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.
API Pricing
| Provider | Input / 1M | Output / 1M | Cached / 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 →Benchmarks measured Nov 2026. Scores are independent evaluations, not vendor-reported.
Model Details
General
- Creator
- 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
About Gemma 4 12B
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.