Model Comparison

DeepSeek R1 Zero vs Kimi K2 0905Which is better in 2026?

Both models are evenly matched across the benchmarks.

Verdict: DeepSeek R1 Zero vs Kimi K2 0905 — which is better?

DeepSeek R1 Zero (by DeepSeek) and Kimi K2 0905 (by Moonshot AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

DeepSeek R1 Zero outperforms in 1 benchmarks (AIME 2024), while Kimi K2 0905 is better at 1 benchmark (GPQA). Both models are evenly matched across the benchmarks.

Choose DeepSeek R1 Zero if…

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

Choose Kimi K2 0905 if…

  • you want the most recent training data — it shipped Sep 2025
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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek R1 Zero outperforms in 1 benchmarks (AIME 2024), while Kimi K2 0905 is better at 1 benchmark (GPQA).

Both models are evenly matched across the benchmarks.

Sun Jun 14 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

329.0B diff

Kimi K2 0905 has 329.0B more parameters than DeepSeek R1 Zero, making it 49.0% larger.

DeepSeek
DeepSeek R1 Zero
671.0Bparameters
Moonshot AI
Kimi K2 0905
1.0Tparameters
671.0B
DeepSeek R1 Zero
1000.0B
Kimi K2 0905

Context Window

Maximum input and output token capacity

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

DeepSeek
DeepSeek R1 Zero
Input- tokens
Output- tokens
Moonshot AI
Kimi K2 0905
Input262,144 tokens
Output262,144 tokens
Sun Jun 14 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek R1 Zero is licensed under MIT, while Kimi K2 0905 uses a proprietary license.

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

DeepSeek R1 Zero

MIT

Open weights

Kimi K2 0905

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek R1 Zero was released on 2025-01-20, while Kimi K2 0905 was released on 2025-09-05.

Kimi K2 0905 is 8 months newer than DeepSeek R1 Zero.

DeepSeek R1 Zero

Jan 20, 2025

1.4 years ago

Kimi K2 0905

Sep 5, 2025

9 months ago

7mo newer

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?Start an Issue discussion→

Key Takeaways

DeepSeek
Has open weights
Higher AIME 2024 score (86.7% vs 72.0%)
Moonshot AI
Larger context window (262,144 tokens)
Higher GPQA score (75.8% vs 73.3%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek R1 Zero
Moonshot AI
Kimi K2 0905

FAQ

Common questions about DeepSeek R1 Zero vs Kimi K2 0905.

Which is better, DeepSeek R1 Zero or Kimi K2 0905?

Both models are evenly matched across the benchmarks. DeepSeek R1 Zero is made by DeepSeek and Kimi K2 0905 is made by Moonshot AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek R1 Zero compare to Kimi K2 0905 in benchmarks?

DeepSeek R1 Zero scores MATH-500: 95.9%, AIME 2024: 86.7%, GPQA: 73.3%, LiveCodeBench: 50.0%. Kimi K2 0905 scores HumanEval: 94.5%, MMLU: 90.2%, MATH: 89.1%, MMLU-Pro: 82.5%, GPQA: 75.8%.

What are the context window sizes for DeepSeek R1 Zero and Kimi K2 0905?

DeepSeek R1 Zero supports an unknown number of tokens and Kimi K2 0905 supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek R1 Zero and Kimi K2 0905?

Key differences include licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek R1 Zero and Kimi K2 0905?

DeepSeek R1 Zero is developed by DeepSeek and Kimi K2 0905 is developed by Moonshot AI.

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