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Gemini 4 Argon vs Kimi K2 Instruct

Gemini 4 Argon leads the LLM Stats Score 55.1 to 21.8.

Google · Moonshot AI · Updated for 2026

Which is better?

Gemini 4 Argon leads the overall LLM Stats Score 55.1 to 21.8, ranking #4 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Gemini 4 Argon

  • overall performance matters — it scores 55.1 and ranks #4 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Sep 2026

Choose Kimi K2 Instruct

  • you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

Gemini 4 Argon
Google
Kimi K2 Instruct
Moonshot AI
Core performance indexes
LLM Stats Score
55.1
#4
21.8
#198
Reasoning
52.4
#7
22.0
#189
Coding
44.1
#4
12.3
#160
Agents
40.5
#4
-1.0
#191
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$0.50 / M
Output price
— / M
$0.50 / M
Context window
—
200,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Gemini 4 Argon
Kimi K2 Instruct
Tool use
30.6#6
6.3#157
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

19 reported for Gemini 4 Argon · 38 for Kimi K2 Instruct

No common benchmarks found

Gemini 4 Argon and Kimi K2 Instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

Only Kimi K2 Instruct specifies input context (200,000 tokens). Only Kimi K2 Instruct specifies output context (200,000 tokens).

Google
Gemini 4 Argon
Input- tokens
Output- tokens
Moonshot AI
Kimi K2 Instruct
Input200,000 tokens
Output200,000 tokens
Fri Oct 09 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Gemini 4 Argon supports multimodal inputs, whereas Kimi K2 Instruct does not.

Gemini 4 Argon can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemini 4 Argon

Text
Images
Audio
Video

Kimi K2 Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 4 Argon is licensed under a proprietary license, while Kimi K2 Instruct uses MIT.

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

Gemini 4 Argon

Proprietary

Closed source

Kimi K2 Instruct

MIT

Open weights

Release Timeline

When each model was launched

Gemini 4 Argon was released on 2026-09-30, while Kimi K2 Instruct was released on 2025-07-11.

Gemini 4 Argon is 15 months newer than Kimi K2 Instruct.

Gemini 4 Argon

Sep 30, 2026

1 weeks ago

1.2yr newer
Kimi K2 Instruct

Jul 11, 2025

1.2 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against Gemini 4 Argon and Kimi K2 Instruct side-by-side, then vote on the output you prefer.

Gemini 4 Argon
✓ Preferred
Kimi K2 Instruct
Open in Playground

FAQ

Common questions about Gemini 4 Argon vs Kimi K2 Instruct.

Which is better, Gemini 4 Argon or Kimi K2 Instruct?

Gemini 4 Argon leads the LLM Stats Score 55.1 to 21.8. Gemini 4 Argon is made by Google and Kimi K2 Instruct is made by Moonshot AI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Gemini 4 Argon compare to Kimi K2 Instruct in benchmarks?

Gemini 4 Argon scores Graphwalks BFS <128k: 99.7%, Vibe Code Bench: 91.9%, LVBench: 91.7%, LABBench2: 88.8%, Graphwalks BFS >128k: 84.2%. Kimi K2 Instruct scores MATH-500: 97.4%, GSM8k: 97.3%, CBNSL: 95.6%, HumanEval: 93.3%, MMLU-Redux: 92.7%.

What are the context window sizes for Gemini 4 Argon and Kimi K2 Instruct?

Gemini 4 Argon supports an unknown number of tokens and Kimi K2 Instruct supports 200K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Gemini 4 Argon and Kimi K2 Instruct?

Key differences include LLM Stats Score (55.1 vs 21.8), multimodal support (yes vs no), licensing (Proprietary vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemini 4 Argon and Kimi K2 Instruct?

Gemini 4 Argon is developed by Google and Kimi K2 Instruct is developed by Moonshot AI.

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