Gemma 3 12B vs Kimi K2 Instruct
Kimi K2 Instruct leads the LLM Stats Score 22.0 to 5.7. Gemma 3 12B is 8.0x cheaper per token.
Google · Moonshot AI · Updated for 2026
Which is better?
Kimi K2 Instruct leads the overall LLM Stats Score 22.0 to 5.7, ranking #171 overall.
In the 6 individual benchmarks reported for both models, Kimi K2 Instruct wins 6; this is a narrower head-to-head signal than the composite indexes.
On price, Gemma 3 12B is roughly 8.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Kimi K2 Instruct also accepts a larger context window (200,000 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 8.0x cheaper per token
Choose Kimi K2 Instruct
- overall performance matters — it scores 22.0 and ranks #171 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 6 of 6 exact shared results
- you process long inputs — it offers a 200,000 token context window
- you want the most recent training data — it shipped Jul 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 · 38 for Kimi K2 Instruct
Gemma 3 12B outperforms in 0 benchmarks, while Kimi K2 Instruct is better at 6 benchmarks (GPQA, GSM8k, HumanEval, IFEval, MMLU-Pro, SimpleQA).
Kimi K2 Instruct significantly outperforms across most 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 10.0x cheaper than Kimi K2 Instruct ($0.50/1M tokens).
For output processing, Gemma 3 12B ($0.10/1M tokens) is 5.0x cheaper than Kimi K2 Instruct ($0.50/1M tokens).
In conclusion, Kimi K2 Instruct is more expensive than Gemma 3 12B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2 Instruct has 988.0B more parameters than Gemma 3 12B, making it 8233.3% larger.
Context Window
Maximum input and output token capacity
Kimi K2 Instruct accepts 200,000 input tokens compared to Gemma 3 12B's 131,072 tokens. Kimi K2 Instruct can generate longer responses up to 200,000 tokens, while Gemma 3 12B is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Gemma 3 12B supports multimodal inputs, whereas Kimi K2 Instruct does not.
Gemma 3 12B can handle both text and other forms of data like images, making it suitable for multimodal applications.
Gemma 3 12B
Kimi K2 Instruct
License
Usage and distribution terms
Gemma 3 12B is licensed under Gemma, while Kimi K2 Instruct uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Gemma
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Gemma 3 12B was released on 2025-03-12, while Kimi K2 Instruct was released on 2025-07-11.
Kimi K2 Instruct is 4 months newer than Gemma 3 12B.
Mar 12, 2025
1.5 years ago
Jul 11, 2025
1.1 years ago
4mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Gemma 3 12B is available from DeepInfra. Kimi K2 Instruct is available from Fireworks, Novita.
Gemma 3 12B
Kimi K2 Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Gemma 3 12B and Kimi K2 Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Gemma 3 12B vs Kimi K2 Instruct.