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Open SourceGoogle

Gemma 4 12B

Gemma 4 12B is a chat model from Google in the Gemma family, sized at roughly 12 billion parameters and positioned as a mid-size option between smaller and larger Gemma variants.

License Open Source
Input from
$0.180 / 1M tokens
on DigitalOcean · 1 provider
Output from
$0.500 / 1M tokens
on DigitalOcean

API Pricing

ProviderInput / 1MOutput / 1MCached / 1M
$0.180$0.500$0.036

Prices updated daily. Last check: Nov 6, 2026

Compare API pricing for every Gemma model →

Gemma 4 12B pricing by provider

Input, output, and batch rates, plus alternatives, for one provider at a time.

Performance & Benchmarks

Source: Artificial Analysis →
Output Speed
109 t/s
Latency (TTFT)
2.2s

Benchmarks measured Nov 2026. Scores are independent evaluations, not vendor-reported.

Model Details

General

Creator
Google
Modalities
Text

Capabilities

Open Source
Yes

Strengths & Limitations

Strengths

  • Mid-size 12B parameter class, a common sweet spot between smaller Gemma variants and larger configurations for accuracy-versus-cost tradeoffs
  • Measured output throughput of roughly 141 tokens per second in third-party benchmarking, adequate for streaming chat interfaces
  • Part of Google's Gemma line, which is generally served by multiple independent inference providers rather than a single vendor
  • Multiple hosting options mean you can compare per-provider pricing and latency rather than accepting a single rate
  • Parameter count in the 12B range is small enough to be a candidate for self-hosting on a single modern accelerator, depending on quantization
  • Suited to high-volume text workloads where a large frontier model would be more capacity than the task requires

Limitations

  • Time to first token of roughly 1.34 seconds in benchmarking, which is not ideal for latency-critical interactive paths
  • We do not currently track a confirmed context window for this model — verify with your chosen provider
  • No verified accuracy benchmark scores (reasoning, coding, math) are recorded in our database for this entry
  • A 12B-class model will generally trail much larger models on complex multi-step reasoning and long-horizon agentic tasks
  • Performance figures come from one provider configuration and will differ across hosts, hardware, and load conditions

Key Features

•Chat/instruct-tuned text generation
•Approximately 12 billion parameter class
•Measured output throughput of ~141 tokens per second (Artificial Analysis)
•Measured time to first token of ~1,336 ms (Artificial Analysis)
•Part of Google's Gemma model line
•Available through multiple third-party inference providers
•Streaming token output for interactive applications

About Gemma 4 12B

Gemma 4 12B is a text chat model released by Google as part of its Gemma line. The "12B" designation refers to its approximate parameter count, which places it in the mid-size band of the family — larger than the compact variants intended for on-device or very high-volume use, and smaller than the largest Gemma configurations. Gemma models are Google's separately branded line from Gemini, and are typically served across a range of third-party inference providers rather than a single first-party endpoint, which is why the pricing table on this page may show several hosts for the same model. On measured serving performance, third-party benchmarking from Artificial Analysis records Gemma 4 12B at approximately 141 output tokens per second with a time to first token of roughly 1.34 seconds. The throughput figure is characteristic of a model in this parameter range: fast enough for interactive chat and streaming responses, while the first-token latency indicates it is better suited to conversational and batch workloads than to latency-critical paths where sub-second response initiation is required. These numbers reflect a specific provider configuration and will vary meaningfully depending on which host you select, the hardware behind it, and the batch conditions at the time of the request. In practice, a 12B-class model is commonly chosen when a smaller model is not accurate enough for the task but a large frontier model is more capacity than the workload justifies. Typical deployments include assistant-style chat, summarization, drafting, classification with reasoning, and retrieval-augmented question answering. We do not currently track a confirmed context window, modality list, or benchmark accuracy scores for Gemma 4 12B in our database, so evaluate those specifics against your target provider's documentation before committing to a deployment.

Common Use Cases

Gemma 4 12B fits workloads that need better quality than a very small model provides but do not justify the cost of a large frontier model: customer-facing chat assistants, document and meeting summarization, email and content drafting, structured extraction from unstructured text, and retrieval-augmented question answering over a knowledge base. Its measured throughput of around 141 tokens per second supports streaming responses at a comfortable reading pace, making it viable for conversational UIs, while the roughly 1.3-second time to first token means it is a better match for chat and batch pipelines than for real-time voice or autocomplete paths where response initiation must be near-instant. Because Gemma models are typically available from several inference providers, this model is also a reasonable choice for teams that want to avoid single-vendor lock-in or that may eventually move the workload to self-hosted infrastructure — a 12B-class model is within reach of a single modern GPU depending on precision and context length.

Frequently Asked Questions

How much does Gemma 4 12B cost to run?

Pricing varies by provider and by pricing type — per-input-token, per-output-token, and hourly GPU rates for self-hosted deployments all differ. Because Gemma models are commonly served by multiple independent inference hosts, rates for the same model can vary considerably. Check the live pricing table on this page for current per-provider figures.

What is Gemma 4 12B best used for?

It suits mid-tier text workloads: chat assistants, summarization, drafting, classification, structured extraction, and retrieval-augmented question answering. It is a fit where a compact model is not accurate enough but a large frontier model is more capability than the task needs.

How fast is Gemma 4 12B?

Third-party benchmarking from Artificial Analysis records approximately 141 output tokens per second with a time to first token of about 1,336 milliseconds. Both figures reflect a specific provider setup and will vary with the host, hardware, quantization, and concurrent load.

How does Gemma 4 12B compare to smaller and larger Gemma variants?

The 12B designation places it in the middle of the family's size range. Smaller Gemma variants generally offer lower latency and cost at reduced accuracy, while larger configurations trade throughput and cost for stronger performance on complex reasoning. If you are choosing between them, benchmark all sizes on your own task rather than relying on parameter count alone.

What context window does Gemma 4 12B support?

We do not currently track a confirmed context window for this model in our database. Check the documentation of the specific inference provider you plan to use, since effective context limits can also be capped below the model's native maximum by individual hosts.