Gemma-4-31B: Complete Specifications, Pricing, API Access & Use Cases (2026)

Gemma-4-31B is a dense, open-weight model from Google designed for coding, mathematical reasoning, long-context analysis, multimodal understanding, and tool-based workflows. Its combination of downloadable weights and hosted API availability makes it relevant to teams comparing private deployment with managed inference. The specifications and pricing below reflect the available Google and Gate.AI information reviewed for 2026.

What is Gemma-4-31B?

Gemma-4-31B is the 31-billion-parameter dense model in Google’s Gemma 4 family. Unlike a mixture-of-experts model that activates only part of its total parameter count for each token, a dense model uses its full network during inference. This can make compute requirements easier to predict, although running a 31B model locally still requires substantial memory.

The model is positioned for coding, mathematics, document analysis, visual understanding, structured generation, and agentic applications. Native function calling allows applications to connect the model to approved tools, databases, search systems, or internal services.

As per the Gate.AI listing, the hosted model ID is google/gemma-4-31b , with a listing date of April 2, 2026. The open-weight format also allows eligible teams to deploy the model through their preferred inference framework, subject to Google’s applicable Gemma license terms.

What Are Gemma-4-31B’s Key Specifications and Pricing?

The 256K and 262K context values ​​​​should remain separate. Google’s model documentation and the Gate.AI model-card describe different access environments, so developers should follow the limit enforced by the platform they use.

A request containing 800,000 input tokens and producing 200,000 output tokens would have the following estimated Gate.AI cost:

( 0.8 × $0.12) + (0.2 × $0.37) = $0.17

This is a calculated estimate based on listed token rates. Retries, application prompts, tool results, and account-specific terms may affect the final cost.

What Can Gemma-4-31B Do That Makes It Useful in Production?

Gemma-4-31B can support coding assistants that explain repositories, review functions, generate implementation drafts, identify likely bugs, and convert written requirements into structured development tasks. Its long context may help applications process larger code or documentation collections, although retrieval and careful prompt construction remain useful for maintaining relevance.

Native function calling makes the model suitable for controlled agent workflows. An application could ask it to choose from predefined functions for searching internal documentation, querying a database, checking an issue tracker, or preparing a code change. The application—not the model—should enforce permissions and execute each tool.

Open weights provide greater control over hosting, quantization, logging, fine-tuning, and data residency. This may benefit organizations working with private repositories or restricted documents. However, self-hosting also transfers responsibility for infrastructure, scaling, security, monitoring, model updates, and output safeguards to the operator.

What Are Gemma-4-31B’s Supported Modalities?

Gemma-4-31B can interpret text and images but produces text rather than generated images, audio, or video. Multimodal input therefore supports understanding tasks, not media creation.

Where Does Gemma-4-31B Fall Short?

Local deployment requires more memory and compute than smaller Gemma variants. Quantization can reduce hardware requirements, but the resulting quality, speed, and framework compatibility depend on the selected format and serving stack.

The model can also produce incorrect reasoning, insecure code, unsupported claims, or invalid tool arguments. Production systems should validate structured outputs, limit tool permissions, isolate code execution, log actions, and require human approval for consequential changes.

Its listed API price should not be compared with local inference using token cost alone. Self-hosting introduces hardware, electricity, engineering, monitoring, maintenance, and utilization costs.

Finally, some operational fields—such as maximum output length, rate limits, cache pricing, and model-specific fine-tuning support through Gate.AI—are not confirmed by the supplied listing.

What Is Gemma-4-31B Best Used For?

Gemma-4-31B is well suited to private coding assistants, repository analysis, long-document review, mathematical problem solving, structured extraction, multimodal document interpretation, and self-hosted agents connected to internal tools.

Choose it when open weights, local deployment, coding capability, long context, or infrastructure control are central requirements.

Consider a smaller model when memory efficiency and high-throughput routine tasks matter more. A managed proprietary model may be preferable when the team wants reduced infrastructure responsibility, while a dedicated image, audio, or video model is necessary for media generation.

How Does Gemma-4-31B Compare to Gemma-4-26B-A4B and Llama 3.1 70B?

Gemma-4-26B-A4B may fit teams prioritizing lower active compute, while Gemma-4-31B offers conventional dense execution. Llama 3.1 70B specifications are relevant for teams considering a larger open model. DeepSeek V3 offers another coding-oriented comparison, while Gemini 2.5 Flash may suit managed multimodal workloads.

How Do I Access Gemma-4-31B Through Gate.AI?

As per the Gate.AI model-card, use the model ID google/gemma-4-31b . The following OpenAI-compatible examples use the Gate.AI base URL and should be checked against current documentation before production deployment.

Python

Python import os
import OpenAI from openai
client = OpenAI(
    api_key=os.environ["GATEAI_API_KEY"],
    base_url="https://api.gate.ai/openai/v1",
)
response = client.chat.completions.create(
    model="google/gemma-4-31b",
    messages=[
        {
            "role": "user",
            "content": "Explain binary search and provide a Python example."
        }
    ],
)
print(response.choices[0].message.content)

curl

Bash curl https://api.gate.ai/openai/v1/chat/completions \
  -H "Authorization: Bearer $GATEAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemma-4-31b",
    "messages": [
      {
        "role": "user",
        "content": "Explain binary search and provide a Python example."
      }
    ]
  }'

For function-calling applications, confirm the current tool schema, supported parameters, and response structure before implementation.

FAQs

Is Gemma-4-31B open source?

It is more accurately described as open-weight. The weights are available under Google’s Gemma terms, which developers should review before deployment or redistribution.

Can Gemma-4-31B run locally?

Yes, although a dense 31B model requires significant memory. Quantization may make it practical on suitable workstations or servers.

Does it support images?

Yes. It can accept image input for understanding tasks and return text. It is not an image-generation model.

What does Gemma-4-31B cost through Gate.AI?

As per the Gate.AI listing, input costs $0.12 per million tokens and output costs $0.37 per million tokens. Cache pricing is not listed.

Is it suitable for autonomous coding agents?

It can support coding agents through reasoning, code generation, long context, and function calling. Production systems still need sandboxing, validation, permission controls, and human review.