Model Comparison

DeepSeek-V3.1 vs Kimi K2 Base

DeepSeek-V3.1 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek-V3.1 outperforms in 3 benchmarks (GPQA, MMLU-Pro, SimpleQA), while Kimi K2 Base is better at 0 benchmarks.

DeepSeek-V3.1 significantly outperforms across most benchmarks.

Tue Apr 14 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
Tue Apr 14 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.1
Input tokens$0.27
Output tokens$1.00
Best providerDeepinfra
Moonshot AI
Kimi K2 Base
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

329.0B diff

Kimi K2 Base has 329.0B more parameters than DeepSeek-V3.1, making it 49.0% larger.

DeepSeek
DeepSeek-V3.1
671.0Bparameters
Moonshot AI
Kimi K2 Base
1000.0Bparameters
671.0B
DeepSeek-V3.1
1000.0B
Kimi K2 Base

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.1 specifies input context (163,840 tokens). Only DeepSeek-V3.1 specifies output context (163,840 tokens).

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
Moonshot AI
Kimi K2 Base
Input- tokens
Output- tokens
Tue Apr 14 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-V3.1

MIT

Open weights

Kimi K2 Base

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while Kimi K2 Base was released on 2025-07-11.

Kimi K2 Base is 6 months newer than DeepSeek-V3.1.

DeepSeek-V3.1

Jan 10, 2025

1.3 years ago

Kimi K2 Base

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

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (163,840 tokens)
Higher GPQA score (74.9% vs 48.1%)
Higher MMLU-Pro score (83.7% vs 69.2%)
Higher SimpleQA score (93.4% vs 35.3%)
Moonshot AI

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.1
Moonshot AI
Kimi K2 Base

FAQ

Common questions about DeepSeek-V3.1 vs Kimi K2 Base

DeepSeek-V3.1 significantly outperforms across most benchmarks. DeepSeek-V3.1 is made by DeepSeek and Kimi K2 Base is made by Moonshot AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. Kimi K2 Base scores C-Eval: 92.5%, GSM8k: 92.1%, MMLU-redux-2.0: 90.2%, MMLU: 87.8%, TriviaQA: 85.1%.
DeepSeek-V3.1 supports 164K 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.
DeepSeek-V3.1 is developed by DeepSeek and Kimi K2 Base is developed by Moonshot AI.