Model Comparison

DeepSeek-V3.2 (Thinking) vs Kimi K2 0905Which is better in 2026?

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. DeepSeek-V3.2 (Thinking) is 3.4x cheaper per token.

Verdict: DeepSeek-V3.2 (Thinking) vs Kimi K2 0905 — which is better?

DeepSeek-V3.2 (Thinking) (by DeepSeek) and Kimi K2 0905 (by Moonshot AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

DeepSeek-V3.2 (Thinking) outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Kimi K2 0905 is better at 0 benchmarks. DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

On price, DeepSeek-V3.2 (Thinking) is roughly 3.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Kimi K2 0905 also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.

Choose DeepSeek-V3.2 (Thinking) if…

  • you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
  • cost matters — it's about 3.4x cheaper per token
  • you want the most recent training data — it shipped Dec 2025
  • you need open weights you can self-host or fine-tune

Choose Kimi K2 0905 if…

  • you process long inputs — it offers a 262,144 token context window
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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek-V3.2 (Thinking) outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Kimi K2 0905 is better at 0 benchmarks.

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

Sun Jun 21 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek-V3.2 (Thinking) costs less

For input processing, DeepSeek-V3.2 (Thinking) ($0.28/1M tokens) is 2.1x cheaper than Kimi K2 0905 ($0.60/1M tokens).

For output processing, DeepSeek-V3.2 (Thinking) ($0.42/1M tokens) is 6.0x cheaper than Kimi K2 0905 ($2.50/1M tokens).

In conclusion, Kimi K2 0905 is more expensive than DeepSeek-V3.2 (Thinking).*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Sun Jun 21 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2 (Thinking)
Input tokens$0.28
Output tokens$0.42
Best providerDeepSeek
Moonshot AI
Kimi K2 0905
Input tokens$0.60
Output tokens$2.50
Best providerNovita
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

315.0B diff

Kimi K2 0905 has 315.0B more parameters than DeepSeek-V3.2 (Thinking), making it 46.0% larger.

DeepSeek
DeepSeek-V3.2 (Thinking)
685.0Bparameters
Moonshot AI
Kimi K2 0905
1.0Tparameters
685.0B
DeepSeek-V3.2 (Thinking)
1000.0B
Kimi K2 0905

Context Window

Maximum input and output token capacity

Kimi K2 0905 accepts 262,144 input tokens compared to DeepSeek-V3.2 (Thinking)'s 131,072 tokens. Kimi K2 0905 can generate longer responses up to 262,144 tokens, while DeepSeek-V3.2 (Thinking) is limited to 65,536 tokens.

DeepSeek
DeepSeek-V3.2 (Thinking)
Input131,072 tokens
Output65,536 tokens
Moonshot AI
Kimi K2 0905
Input262,144 tokens
Output262,144 tokens
Sun Jun 21 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.2 (Thinking) 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.

DeepSeek-V3.2 (Thinking)

MIT

Open weights

Kimi K2 0905

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while Kimi K2 0905 was released on 2025-09-05.

DeepSeek-V3.2 (Thinking) is 3 months newer than Kimi K2 0905.

DeepSeek-V3.2 (Thinking)

Dec 1, 2025

6 months ago

2mo newer
Kimi K2 0905

Sep 5, 2025

9 months 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

Provider Availability

DeepSeek-V3.2 (Thinking) is available from DeepSeek. Kimi K2 0905 is available from Novita.

DeepSeek-V3.2 (Thinking)

deepseek logo
DeepSeek
Input Price:Input: $0.28/1MOutput Price:Output: $0.42/1M

Kimi K2 0905

novita logo
Novita
Input Price:Input: $0.60/1MOutput Price:Output: $2.50/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Less expensive input tokens
Less expensive output tokens
Has open weights
Higher GPQA score (82.4% vs 75.8%)
Higher MMLU-Pro score (85.0% vs 82.5%)
Moonshot AI
Larger context window (262,144 tokens)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2 (Thinking)
Moonshot AI
Kimi K2 0905

FAQ

Common questions about DeepSeek-V3.2 (Thinking) vs Kimi K2 0905.

Which is better, DeepSeek-V3.2 (Thinking) or Kimi K2 0905?

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. DeepSeek-V3.2 (Thinking) is made by DeepSeek and Kimi K2 0905 is made by Moonshot AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V3.2 (Thinking) compare to Kimi K2 0905 in benchmarks?

DeepSeek-V3.2 (Thinking) scores AIME 2025: 93.1%, HMMT 2025: 90.2%, MMLU-Pro: 85.0%, LiveCodeBench: 83.3%, GPQA: 82.4%. Kimi K2 0905 scores HumanEval: 94.5%, MMLU: 90.2%, MATH: 89.1%, MMLU-Pro: 82.5%, GPQA: 75.8%.

Is DeepSeek-V3.2 (Thinking) cheaper than Kimi K2 0905?

DeepSeek-V3.2 (Thinking) is 2.1x cheaper for input tokens. DeepSeek-V3.2 (Thinking) costs $0.28/M input and $0.42/M output via deepseek. Kimi K2 0905 costs $0.60/M input and $2.50/M output via novita.

What are the context window sizes for DeepSeek-V3.2 (Thinking) and Kimi K2 0905?

DeepSeek-V3.2 (Thinking) supports 131K tokens and Kimi K2 0905 supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3.2 (Thinking) and Kimi K2 0905?

Key differences include context window (131K vs 262K), input pricing ($0.28 vs $0.60/M), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2 (Thinking) and Kimi K2 0905?

DeepSeek-V3.2 (Thinking) is developed by DeepSeek and Kimi K2 0905 is developed by Moonshot AI.

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