Model Comparison

DeepSeek R1 Zero vs Kimi K2 Instruct

Kimi K2 Instruct shows notably better performance in the majority of benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek R1 Zero outperforms in 1 benchmarks (AIME 2024), while Kimi K2 Instruct is better at 2 benchmarks (GPQA, MATH-500).

Kimi K2 Instruct shows notably better performance in the majority of benchmarks.

Thu Apr 30 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
Thu Apr 30 2026 • llm-stats.com
DeepSeek
DeepSeek R1 Zero
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Moonshot AI
Kimi K2 Instruct
Input tokens$0.50
Output tokens$0.50
Best providerFireworks
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Model Size

Parameter count comparison

329.0B diff

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

DeepSeek
DeepSeek R1 Zero
671.0Bparameters
Moonshot AI
Kimi K2 Instruct
1000.0Bparameters
671.0B
DeepSeek R1 Zero
1000.0B
Kimi K2 Instruct

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).

DeepSeek
DeepSeek R1 Zero
Input- tokens
Output- tokens
Moonshot AI
Kimi K2 Instruct
Input200,000 tokens
Output200,000 tokens
Thu Apr 30 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

DeepSeek R1 Zero

MIT

Open weights

Kimi K2 Instruct

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Zero was released on 2025-01-20, while Kimi K2 Instruct was released on 2025-07-11.

Kimi K2 Instruct is 6 months newer than DeepSeek R1 Zero.

DeepSeek R1 Zero

Jan 20, 2025

1.3 years ago

Kimi K2 Instruct

Jul 11, 2025

9 months ago

5mo 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

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Key Takeaways

DeepSeek
Higher AIME 2024 score (86.7% vs 69.6%)
Moonshot AI
Larger context window (200,000 tokens)
Higher GPQA score (75.1% vs 73.3%)
Higher MATH-500 score (97.4% vs 95.9%)

Detailed Comparison

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

FAQ

Common questions about DeepSeek R1 Zero vs Kimi K2 Instruct

Kimi K2 Instruct shows notably better performance in the majority of benchmarks. DeepSeek R1 Zero is made by DeepSeek and Kimi K2 Instruct is made by Moonshot AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek R1 Zero scores MATH-500: 95.9%, AIME 2024: 86.7%, GPQA: 73.3%, LiveCodeBench: 50.0%. Kimi K2 Instruct scores MATH-500: 97.4%, GSM8k: 97.3%, CBNSL: 95.6%, HumanEval: 93.3%, MMLU-Redux: 92.7%.
DeepSeek R1 Zero 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.
DeepSeek R1 Zero is developed by DeepSeek and Kimi K2 Instruct is developed by Moonshot AI.