Gemma 3 12B vs GPT-4.1 nano
Gemma 3 12B and GPT-4.1 nano are closely matched at 5.6 and 1.6 on the LLM Stats Score. Gemma 3 12B is 2.3x cheaper per token.
Google · OpenAI · Updated for 2026
Which is better?
Gemma 3 12B and GPT-4.1 nano are closely matched on the overall LLM Stats Score at 5.6 and 1.6.
The models split the 2 individual benchmarks reported for both models evenly.
On price, Gemma 3 12B is roughly 2.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-4.1 nano also accepts a larger context window (1,047,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Gemma 3 12B
- cost matters — it's about 2.3x cheaper per token
- you need open weights you can self-host or fine-tune
Choose GPT-4.1 nano
- you process long inputs — it offers a 1,047,576 token context window
- you want the most recent training data — it shipped Apr 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
26 reported for Gemma 3 12B · 24 for GPT-4.1 nano
Gemma 3 12B outperforms in 1 benchmarks (IFEval), while GPT-4.1 nano is better at 1 benchmark (GPQA).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Gemma 3 12B ($0.05/1M tokens) is 2.0x cheaper than GPT-4.1 nano ($0.10/1M tokens).
For output processing, Gemma 3 12B ($0.15/1M tokens) is 2.7x cheaper than GPT-4.1 nano ($0.40/1M tokens).
In conclusion, GPT-4.1 nano is more expensive than Gemma 3 12B.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-4.1 nano accepts 1,047,576 input tokens compared to Gemma 3 12B's 131,072 tokens. Gemma 3 12B can generate longer responses up to 131,072 tokens, while GPT-4.1 nano is limited to 32,768 tokens.
Input capabilities
Documented input modalities across available providers
Both Gemma 3 12B and GPT-4.1 nano support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Gemma 3 12B
GPT-4.1 nano
License
Usage and distribution terms
Gemma 3 12B is licensed under Gemma, while GPT-4.1 nano uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
Gemma
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Gemma 3 12B was released on 2025-03-12, while GPT-4.1 nano was released on 2025-04-14.
GPT-4.1 nano is 1 month newer than Gemma 3 12B.
Mar 12, 2025
1.5 years ago
Apr 14, 2025
1.4 years ago
1mo newerKnowledge Cutoff
When training data ends
GPT-4.1 nano has a documented knowledge cutoff of 2024-05-31, while Gemma 3 12B's cutoff date is not specified.
We can confirm GPT-4.1 nano's training data extends to 2024-05-31, but cannot make a direct comparison without Gemma 3 12B's cutoff date.
—
May 2024
Provider Availability
Gemma 3 12B is available from DeepInfra. GPT-4.1 nano is available from OpenAI.
Gemma 3 12B
GPT-4.1 nano
Outputs Comparison
Judge for yourself.
Run your own prompts against Gemma 3 12B and GPT-4.1 nano side-by-side, then vote on the output you prefer.
FAQ
Common questions about Gemma 3 12B vs GPT-4.1 nano.