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DeepSeek-V3 vs Kimi K2 Base

DeepSeek-V3 and Kimi K2 Base are closely matched at 15.1 and 13.0 on the LLM Stats Score.

DeepSeek · Moonshot AI · Updated for 2026

Which is better?

DeepSeek-V3 and Kimi K2 Base are closely matched on the overall LLM Stats Score at 15.1 and 13.0.

The models split the 6 individual benchmarks reported for both models evenly.

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

Choose DeepSeek-V3

  • you want predictable pricing at $0.27/M input and $0.89/M output

Choose Kimi K2 Base

  • your work emphasizes coding — it leads those capability indexes
  • you want the most recent training data — it shipped Jul 2025

At a glance

The differences that matter most.

DeepSeek-V3
DeepSeek
Kimi K2 Base
Moonshot AI
Core performance indexes
LLM Stats Score
15.1
#254
13.0
#270
Reasoning
14.3
#253
12.1
#268
Coding
5.9
#215
10.5
#185
Cost, coverage & limits
Benchmark wins
3 of 6
3 of 6
Input price
$0.27 / M
— / M
Output price
$0.89 / M
— / M
Context window
131,072
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
DeepSeek-V3
Kimi K2 Base
Math
17.9#199
14.2#231
Finance
19.9#86
16.3#119
Legal
19.9#69
16.3#100
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V3 · 13 for Kimi K2 Base

6 shared

DeepSeek-V3 outperforms in 3 benchmarks (GPQA, MMLU, MMLU-Pro), while Kimi K2 Base is better at 3 benchmarks (C-Eval, CSimpleQA, SimpleQA).

Both models are evenly matched across the benchmarks.

Sat Oct 24 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

329.0B diff

Kimi K2 Base has 329.0B more parameters than DeepSeek-V3, making it 49.0% larger.

DeepSeek
DeepSeek-V3
671.0Bparameters
Moonshot AI
Kimi K2 Base
1.0Tparameters
671.0B
DeepSeek-V3
1000.0B
Kimi K2 Base

Context Window

Maximum input and output token capacity

Only DeepSeek-V3 specifies input context (131,072 tokens). Only DeepSeek-V3 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Moonshot AI
Kimi K2 Base
Input- tokens
Output- tokens
Sat Oct 24 2026 • llm-stats.com

License

Usage and distribution terms

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

MIT

Open weights

Release Timeline

When each model was launched

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

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

DeepSeek-V3

Dec 25, 2024

1.8 years ago

Kimi K2 Base

Jul 11, 2025

1.3 years 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

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against DeepSeek-V3 and Kimi K2 Base side-by-side, then vote on the output you prefer.

DeepSeek-V3
✓ Preferred
Kimi K2 Base
Open in Playground

FAQ

Common questions about DeepSeek-V3 vs Kimi K2 Base.

Which is better, DeepSeek-V3 or Kimi K2 Base?

DeepSeek-V3 and Kimi K2 Base are closely matched on the LLM Stats Score at 15.1 and 13.0. DeepSeek-V3 is made by DeepSeek and Kimi K2 Base 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 DeepSeek-V3 compare to Kimi K2 Base in benchmarks?

DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. Kimi K2 Base scores C-Eval: 92.5%, GSM8k: 92.1%, MMLU-redux-2.0: 90.2%, MMLU: 87.8%, TriviaQA: 85.1%.

What are the context window sizes for DeepSeek-V3 and Kimi K2 Base?

DeepSeek-V3 supports 131K tokens and Kimi K2 Base supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3 and Kimi K2 Base?

Key differences include LLM Stats Score (15.1 vs 13.0), licensing (MIT + Model License (Commercial use allowed) vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3 and Kimi K2 Base?

DeepSeek-V3 is developed by DeepSeek and Kimi K2 Base is developed by Moonshot AI.

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