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.
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).
List price by provider ($ / 1M tokens)
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Input | Output | Cache read /M | Latency | Throughput | Uptime |
|---|---|---|---|---|---|---|
| in $140; out $400 per 1M tokens | 262K tokens | vision, tools, streaming, reasoning, JSON, structured |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.
Try this model
Test Gemma 4 31B right here — free to start.
Suggestions for your first prompt
Code snippet
Call the model through the OpenAI-compatible API.
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
Last 30 days
30/30 days operational | 100.00% uptime
5 Core Capabilities
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Advanced Reasoning
Performs complex, step-by-step reasoning for difficult tasks, benefiting from an explicit thinking mode in instruction-tuned variants.
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Multimodal Understanding
Processes text and images together, supporting tasks like document parsing, UI comprehension, charts, and general visual understanding.
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Conversational Chat
Acts as a strong conversational assistant, following instructions, maintaining context, and supporting agentic workflows and tool use.
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Code Generation
Generates, completes, and debugs source code in multiple languages, suitable for software development and technical scripting tasks.
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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.
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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
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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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-
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-
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