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Gemma 4 31B

Gemma 4 31B is Google DeepMind’s largest Gemma 4 open-weight dense multimodal model, featuring around 31 billion parameters and strong performance on text and image understanding tasks.

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What is Gemma 4 31B?

Gemma 4 31B is a 31-billion-parameter dense multimodal large language model from Google DeepMind that processes text and images with text outputs. It is primarily used for advanced assistant-style chat, coding help, and analytical reasoning tasks that benefit from long-context understanding. It is also applied to multimodal use cases such as image-grounded question answering and document understanding where both text and images must be interpreted together. It belongs to the Gemma 4 family of open models, which span multiple sizes from edge-oriented variants to this largest 31B configuration.

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).

Provider Input Output Cache read /M Latency Throughput Uptime
Google ~$0.35 per 1M tokens ~$0.70 per 1M tokens — ~220ms ~80 tps 100.00%
Vertex AI (Google Cloud) ~$0.38 per 1M tokens ~$0.76 per 1M tokens — ~260ms ~60 tps ~99.9%
AWS Bedrock (3rd‑party Gemma‑equivalent) ~$0.40 per 1M tokens ~$0.80 per 1M tokens — ~250ms ~70 tps ~99.9%
Anthropic (Claude Sonnet‑class alternative) ~$0.50 per 1M tokens ~$1.00 per 1M tokens — ~230ms ~75 tps ~99.9%

Try this model

Test Gemma 4 31B right here — free to start.

Gemma 4 31B
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Code snippet

Call the model through the OpenAI-compatible API.

python 
                                        from openai import OpenAI
                                            
                                            client = OpenAI(
                                                api_key="YOUR_API_KEY",
                                                base_url="https://inference.example.com/v1"
                                            )
                                            
                                            response = client.chat.completions.create(
                                                model="google/gemma-4-31b",
                                                messages=[
                                                    {
                                                        "role": "user",
                                                        "content": "Describe this image in one sentence."
                                                    }
                                                ],
                                            )
                                            
                                            print(response.to_json())
                                        
                                    
                                        {
                                                "model": "google/gemma-4-31b",
                                                "messages": [
                                                    {
                                                        "role": "user",
                                                        "content": "Describe this image in one sentence."
                                                    }
                                                ]
                                            }

Uptime

30-Day Uptime
100.00%
Past Incidents (30d)
0
Error rate (24h)
0.00%

Last 30 days

30/30 days operational | 100.00% uptime

30 days ago Today
Operational Degraded Outage Maintenance
See All Incidents

5 Core Capabilities

  • Advanced Reasoning

    Performs complex, step-by-step reasoning for difficult tasks, benefiting from an explicit thinking mode in instruction-tuned variants.

  • Multimodal Understanding

    Processes text and images together, supporting tasks like document parsing, UI comprehension, charts, and general visual understanding.

  • Conversational Chat

    Acts as a strong conversational assistant, following instructions, maintaining context, and supporting agentic workflows and tool use.

  • Code Generation

    Generates, completes, and debugs source code in multiple languages, suitable for software development and technical scripting tasks.

  • Multilingual Text

    Handles multilingual input and output across many languages, enabling translation-style tasks and cross-lingual reasoning over long context.

6 Most Valuable Use Cases

  • Customer Support Chatbots
  • Invoice Data Extraction
  • Legal Document Review
  • Compliance Case Monitoring
  • E-commerce Product Assistants
  • Code Generation Assistance

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Intelligent Model Routing

    Automatically route each request to the optimal model across providers based on latency, cost, and capability — no client changes required.

    One endpoint, every model

  • Cost-Aware Orchestration

    Optimize spend with dynamic model selection, rate limiting, and usage controls that keep your AI bill predictable while preserving performance.

    Lower cost, same quality

  • Resilient Fallback Logic

    Define cross-provider failover rules so requests automatically retry on backup models when a provider is down, slow, or throttling.

    No single point of failure

  • End-to-End Observability

    Get unified logs, metrics, traces, and payload sampling across all providers to debug failures, tune prompts, and monitor performance in one place.

    See every token, everywhere

  • Task-Level Abstractions

    Call high-level tasks like chat, RAG, tools, or agents without wiring each provider’s primitives yourself, so you ship features instead of glue code.

    APIs speak in tasks

  • High-Throughput Batch Jobs

    Run large-scale inference workloads with parallel execution, retries, and progress tracking built in, without manually managing queues or worker pools.

    Scale from 10 to 10M

When to Use — When NOT to Use

Use it if...

  • You need a strong open-weight LLM that can be self-hosted on your infrastructure.
  • You need high-quality English and multilingual text generation for chatbots or virtual assistants.
  • Your use case involves fine-tuning or LoRA adapters on a powerful 30B-class backbone.
  • Your use case involves moderate-length coding help, code explanations, and boilerplate generation.
  • You need a balance of reasoning quality and cost compared with much larger proprietary models.
  • Your use case involves RAG over medium documents where ultra-long context is unnecessary.
  • You need an open model compatible with common inference stacks like vLLM or Ollama.

Avoid if...

  • You need cutting-edge reasoning and tool use rivaling the very best flagship proprietary models.
  • Your workload requires extremely long-context processing, such as full-book analysis or codebases.
  • You need highly optimized edge or mobile deployment where a 31B model is impractical.
  • You need top-tier, production-grade code synthesis for complex multi-file or large refactor tasks.
  • Your workload requires guaranteed low-latency responses on modest GPUs or CPU-only environments.
  • You need native, fully managed hosting with tight integration into non-Google cloud ecosystems.
  • Your workload requires robust, battle-tested safety layers and policy enforcement out-of-the-box.

Frequently Asked Questions

  • What is Gemma 4 31B?

    Gemma 4 31B is a 31-billion-parameter Google language model focused on strong reasoning, coding, and instruction-following capabilities via the LLM.API gateway.

  • What is the context window of Gemma 4 31B?

    Gemma 4 31B supports a 32K token context window, allowing relatively long conversations and documents before older tokens are pushed out.

  • What is Gemma 4 31B best suited for?

    Gemma 4 31B is best for complex reasoning, multi-step agents, advanced coding assistance, and high-quality English writing where accuracy matters.

  • Does Gemma 4 31B support images or other modalities?

    Gemma 4 31B is a text-only model on LLM.API, supporting text inputs and outputs but not images, audio, or video.

  • How fast is Gemma 4 31B when called through LLM.API?

    On LLM.API, Gemma 4 31B typically returns first tokens within a few hundred milliseconds and then streams tokens at an interactive rate.

  • How is Gemma 4 31B priced on LLM.API?

    Gemma 4 31B pricing on LLM.API is usage-based per input and output token; check the LLM.API pricing page for up-to-date rates.

  • How do I call Gemma 4 31B via LLM.API?

    You select the Gemma 4 31B model name in your LLM.API request and authenticate with your LLM.API key, without needing direct Google Cloud setup.

  • How does Gemma 4 31B compare to smaller Gemma variants?

    Compared to smaller Gemma models, Gemma 4 31B generally offers better reasoning quality and coding ability at the cost of higher latency and price.

  • What are the main limitations of Gemma 4 31B?

    Gemma 4 31B can hallucinate facts, lacks real-time web access, may underperform on niche domains, and is restricted to its context window.

  • Can I use Gemma 4 31B for structured outputs like JSON?

    Yes, Gemma 4 31B can reliably follow JSON or schema-like formats when prompted clearly and validated by your application logic.

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