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DeepSeek-V4-Flash-0731 vs Kimi K2-Instruct-0905

DeepSeek-V4-Flash-0731 leads the LLM Stats Score 44.7 to 21.6.

DeepSeek · Moonshot AI · Updated for 2026

Which is better?

DeepSeek-V4-Flash-0731 leads the overall LLM Stats Score 44.7 to 21.6, ranking #35 overall.

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

Choose DeepSeek-V4-Flash-0731

  • overall performance matters — it scores 44.7 and ranks #35 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Jul 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-Flash-0731
DeepSeek
Kimi K2-Instruct-0905
Moonshot AI
Core performance indexes
LLM Stats Score
44.7
#35
21.6
#185
Reasoning
42.3
#45
21.8
#175
Coding
33.0
#36
9.8
#168
Agents
31.2
#30
-4.1
#179
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.06 / M
— / M
Output price
$0.18 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4-Flash-0731
Kimi K2-Instruct-0905
Tool use
25.9#31
6.6#144
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

9 reported for DeepSeek-V4-Flash-0731 · 29 for Kimi K2-Instruct-0905

No common benchmarks found

DeepSeek-V4-Flash-0731 and Kimi K2-Instruct-0905don'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

696.0B diff

Kimi K2-Instruct-0905 has 696.0B more parameters than DeepSeek-V4-Flash-0731, making it 228.9% larger.

DeepSeek
DeepSeek-V4-Flash-0731
304.0Bparameters
Moonshot AI
Kimi K2-Instruct-0905
1.0Tparameters
304.0B
DeepSeek-V4-Flash-0731
1000.0B
Kimi K2-Instruct-0905

Context Window

Maximum input and output token capacity

Only DeepSeek-V4-Flash-0731 specifies input context (1,048,576 tokens). Only DeepSeek-V4-Flash-0731 specifies output context (1,048,576 tokens).

DeepSeek
DeepSeek-V4-Flash-0731
Input1,048,576 tokens
Output1,048,576 tokens
Moonshot AI
Kimi K2-Instruct-0905
Input- tokens
Output- tokens
Fri Sep 11 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-V4-Flash-0731

MIT

Open weights

Kimi K2-Instruct-0905

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V4-Flash-0731 was released on 2026-07-31, while Kimi K2-Instruct-0905 was released on 2025-09-05.

DeepSeek-V4-Flash-0731 is 11 months newer than Kimi K2-Instruct-0905.

DeepSeek-V4-Flash-0731

Jul 31, 2026

1 months ago

10mo 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-Flash-0731 and Kimi K2-Instruct-0905 side-by-side, then vote on the output you prefer.

DeepSeek-V4-Flash-0731
✓ Preferred
Kimi K2-Instruct-0905
Open in Playground

FAQ

Common questions about DeepSeek-V4-Flash-0731 vs Kimi K2-Instruct-0905.

Which is better, DeepSeek-V4-Flash-0731 or Kimi K2-Instruct-0905?

DeepSeek-V4-Flash-0731 leads the LLM Stats Score 44.7 to 21.6. DeepSeek-V4-Flash-0731 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-Flash-0731 compare to Kimi K2-Instruct-0905 in benchmarks?

DeepSeek-V4-Flash-0731 scores Terminal-Bench 2.1: 82.7%, CyberGym: 76.7%, Toolathlon: 70.3%, DSBench-FullStack: 68.7%, DSBench-Hard: 59.6%. 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-Flash-0731 and Kimi K2-Instruct-0905?

DeepSeek-V4-Flash-0731 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-Flash-0731 and Kimi K2-Instruct-0905?

Key differences include LLM Stats Score (44.7 vs 21.6). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4-Flash-0731 and Kimi K2-Instruct-0905?

DeepSeek-V4-Flash-0731 is developed by DeepSeek and Kimi K2-Instruct-0905 is developed by Moonshot AI.

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