Google: Gemma 3 12B

Google

•

Gemma 3 12B Instruct

•

Released

March 2025

Google
Gemma 3 12B Instruct

Google's 12B open-weight vision-language model with a 128K context window that runs efficiently on a single GPU.

Modality:

Text

Image

Video

PDF

model ID

google/gemma-3-12b-it

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Output Speed *

0.00

tok/s

Intelligence Index *

3.8

/ 100

Context Window *

131072

tokens

Input price

0.24

Anytoken

Output price

0.78

Anytoken

Gemma 3 12B: Open-Weight Vision-Language With a 128K Context on One GPU Gemma 3 12B is a 12-billion-parameter open-weight model from Google DeepMind, built on the same research as the Gemini family. It sits between the 4B and 27B variants as the balanced tier: multimodal text-and-image input, a 128K-token context window, and support for 140+ languages. Unlike proprietary API-only models, its open weights permit self-hosting on a single GPU or TPU. It fits document analysis, multilingual understanding, and vision-language tasks where deployment control and cost efficiency matter more than frontier reasoning scores. Start building with the Gemma 3 12B API on AnyAPI.ai

Performance

Where Gemma 3 12B Actually Delivers: Multilingual, Vision and Long Context

Gemma 3 12B's strength is broad, efficient capability at a modest size rather than frontier benchmark scores. It handles text and image input, a 128K context, and 140+ languages while running on a single accelerator. On Google's own evaluations it reached 60.6 on MMLU-Pro and 69.5 on Global MMLU-Lite, indicating solid knowledge and multilingual coverage for its class. As a non-reasoning model it responds directly without extended chain-of-thought. In production this means fast, controllable inference for document, multilingual, and vision-language workloads where self-hosting and predictable cost outweigh top-tier reasoning.

Benchmarks

Gemma 3 12B on Google's Published Benchmarks

Google's Gemma 3 technical report places the 12B instruction-tuned model at 60.6 on MMLU-Pro, 40.9 on GPQA Diamond, and 24.6 on LiveCodeBench — respectable for a 12B model but well below reasoning-tuned frontier systems. Independent testing by Artificial Analysis assigns it a modest Intelligence Index score and confirms it is not a reasoning model, providing direct answers without extended thinking. These numbers position Gemma 3 12B as a capable general-purpose open model rather than a math, coding, or agentic specialist. Validate coding-heavy or complex reasoning workloads on your own data before committing.

Output Speed

0.00

tok/s

Intelligence Index

3.8

/ 100

MMLU *

Broad world knowledge and problem-solving

GPQA *

PhD-level scientific reasoning across physics, biology, chemistry.

HLE *

Adherence to multi-step structured instructions.

LiveCodeBench *

Tool-calling reliability in long agentic loops.

Technical Specifications

What the model supports

Gemma 3 12B accepts text and image input and generates text only — it is not an image or audio generator. The 128K-token context window (versus 32K on the 1B model) is the standout production feature, enabling whole-document and multi-image prompts in a single call. It is a dense, non-reasoning model with function calling and structured output support. Open weights under the Gemma license permit commercial self-hosting. The most consequential limits: text-only output and a 12B parameter budget that caps performance on the hardest reasoning and coding tasks.

Verified Specifications —

Gemma 3 12B Instruct

Input modalities

Text

Image

Video

PDF

output modalities

Text

Context window

131072

tokens

Maximum output tokens

16384

Reasoning

Knowledge cutoff

March 2025

Pricing (standard)

0.24

AnyTokens in

0.78

AnyTokens out

Quickstart

Sample code for Gemma 3 12B Instruct

import requests

url = "https://api.anyapi.ai/v1/chat/completions"

payload = {
    "stream": False,
    "tool_choice": "auto",
    "logprobs": False,
    "model": "Model_Name",
    "messages": [
        {
            "content": [
                {
                    "type": "text",
                    "text": "Hello"
                },
                {
                    "image_url": {
                        "detail": "auto",
                        "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
                    },
                    "type": "image_url"
                }
            ],
            "role": "user"
        }
    ]
}
headers = {
    "Authorization": "Bearer AnyAPI_API_KEY",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.json())

import requests url = "https://api.anyapi.ai/v1/chat/completions" payload = { "stream": False, "tool_choice": "auto", "logprobs": False, "model": "Model_Name", "messages": [ { "content": [ { "type": "text", "text": "Hello" }, { "image_url": { "detail": "auto", "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" }, "type": "image_url" } ], "role": "user" } ] } headers = { "Authorization": "Bearer AnyAPI_API_KEY", "Content-Type": "application/json" } response = requests.post(url, json=payload, headers=headers) print(response.json())

