Gemma 4 E4B vs Kimi K2 Instruct
Kimi K2 Instruct leads the LLM Stats Score 21.9 to 13.8. Gemma 4 E4B is 12.5x cheaper per token.
Google · Moonshot AI · Updated for 2026
Which is better?
Kimi K2 Instruct leads the overall LLM Stats Score 21.9 to 13.8, ranking #190 overall.
In the 3 individual benchmarks reported for both models, Kimi K2 Instruct wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, Gemma 4 E4B is roughly 12.5x 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 4 E4B
- cost matters — it's about 12.5x cheaper per token
- you want the most recent training data — it shipped Apr 2026
Choose Kimi K2 Instruct
- overall performance matters — it scores 21.9 and ranks #190 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- you process long inputs — it offers a 200,000 token context window
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
11 reported for Gemma 4 E4B · 38 for Kimi K2 Instruct
Gemma 4 E4B outperforms in 0 benchmarks, while Kimi K2 Instruct is better at 3 benchmarks (GPQA, LiveCodeBench v6, MMLU-Pro).
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 4 E4B ($0.02/1M tokens) is 25.0x cheaper than Kimi K2 Instruct ($0.50/1M tokens).
For output processing, Gemma 4 E4B ($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 4 E4B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2 Instruct has 992.0B more parameters than Gemma 4 E4B, making it 12400.0% larger.
Context Window
Maximum input and output token capacity
Kimi K2 Instruct accepts 200,000 input tokens compared to Gemma 4 E4B's 131,072 tokens. Kimi K2 Instruct can generate longer responses up to 200,000 tokens, while Gemma 4 E4B is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Gemma 4 E4B supports multimodal inputs, whereas Kimi K2 Instruct does not.
Gemma 4 E4B can handle both text and other forms of data like images, making it suitable for multimodal applications.
Gemma 4 E4B
Kimi K2 Instruct
License
Usage and distribution terms
Gemma 4 E4B is licensed under Apache 2.0, while Kimi K2 Instruct uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Gemma 4 E4B was released on 2026-04-02, while Kimi K2 Instruct was released on 2025-07-11.
Gemma 4 E4B is 9 months newer than Kimi K2 Instruct.
Apr 2, 2026
6 months ago
8mo newerJul 11, 2025
1.2 years ago
Knowledge Cutoff
When training data ends
Gemma 4 E4B has a documented knowledge cutoff of 2025-01-01, while Kimi K2 Instruct's cutoff date is not specified.
We can confirm Gemma 4 E4B's training data extends to 2025-01-01, but cannot make a direct comparison without Kimi K2 Instruct's cutoff date.
Jan 2025
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Provider Availability
Gemma 4 E4B is available from DeepInfra. Kimi K2 Instruct is available from Fireworks, Novita.
Gemma 4 E4B
Kimi K2 Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Gemma 4 E4B and Kimi K2 Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Gemma 4 E4B vs Kimi K2 Instruct.