Model Comparison

DeepSeek-V3.2-Exp vs Kimi K2 0905

DeepSeek-V3.2-Exp significantly outperforms across most benchmarks. DeepSeek-V3.2-Exp is 3.5x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

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

DeepSeek-V3.2-Exp significantly outperforms across most benchmarks.

Fri Apr 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek-V3.2-Exp costs less

For input processing, DeepSeek-V3.2-Exp ($0.27/1M tokens) is 2.2x cheaper than Kimi K2 0905 ($0.60/1M tokens).

For output processing, DeepSeek-V3.2-Exp ($0.41/1M tokens) is 6.1x cheaper than Kimi K2 0905 ($2.50/1M tokens).

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

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

Lowest available price from all providers
Fri Apr 17 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2-Exp
Input tokens$0.27
Output tokens$0.41
Best providerNovita
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-Exp, making it 46.0% larger.

DeepSeek
DeepSeek-V3.2-Exp
685.0Bparameters
Moonshot AI
Kimi K2 0905
1000.0Bparameters
685.0B
DeepSeek-V3.2-Exp
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-Exp's 163,840 tokens. Kimi K2 0905 can generate longer responses up to 262,144 tokens, while DeepSeek-V3.2-Exp is limited to 65,536 tokens.

DeepSeek
DeepSeek-V3.2-Exp
Input163,840 tokens
Output65,536 tokens
Moonshot AI
Kimi K2 0905
Input262,144 tokens
Output262,144 tokens
Fri Apr 17 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.2-Exp 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-Exp

MIT

Open weights

Kimi K2 0905

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V3.2-Exp was released on 2025-09-29, while Kimi K2 0905 was released on 2025-09-05.

DeepSeek-V3.2-Exp is 1 month newer than Kimi K2 0905.

DeepSeek-V3.2-Exp

Sep 29, 2025

6 months ago

3w newer
Kimi K2 0905

Sep 5, 2025

7 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-Exp is available from Novita. Kimi K2 0905 is available from Novita.

DeepSeek-V3.2-Exp

novita logo
Novita
Input Price:Input: $0.27/1MOutput Price:Output: $0.41/1M

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

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Key Takeaways

DeepSeek
Less expensive input tokens
Less expensive output tokens
Has open weights
Higher GPQA score (79.9% 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-Exp
Moonshot AI
Kimi K2 0905

FAQ

Common questions about DeepSeek-V3.2-Exp vs Kimi K2 0905

DeepSeek-V3.2-Exp significantly outperforms across most benchmarks. DeepSeek-V3.2-Exp 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.
DeepSeek-V3.2-Exp scores SimpleQA: 97.1%, AIME 2025: 89.3%, MMLU-Pro: 85.0%, HMMT 2025: 83.6%, GPQA: 79.9%. Kimi K2 0905 scores HumanEval: 94.5%, MMLU: 90.2%, MATH: 89.1%, MMLU-Pro: 82.5%, GPQA: 75.8%.
DeepSeek-V3.2-Exp is 2.2x cheaper for input tokens. DeepSeek-V3.2-Exp costs $0.27/M input and $0.41/M output via novita. Kimi K2 0905 costs $0.60/M input and $2.50/M output via novita.
DeepSeek-V3.2-Exp supports 164K tokens and Kimi K2 0905 supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (164K vs 262K), input pricing ($0.27 vs $0.60/M), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3.2-Exp is developed by DeepSeek and Kimi K2 0905 is developed by Moonshot AI.