/s) considered too slow for real-time applications by some - **Hardware requirements:** 20-34GB VRAM limits casual local use to enthusiasts with high-end GPUs - **Missing features:** Lack of audio support and slower speeds compared to MoE family members noted as trade-offs **Usage Trends:** - **Primary use cases:** Coding assistance, document analysis, research, and privacy-sensitive applications - **Deployment:** Increasingly used in enterprise settings for data-sensitive workflows via self-hosting - **Ecosystem:** Growing support in popular frameworks (Transformers, llama.cpp, Ollama, LM Studio)

Use Cases

### Specific Use Cases and When to Choose 1. **Private Code Assistance and Fine-tuning** - **Example:** Enterprise development teams needing custom coding assistants trained on proprietary codebases - **When to choose over alternatives:** When data sovereignty is critical and teams have 24GB+ VRAM GPUs; superior to Claude/GPT for fine-tuning due to open weights; better than smaller Gemma models for code quality 2. **Research and Scientific Analysis** - **Example:** Academic researchers analyzing large datasets of scientific papers or performing multi-modal analysis of images with text - **When to choose over alternatives:** When 84.3% GPQA performance is sufficient and budget constraints favor self-hosting; better than cheaper models for scientific reasoning; preferred over API models when processing sensitive data 3. **Local Document Processing and Analysis** - **Example:** Legal or financial firms needing to process confidential documents without cloud exposure - **When to choose over alternatives:** When 256K context window is needed for long documents; better than cloud APIs for privacy; more cost-effective than commercial APIs for high-volume processing 4. **Agentic Workflows with Function Calling** - **Example:** Building autonomous AI agents that need to call external APIs and reason through multi-step processes - **When to choose over alternatives:** When 76.9% τ2-bench score is acceptable; preferred over smaller models for complex reasoning chains; better than MoE family variants when maximum reasoning quality is needed **Decision Framework:** - Choose Gemma 4 31B when: You need top-tier open-weight quality, have GPU resources, require fine-tuning, or prioritize data privacy - Avoid when: You need real-time interaction (choose 26B A4B instead), require audio input (choose E4B), or have limited hardware (choose E2B/E4B)

Latest News

### Recent Developments (as of April-May 2026) **Release and Adoption:** - **April 2, 2026:** Official release of Gemma 4 family with 4 sizes (E2B, E4B, 26B A4B, 31B) - **April 2026:** Immediate availability on Hugging Face with 525K+ downloads in first month - **April 2026:** Apache 2.0 licensing shift from previous Gemma licenses **API Availability:** - **Multiple providers:** Available via Novita, Parasail, AkashML, Venice, Together, DeepInfra, Chutes, Friendli - **Pricing:** $0.13-0.175/1M input tokens, $0.38-0.5/1M output tokens across providers - **No official Google hosted API yet** (Google AI Studio offers Gemini models but not Gemma 4) **Technical Updates:** - **April 2026:** Optimized weights released resolving "MoE Paradox" - 26B A4B now significantly outperforms 31B dense in speed - **April 2026:** llama.cpp support added for cross-platform CPU/GPU inference - **April 2026:** Ollama integration available (`ollama run gemma4:31b`) **Community Development:** - **April 3, 2026:** Benchmark repository created (lmassaron/gemma-4-benchmarks) with DGX Spark performance data - **April 2026:** Unsloth releases fine-tuning guides for Gemma 4 family - **May 2026:** Growing ecosystem of third-party tools and integrations **Notable Observations:** - **Speed trade-off confirmed:** Dense 31B runs at 3-8 tok/s locally vs 50+ tok/s for 26B A4B MoE - **Quality leadership:** Remains the highest quality model in Gemma 4 family despite speed limitations - **Enterprise adoption:** Increasing use in privacy-sensitive industries due to Apache 2.0 license

Designed for developers needing strong reasoning, coding, and multimodal capabilities, the model supports text and image inputs with configurable chain-of-thought reasoning. While its dense architecture makes it slower than Mixture-of-Experts alternatives (6-8 tok/s locally vs 50+ tok/s for Gemma 4 26B A4B MoE), it offers the highest quality ceiling within the Gemma family. The 256K context window with improved retrieval reliability (66.4% on multi-needle tests) enables practical long-document processing.

The model positions itself as the premier open-weight alternative to commercial API models, particularly for teams requiring data sovereignty, custom fine-tuning, or cost-controlled self-hosting. Recent community adoption shows strong interest in coding assistance, research applications, and agentic workflows, though hardware requirements limit casual local use.

Sources

Analysis generated: 2026-05-23