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DeepSeek-V4.1-Flash-1017 vs Kimi K2 Base

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 13.2.

DeepSeek · Moonshot AI · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to 13.2, ranking #15 overall.

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

Choose DeepSeek-V4.1-Flash-1017

  • overall performance matters — it scores 51.0 and ranks #15 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026

Choose Kimi K2 Base

  • you are already invested in the Moonshot AI ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
Kimi K2 Base
Moonshot AI
Core performance indexes
LLM Stats Score
51.0
#15
13.2
#268
Reasoning
48.6
#18
12.3
#266
Coding
39.1
#15
10.6
#184
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.30 / M
— / M
Output price
$1.20 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4.1-Flash-1017
Kimi K2 Base
Math
31.8#67
14.2#230
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 13 for Kimi K2 Base

No common benchmarks found

DeepSeek-V4.1-Flash-1017 and Kimi K2 Basedon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

236.8B diff

Kimi K2 Base has 236.8B more parameters than DeepSeek-V4.1-Flash-1017, making it 31.0% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
Moonshot AI
Kimi K2 Base
1.0Tparameters
763.2B
DeepSeek-V4.1-Flash-1017
1000.0B
Kimi K2 Base

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash-1017 specifies input context (1,048,576 tokens). Only DeepSeek-V4.1-Flash-1017 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
Moonshot AI
Kimi K2 Base
Input- tokens
Output- tokens
Tue Oct 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas Kimi K2 Base does not.

DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4.1-Flash-1017

Text
Images
Audio
Video

Kimi K2 Base

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under MIT.

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

DeepSeek-V4.1-Flash-1017

MIT

Open weights

Kimi K2 Base

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Kimi K2 Base was released on 2025-07-11.

DeepSeek-V4.1-Flash-1017 is 15 months newer than Kimi K2 Base.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

2 days ago

1.3yr newer
Kimi K2 Base

Jul 11, 2025

1.3 years ago

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-V4.1-Flash-1017 and Kimi K2 Base side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
Kimi K2 Base
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs Kimi K2 Base.

Which is better, DeepSeek-V4.1-Flash-1017 or Kimi K2 Base?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 13.2. DeepSeek-V4.1-Flash-1017 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-V4.1-Flash-1017 compare to Kimi K2 Base in benchmarks?

DeepSeek-V4.1-Flash-1017 scores BabyVision: 92.5%, Chartography: 83.0%, DeepSWE 1.1: 77.0%, NL2Repo: 68.2%, AutomationBench: 60.5%. 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-V4.1-Flash-1017 and Kimi K2 Base?

DeepSeek-V4.1-Flash-1017 supports 1.0M 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-V4.1-Flash-1017 and Kimi K2 Base?

Key differences include LLM Stats Score (51.0 vs 13.2), multimodal support (yes vs no). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and Kimi K2 Base?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and Kimi K2 Base is developed by Moonshot AI.

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