DeepSeek-V4.1-Flash vs Kimi K2 0905
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 21.6. DeepSeek-V4.1-Flash is 3.3x cheaper per token.
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 #12 overall.
In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V4.1-Flash is roughly 3.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash also accepts a larger context window (1,040,000 input tokens), making it the stronger choice for long documents and large codebases.
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 and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 3.3x cheaper per token
- you process long inputs — it offers a 1,040,000 token context window
- you want the most recent training data — it shipped Sep 2026
- you need open weights you can self-host or fine-tune
Choose Kimi K2 0905
- you want predictable pricing at $0.60/M input and $2.50/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for DeepSeek-V4.1-Flash · 6 for Kimi K2 0905
DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while Kimi K2 0905 is better at 0 benchmarks.
DeepSeek-V4.1-Flash significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4.1-Flash ($0.22/1M tokens) is 2.7x cheaper than Kimi K2 0905 ($0.60/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 3.8x cheaper than Kimi K2 0905 ($2.50/1M tokens).
In conclusion, Kimi K2 0905 is more expensive than DeepSeek-V4.1-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2 0905 has 236.8B more parameters than DeepSeek-V4.1-Flash, making it 31.0% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash accepts 1,040,000 input tokens compared to Kimi K2 0905's 262,144 tokens. DeepSeek-V4.1-Flash can generate longer responses up to 393,216 tokens, while Kimi K2 0905 is limited to 262,144 tokens.
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash supports multimodal inputs, whereas Kimi K2 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
Kimi K2 0905
License
Usage and distribution terms
DeepSeek-V4.1-Flash is licensed under MIT, while Kimi K2 0905 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V4.1-Flash was released on 2026-09-10, while Kimi K2 0905 was released on 2025-09-05.
DeepSeek-V4.1-Flash is 12 months newer than Kimi K2 0905.
Sep 10, 2026
0 days ago
1.0yr newerSep 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.
Provider Availability
DeepSeek-V4.1-Flash is available from Fireworks, DeepInfra, DeepSeek, Novita. Kimi K2 0905 is available from Novita.
DeepSeek-V4.1-Flash
Kimi K2 0905
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Kimi K2 0905 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Kimi K2 0905.