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const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'},
  body: '{"stream":false,"tool_choice":"auto","logprobs":false,"model":"Model_Name","messages":[{"content":[{"type":"text","text":"Hello"},{"image_url":{"detail":"auto","url":"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"},"type":"image_url"}],"role":"user"}]}'
};

try {
  const response = await fetch(url, options);
  const data = await response.json();
  console.log(data);
} catch (error) {
  console.error(error);
}
const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = { method: 'POST', headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'}, body: '{"stream":false,"tool_choice":"auto","logprobs":false,"model":"Model_Name","messages":[{"content":[{"type":"text","text":"Hello"},{"image_url":{"detail":"auto","url":"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"},"type":"image_url"}],"role":"user"}]}'
}; try { const response = await fetch(url, options); const data = await response.json(); console.log(data);
} catch (error) { console.error(error);
}
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Code is copied

curl --request POST \
  --url https://api.anyapi.ai/v1/chat/completions \
  --header 'Authorization: Bearer AnyAPI_API_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
  "stream": false,
  "tool_choice": "auto",
  "logprobs": false,
  "model": "Model_Name",
  "messages": [
    {
      "content": [
        {
          "type": "text",
          "text": "Hello"
        },
        {
          "image_url": {
            "detail": "auto",
            "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
          },
          "type": "image_url"
        }
      ],
      "role": "user"
    }
  ]
}'

curl --request POST \ --url https://api.anyapi.ai/v1/chat/completions \ --header 'Authorization: Bearer AnyAPI_API_KEY' \ --header 'Content-Type: application/json' \ --data '{ "stream": false, "tool_choice": "auto", "logprobs": false, "model": "Model_Name", "messages": [ { "content": [ { "type": "text", "text": "Hello" }, { "image_url": { "detail": "auto", "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" }, "type": "image_url" } ], "role": "user" } ] }'

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Limitations & Trade-offs

Where Gemma 3 12B Instruct falls short

Not a reasoning model. Gemma 3 12B produces direct answers without an extended chain-of-thought phase, and independent evaluation confirms a modest intelligence score for its size class. This limits performance on multi-step math, complex logical reasoning, and hard agentic tasks. For workloads that depend on step-by-step deliberation, a dedicated reasoning model is a better fit than Gemma 3 12B.

Moderate coding performance. On Google's own LiveCodeBench evaluation the 12B reached 24.6, below the 27B and far below reasoning-tuned frontier models. It can assist with routine code generation and explanation but is not a competitive-programming or complex-refactoring engine. Teams building coding agents should benchmark it against larger or reasoning-oriented alternatives before relying on it.

Text-only output. Gemma 3 12B accepts text and images but generates only text. It cannot produce images, audio, or video. Applications needing generated visuals or speech must pair it with separate generation models; it functions purely as an understanding-and-generation-of-text engine on multimodal input.

Older knowledge cutoff. Training data ends in August 2024, so the model lacks awareness of later events, releases, and APIs. For questions about recent developments, retrieval augmentation or tool calling is required. The large 128K context makes RAG practical, but the base model alone will not reflect anything after its cutoff.

Best-Fit Workloads

Where this model earns its place

Long-document analysis The 128K-token context lets Gemma 3 12B ingest lengthy contracts, research papers, or transcripts in a single request for summarization, extraction, and Q&A. Its dense, non-reasoning design keeps latency predictable across long inputs. This suits knowledge-base pipelines and RAG systems where whole-document context beats chunked retrieval, though the August 2024 cutoff means external facts should come from retrieved passages.

Multilingual understanding With support for 140+ languages and a 69.5 Global MMLU-Lite score, Gemma 3 12B handles cross-lingual summarization, classification, and answering. Independent testing notes stronger European, East Asian, and Arabic handling than comparably sized peers. This fits global content moderation, multilingual support triage, and localization workflows, especially where self-hosting keeps sensitive data in-region.

