Model Comparison

DeepSeek-V3 vs Kimi K2 Instruct

Kimi K2 Instruct significantly outperforms across most benchmarks. DeepSeek-V3 is 1.0x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

11 benchmarks

DeepSeek-V3 outperforms in 0 benchmarks, while Kimi K2 Instruct is better at 11 benchmarks (Aider-Polyglot, AIME 2024, CNMO 2024, CSimpleQA, GPQA, IFEval, MATH-500, MMLU, MMLU-Pro, MMLU-Redux, SimpleQA).

Kimi K2 Instruct significantly outperforms across most benchmarks.

Thu Apr 16 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek-V3 costs less

For input processing, DeepSeek-V3 ($0.27/1M tokens) is 1.9x cheaper than Kimi K2 Instruct ($0.50/1M tokens).

For output processing, DeepSeek-V3 ($1.10/1M tokens) is 2.2x more expensive than Kimi K2 Instruct ($0.50/1M tokens).

In conclusion, Kimi K2 Instruct is more expensive than DeepSeek-V3.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Thu Apr 16 2026 • llm-stats.com
DeepSeek
DeepSeek-V3
Input tokens$0.27
Output tokens$1.10
Best providerDeepSeek
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-V3, making it 49.0% larger.

DeepSeek
DeepSeek-V3
671.0Bparameters
Moonshot AI
Kimi K2 Instruct
1000.0Bparameters
671.0B
DeepSeek-V3
1000.0B
Kimi K2 Instruct

Context Window

Maximum input and output token capacity

Kimi K2 Instruct accepts 200,000 input tokens compared to DeepSeek-V3's 131,072 tokens. Kimi K2 Instruct can generate longer responses up to 200,000 tokens, while DeepSeek-V3 is limited to 131,072 tokens.

DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Moonshot AI
Kimi K2 Instruct
Input200,000 tokens
Output200,000 tokens
Thu Apr 16 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while Kimi K2 Instruct uses MIT.

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

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

Kimi K2 Instruct

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while Kimi K2 Instruct was released on 2025-07-11.

Kimi K2 Instruct is 7 months newer than DeepSeek-V3.

DeepSeek-V3

Dec 25, 2024

1.3 years ago

Kimi K2 Instruct

Jul 11, 2025

9 months ago

6mo 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

Provider Availability

DeepSeek-V3 is available from DeepSeek. Kimi K2 Instruct is available from Fireworks, Novita.

DeepSeek-V3

deepseek logo
DeepSeek
Input Price:Input: $0.27/1MOutput Price:Output: $1.10/1M

Kimi K2 Instruct

fireworks logo
Fireworks
Input Price:Input: $0.50/1MOutput Price:Output: $0.50/1M
novita logo
Novita
Input Price:Input: $0.57/1MOutput Price:Output: $2.30/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Less expensive input tokens
Moonshot AI
Larger context window (200,000 tokens)
Less expensive output tokens
Higher Aider-Polyglot score (60.0% vs 49.6%)
Higher AIME 2024 score (69.6% vs 39.2%)
Higher CNMO 2024 score (74.3% vs 43.2%)
Higher CSimpleQA score (78.4% vs 64.8%)
Higher GPQA score (75.1% vs 59.1%)
Higher IFEval score (89.8% vs 86.1%)
Higher MATH-500 score (97.4% vs 90.2%)
Higher MMLU score (89.5% vs 88.5%)
Higher MMLU-Pro score (81.1% vs 75.9%)
Higher MMLU-Redux score (92.7% vs 89.1%)
Higher SimpleQA score (31.0% vs 24.9%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3
Moonshot AI
Kimi K2 Instruct

FAQ

Common questions about DeepSeek-V3 vs Kimi K2 Instruct

Kimi K2 Instruct significantly outperforms across most benchmarks. DeepSeek-V3 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-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. Kimi K2 Instruct scores MATH-500: 97.4%, GSM8k: 97.3%, CBNSL: 95.6%, HumanEval: 93.3%, MMLU-Redux: 92.7%.
DeepSeek-V3 is 1.9x cheaper for input tokens. DeepSeek-V3 costs $0.27/M input and $1.10/M output via deepseek. Kimi K2 Instruct costs $0.50/M input and $0.50/M output via fireworks.
DeepSeek-V3 supports 131K tokens and Kimi K2 Instruct supports 200K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (131K vs 200K), input pricing ($0.27 vs $0.50/M), licensing (MIT + Model License (Commercial use allowed) vs MIT). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3 is developed by DeepSeek and Kimi K2 Instruct is developed by Moonshot AI.