Gemma 3 4B — Google | Modeldex

Gemma 3 4B

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Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities,...

Updated 4 days agoStructured data from Modeldex catalog

VisionBudget

Knowledge cutoff

Aug 31, 2024(1.6 years ago)

API release

Mar 13, 2025(1.1 years ago)

Not enough benchmark coverage yet for an Intelligence Index — needs at least 3 results across 2 categories.

Overview

History

Gemma 3 4B became available via the Google API on 2025-03-13.

Training & availability

Training data has a knowledge cutoff of 2024-08-31 — information about events after that date is unlikely to appear in the model's responses. Google has not released the underlying model weights — access is via their hosted API only.

Capabilities

Context window: 131K tokens.
Input modalities: text, image.

Recommended for: vision, cheap.

Limitations

The knowledge cutoff is 19 months old — this model will not know about recent events, releases, or API changes.

Pricing

Input: $0.0400 per 1M tokens
Output: $0.0800 per 1M tokens

Use the cost calculator above to estimate monthly spend for your workload.

Quick start

Minimal example using the google API. Copy, paste, replace the key.

from google import genai

client = genai.Client(api_key="...")
resp = client.models.generate_content(
    model="gemma-3.4b-it",
    contents="Explain quantum computing in one sentence.",
)
print(resp.text)

Cost calculator

Estimate your monthly bill. Presets are typical workload sizes.

Input tokens / month5.0M

@ $0.04/1M

Output tokens / month2.0M

@ $0.08/1M

Input cost

$0.2

5.0M × $0.04/1M

Output cost

$0.16

2.0M × $0.08/1M

Total / month

$0.36

$4.32 / year

Providers & performance

1 provider

Multi-provider inference routes for this model — sorted by throughput. Latency is time-to-first-token; throughput is output tokens per second. Data from OpenRouter, measured over the last 30 minutes.

Provider	Throughput	Latency (TTFT)	Input $ / 1M	Output $ / 1M	Context	Quant	Supports
DeepInfra	20tok/s	464ms	$0.04	$0.08	131K	bf16	json

Popularity

Signals from open-source communities — not a quality measure, but useful for gauging adoption among developers.

🤗

HuggingFace downloads

1.8M/ month

HuggingFace likes

1.3K

Integrations & tooling support

Tool calling: Not supported
Structured outputs: Not supported

Price vs quality

Budget pricing

Priced low — good for high-volume tasks. Quality tier pending more benchmark coverage.