DeepSeek-R1-0528 vs Kimi K2 Instruct
DeepSeek-R1-0528 significantly outperforms across most benchmarks. Kimi K2 Instruct is 1.8x cheaper per token.
DeepSeek · Moonshot AI · Updated for 2026
Which is better?
DeepSeek-R1-0528 outperforms in 9 benchmarks (Aider-Polyglot, AIME 2024, AIME 2025, GPQA, HMMT 2025, Humanity's Last Exam, MMLU-Pro, MMLU-Redux, SimpleQA), while Kimi K2 Instruct is better at 2 benchmarks (SWE-bench Multilingual, Terminal-Bench). DeepSeek-R1-0528 significantly outperforms across most benchmarks.
On price, Kimi K2 Instruct is roughly 1.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Kimi K2 Instruct also accepts a larger context window (200,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-R1-0528
- you want the strongest raw capability — it leads on 9 of 11 shared benchmarks
Choose Kimi K2 Instruct
- cost matters — it's about 1.8x cheaper per token
- you process long inputs — it offers a 200,000 token context window
- you want the most recent training data — it shipped Jul 2025
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1-0528 outperforms in 9 benchmarks (Aider-Polyglot, AIME 2024, AIME 2025, GPQA, HMMT 2025, Humanity's Last Exam, MMLU-Pro, MMLU-Redux, SimpleQA), while Kimi K2 Instruct is better at 2 benchmarks (SWE-bench Multilingual, Terminal-Bench).
DeepSeek-R1-0528 significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1-0528 ($0.50/1M tokens) costs the same as Kimi K2 Instruct ($0.50/1M tokens).
For output processing, DeepSeek-R1-0528 ($2.15/1M tokens) is 4.3x more expensive than Kimi K2 Instruct ($0.50/1M tokens).
In conclusion, DeepSeek-R1-0528 is more expensive than Kimi K2 Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2 Instruct has 329.0B more parameters than DeepSeek-R1-0528, making it 49.0% larger.
Context Window
Maximum input and output token capacity
Kimi K2 Instruct accepts 200,000 input tokens compared to DeepSeek-R1-0528's 131,072 tokens. Kimi K2 Instruct can generate longer responses up to 200,000 tokens, while DeepSeek-R1-0528 is limited to 131,072 tokens.
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-R1-0528 was released on 2025-05-28, while Kimi K2 Instruct was released on 2025-07-11.
Kimi K2 Instruct is 1 month newer than DeepSeek-R1-0528.
May 28, 2025
1.2 years ago
Jul 11, 2025
1.1 years ago
1mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-R1-0528 is available from DeepInfra, DeepSeek, Novita. Kimi K2 Instruct is available from Fireworks, Novita.
DeepSeek-R1-0528
Kimi K2 Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1-0528 and Kimi K2 Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1-0528 vs Kimi K2 Instruct.