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DeepSeek-V4-Flash-0731 vs Kimi-k1.5

DeepSeek-V4-Flash-0731 leads the LLM Stats Score 44.7 to 17.2.

DeepSeek · Moonshot AI · Updated for 2026

Which is better?

DeepSeek-V4-Flash-0731 leads the overall LLM Stats Score 44.7 to 17.2, 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 — it leads those capability indexes
  • you want the most recent training data — it shipped Jul 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-Flash-0731
DeepSeek
Kimi-k1.5
Moonshot AI
Core performance indexes
LLM Stats Score
44.7
#35
17.2
#217
Reasoning
42.3
#45
16.4
#214
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.06 / M
— / M
Output price
$0.18 / M
— / M
Context window
1,048,576
—

Individual benchmarks

9 reported for DeepSeek-V4-Flash-0731 · 9 for Kimi-k1.5

No common benchmarks found

DeepSeek-V4-Flash-0731 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-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-k1.5
Input- tokens
Output- tokens
Thu Sep 10 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Kimi-k1.5 supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 does not.

Kimi-k1.5 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4-Flash-0731

Text
Images
Audio
Video

Kimi-k1.5

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4-Flash-0731 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-Flash-0731

MIT

Open weights

Kimi-k1.5

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4-Flash-0731 was released on 2026-07-31, while Kimi-k1.5 was released on 2025-01-20.

DeepSeek-V4-Flash-0731 is 19 months newer than Kimi-k1.5.

DeepSeek-V4-Flash-0731

Jul 31, 2026

1 months ago

1.5yr 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-Flash-0731 and Kimi-k1.5 side-by-side, then vote on the output you prefer.

DeepSeek-V4-Flash-0731
✓ Preferred
Kimi-k1.5
Open in Playground

FAQ

Common questions about DeepSeek-V4-Flash-0731 vs Kimi-k1.5.

Which is better, DeepSeek-V4-Flash-0731 or Kimi-k1.5?

DeepSeek-V4-Flash-0731 leads the LLM Stats Score 44.7 to 17.2. DeepSeek-V4-Flash-0731 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-Flash-0731 compare to Kimi-k1.5 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-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-Flash-0731 and Kimi-k1.5?

DeepSeek-V4-Flash-0731 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-Flash-0731 and Kimi-k1.5?

Key differences include LLM Stats Score (44.7 vs 17.2), multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4-Flash-0731 and Kimi-k1.5?

DeepSeek-V4-Flash-0731 is developed by DeepSeek and Kimi-k1.5 is developed by Moonshot AI.

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