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DeepSeek-V4.1-Flash vs Kimi-k1.5

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 17.2.

DeepSeek · Moonshot AI · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 17.2, ranking #12 overall.

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

Choose DeepSeek-V4.1-Flash

  • overall performance matters — it scores 51.8 and ranks #12 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you want the most recent training data — it shipped Sep 2026
  • you need open weights you can self-host or fine-tune

Choose Kimi-k1.5

  • you are already invested in the Moonshot AI ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash
DeepSeek
Kimi-k1.5
Moonshot AI
Core performance indexes
LLM Stats Score
51.8
#12
17.2
#217
Reasoning
48.9
#17
16.4
#214
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.22 / M
— / M
Output price
$0.66 / M
— / M
Context window
1,040,000
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
DeepSeek-V4.1-Flash
Kimi-k1.5
Math
35.2#43
20.2#160
Vision
29.5#31
11.0#111
Multimodal
34.3#13
14.7#92
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V4.1-Flash · 9 for Kimi-k1.5

No common benchmarks found

DeepSeek-V4.1-Flash and Kimi-k1.5don'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

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash specifies input context (1,040,000 tokens). Only DeepSeek-V4.1-Flash specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash
Input1,040,000 tokens
Output393,216 tokens
Moonshot AI
Kimi-k1.5
Input- tokens
Output- tokens
Sat Sep 12 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash and Kimi-k1.5 support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

DeepSeek-V4.1-Flash

Text
Images
Audio
Video

Kimi-k1.5

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash is licensed under MIT, while Kimi-k1.5 uses a proprietary license.

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

DeepSeek-V4.1-Flash

MIT

Open weights

Kimi-k1.5

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash was released on 2026-09-10, while Kimi-k1.5 was released on 2025-01-20.

DeepSeek-V4.1-Flash is 20 months newer than Kimi-k1.5.

DeepSeek-V4.1-Flash

Sep 10, 2026

2 days ago

1.6yr newer
Kimi-k1.5

Jan 20, 2025

1.6 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?Start an Issue discussion→

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash and Kimi-k1.5 side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash
✓ Preferred
Kimi-k1.5
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash vs Kimi-k1.5.

Which is better, DeepSeek-V4.1-Flash or Kimi-k1.5?

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 17.2. DeepSeek-V4.1-Flash is made by DeepSeek and Kimi-k1.5 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 compare to Kimi-k1.5 in benchmarks?

DeepSeek-V4.1-Flash scores CodeForces: 100.0%, GPQA: 90.9%, Terminal-Bench 2.1: 90.6%, BabyVision: 89.6%, CyberGym: 88.1%. Kimi-k1.5 scores MATH-500: 96.2%, CLUEWSC: 91.4%, C-Eval: 88.3%, MMLU: 87.4%, IFEval: 87.2%.

What are the context window sizes for DeepSeek-V4.1-Flash and Kimi-k1.5?

DeepSeek-V4.1-Flash supports 1.0M tokens and Kimi-k1.5 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 and Kimi-k1.5?

Key differences include LLM Stats Score (51.8 vs 17.2), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash and Kimi-k1.5?

DeepSeek-V4.1-Flash is developed by DeepSeek and Kimi-k1.5 is developed by Moonshot AI.

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