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DeepSeek-V4.1-Flash vs Kimi K2-Instruct-0905

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 21.6.

DeepSeek · Moonshot AI · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 21.6, ranking #13 overall.

In the 2 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 2; this is a narrower head-to-head signal than the composite indexes.

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 #13 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 2 of 2 exact shared results
  • you want the most recent training data — it shipped Sep 2026

Choose Kimi K2-Instruct-0905

  • you are already invested in the Moonshot AI ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash
DeepSeek
Kimi K2-Instruct-0905
Moonshot AI
Core performance indexes
LLM Stats Score
51.8
#13
21.6
#186
Reasoning
48.9
#18
21.8
#176
Coding
44.2
#5
9.7
#169
Agents
41.1
#4
-4.2
#180
Cost, coverage & limits
Benchmark wins
2 of 2
0 of 2
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

2 shared
Index
DeepSeek-V4.1-Flash
Kimi K2-Instruct-0905
Math
35.2#43
21.5#144
Tool use
34.8#2
6.6#145
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V4.1-Flash · 29 for Kimi K2-Instruct-0905

2 shared

DeepSeek-V4.1-Flash outperforms in 2 benchmarks (GPQA, Humanity's Last Exam), while Kimi K2-Instruct-0905 is better at 0 benchmarks.

DeepSeek-V4.1-Flash significantly outperforms across most benchmarks.

Sun Sep 20 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

236.8B diff

Kimi K2-Instruct-0905 has 236.8B more parameters than DeepSeek-V4.1-Flash, making it 31.0% larger.

DeepSeek
DeepSeek-V4.1-Flash
763.2Bparameters
Moonshot AI
Kimi K2-Instruct-0905
1.0Tparameters
763.2B
DeepSeek-V4.1-Flash
1000.0B
Kimi K2-Instruct-0905

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 K2-Instruct-0905
Input- tokens
Output- tokens
Sun Sep 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash supports multimodal inputs, whereas Kimi K2-Instruct-0905 does not.

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

DeepSeek-V4.1-Flash

Text
Images
Audio
Video

Kimi K2-Instruct-0905

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

MIT

Open weights

Kimi K2-Instruct-0905

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash was released on 2026-09-10, while Kimi K2-Instruct-0905 was released on 2025-09-05.

DeepSeek-V4.1-Flash is 12 months newer than Kimi K2-Instruct-0905.

DeepSeek-V4.1-Flash

Sep 10, 2026

1 weeks ago

1.0yr newer
Kimi K2-Instruct-0905

Sep 5, 2025

1.0 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 K2-Instruct-0905 side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash
✓ Preferred
Kimi K2-Instruct-0905
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash vs Kimi K2-Instruct-0905.

Which is better, DeepSeek-V4.1-Flash or Kimi K2-Instruct-0905?

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 21.6. DeepSeek-V4.1-Flash is made by DeepSeek and Kimi K2-Instruct-0905 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 K2-Instruct-0905 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 K2-Instruct-0905 scores MATH-500: 97.4%, MMLU-Redux: 92.7%, IFEval: 89.8%, AutoLogi: 89.5%, MMLU: 89.5%.

What are the context window sizes for DeepSeek-V4.1-Flash and Kimi K2-Instruct-0905?

DeepSeek-V4.1-Flash supports 1.0M tokens and Kimi K2-Instruct-0905 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 K2-Instruct-0905?

Key differences include LLM Stats Score (51.8 vs 21.6), multimodal support (yes vs no). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash and Kimi K2-Instruct-0905?

DeepSeek-V4.1-Flash is developed by DeepSeek and Kimi K2-Instruct-0905 is developed by Moonshot AI.

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