Model Comparison

Gemma 3n E2B Instructed vs Kimi K2-Thinking-0905

Kimi K2-Thinking-0905 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

Gemma 3n E2B Instructed outperforms in 0 benchmarks, while Kimi K2-Thinking-0905 is better at 3 benchmarks (AIME 2025, GPQA, MMLU-Pro).

Kimi K2-Thinking-0905 significantly outperforms across most benchmarks.

Wed Apr 15 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Wed Apr 15 2026 • llm-stats.com
Google
Gemma 3n E2B Instructed
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Moonshot AI
Kimi K2-Thinking-0905
Input tokens$0.47
Output tokens$2.00
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

992.0B diff

Kimi K2-Thinking-0905 has 992.0B more parameters than Gemma 3n E2B Instructed, making it 12400.0% larger.

Google
Gemma 3n E2B Instructed
8.0Bparameters
Moonshot AI
Kimi K2-Thinking-0905
1000.0Bparameters
8.0B
Gemma 3n E2B Instructed
1000.0B
Kimi K2-Thinking-0905

Context Window

Maximum input and output token capacity

Only Kimi K2-Thinking-0905 specifies input context (262,144 tokens). Only Kimi K2-Thinking-0905 specifies output context (262,144 tokens).

Google
Gemma 3n E2B Instructed
Input- tokens
Output- tokens
Moonshot AI
Kimi K2-Thinking-0905
Input262,144 tokens
Output262,144 tokens
Wed Apr 15 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 3n E2B Instructed supports multimodal inputs, whereas Kimi K2-Thinking-0905 does not.

Gemma 3n E2B Instructed can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemma 3n E2B Instructed

Text
Images
Audio
Video

Kimi K2-Thinking-0905

Text
Images
Audio
Video

License

Usage and distribution terms

Gemma 3n E2B Instructed is licensed under a proprietary license, while Kimi K2-Thinking-0905 uses MIT.

License differences may affect how you can use these models in commercial or open-source projects.

Gemma 3n E2B Instructed

Proprietary

Closed source

Kimi K2-Thinking-0905

MIT

Open weights

Release Timeline

When each model was launched

Gemma 3n E2B Instructed was released on 2025-06-26, while Kimi K2-Thinking-0905 was released on 2025-09-05.

Kimi K2-Thinking-0905 is 2 months newer than Gemma 3n E2B Instructed.

Gemma 3n E2B Instructed

Jun 26, 2025

9 months ago

Kimi K2-Thinking-0905

Sep 5, 2025

7 months ago

2mo newer

Knowledge Cutoff

When training data ends

Gemma 3n E2B Instructed has a documented knowledge cutoff of 2024-06-01, while Kimi K2-Thinking-0905's cutoff date is not specified.

We can confirm Gemma 3n E2B Instructed's training data extends to 2024-06-01, but cannot make a direct comparison without Kimi K2-Thinking-0905's cutoff date.

Gemma 3n E2B Instructed

Jun 2024

Kimi K2-Thinking-0905

—

Outputs Comparison

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Key Takeaways

Google
Supports multimodal inputs
Moonshot AI
Larger context window (262,144 tokens)
Has open weights
Higher AIME 2025 score (100.0% vs 6.7%)
Higher GPQA score (84.5% vs 24.8%)
Higher MMLU-Pro score (84.6% vs 40.5%)

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemma 3n E2B Instructed
Moonshot AI
Kimi K2-Thinking-0905

FAQ

Common questions about Gemma 3n E2B Instructed vs Kimi K2-Thinking-0905

Kimi K2-Thinking-0905 significantly outperforms across most benchmarks. Gemma 3n E2B Instructed is made by Google and Kimi K2-Thinking-0905 is made by Moonshot AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
Gemma 3n E2B Instructed scores HumanEval: 66.5%, MMLU: 60.1%, Global-MMLU-Lite: 59.0%, MBPP: 56.6%, Global-MMLU: 55.1%. Kimi K2-Thinking-0905 scores AIME 2025: 100.0%, HMMT 2025: 97.5%, MMLU-Redux: 94.4%, FRAMES: 87.0%, MMLU-Pro: 84.6%.
Gemma 3n E2B Instructed supports an unknown number of tokens and Kimi K2-Thinking-0905 supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (yes vs no), licensing (Proprietary vs MIT). See the full comparison above for benchmark-by-benchmark results.
Gemma 3n E2B Instructed is developed by Google and Kimi K2-Thinking-0905 is developed by Moonshot AI.