Vision-language and OCR The built-in SigLIP vision encoder with Pan & Scan lets Gemma 3 12B read charts, describe scenes, and extract text from images, reporting measurable accuracy gains on document and visual QA. Combined with the long context, it processes many images per prompt. This suits document digitization, screenshot understanding, and visual Q&A — though it outputs text only, not generated images.

Cost-efficient self-hosted assistants Because Gemma 3 12B is open-weight and runs on a single GPU, it is a practical default for high-volume chat and drafting assistants where per-request cost and data control matter. Instruction-tuned checkpoints follow directions reliably. It fits internal tools and customer-facing assistants that don't require frontier reasoning, letting teams avoid per-token API costs by self-serving.

Pricing in anytokens via AnyAPI

Input

0.24

₳

Output

0.78

₳

Cache write

—

₳

Cache read

—

₳

Get API access

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Integration

Access Gemma 3 12B Instruct via AnyAPI.ai

Access Gemma 3 12B Instruct through AnyAPI.ai using a unified API built for multi-model AI applications. Integrate Gemma 3 12B Instruct without maintaining a separate provider-specific connection, and keep the flexibility to test, switch, or combine models as your application requirements evolve.

One API integration

Access Gemma 3 12B Instruct and other AI models through the same API workflow instead of maintaining separate integrations for every provider.

Easy model switching

Test Gemma 3 12B Instruct against alternative models or switch models as your performance, capability, or cost requirements change without rebuilding your application around another provider API.

Flexible for production

Use Gemma 3 12B Instruct from experimentation through production while keeping your AI stack flexible as workloads, traffic, and model requirements evolve.

Multi-model applications

Use Gemma 3 12B Instruct for the workloads where it performs best and combine it with other models for tasks that require different capabilities, performance, or efficiency.

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Frequently Asked Questions

Answers to common questions about integrating and using this AI model via AnyAPI.ai

What is the context window of Gemma 3 12B?

Gemma 3 12B has a 128K-token context window, per Google's official model card. This applies to the 4B, 12B, and 27B variants, while the 1B and 270M models are limited to 32K. The large window lets the model process long documents, codebases, or multi-image prompts in a single request.

Is Gemma 3 12B multimodal?

Yes. Gemma 3 12B accepts both text and image input using a built-in SigLIP vision encoder, and generates text output. It can perform OCR, chart and diagram interpretation, and visual question answering. It does not generate images, audio, or video — output is text only.

Is Gemma 3 12B a reasoning model?

No. Gemma 3 12B is a dense, non-reasoning model that provides direct responses without an extended chain-of-thought phase, as confirmed by independent evaluation. For multi-step math, complex logic, or hard agentic workflows, a dedicated reasoning model is generally a better choice.

Can Gemma 3 12B be used commercially?

Yes. Gemma 3 12B ships as an open-weight model under the Gemma license, which permits commercial use and self-hosting. The weights are publicly available for download, so teams can run the model on their own GPU or TPU infrastructure or access it through API providers.

What languages does Gemma 3 12B support?

Gemma 3 12B supports over 140 languages, using the Gemini 2.0 tokenizer and enhanced multilingual pre-training. Independent testing reports notably strong handling of European, East Asian, and Arabic languages relative to comparably sized open models, making it suitable for global multilingual applications.

* Benchmark data source: Artificial Analysis artificialanalysis.ai

Output Speed *

Intelligence Index *

Context Window *

Input price

Output price

Performance

Where Gemma 3 12B Actually Delivers: Multilingual, Vision and Long Context

Benchmarks

Gemma 3 12B on Google's Published Benchmarks

Output Speed

Intelligence Index

MMLU *

GPQA *

HLE *

LiveCodeBench *

Technical Specifications

What the model supports

Quickstart

Sample code for Gemma 3 12B Instruct

Limitations & Trade-offs

Where Gemma 3 12B Instruct falls short

Best-Fit Workloads

Where this model earns its place

on this page

Integration

Access Gemma 3 12B Instruct via AnyAPI.ai

One API integration

Easy model switching

Flexible for production

Multi-model applications

Frequently Asked Questions