DeepSeek: DeepSeek V3.2 Exp Rank
DeepSeek V3.2 Exp has an overall score of 67 out of 100, tied for rank 133 of 291. The published scores are price 76, context 47, and freshness 78. The overall score is their average. parameters and recency are not published and are left out of the average.
Data sourced from OpenRouter API and manual data collection | Last updated: Nov 3, 2026, 8:01 AM UTC
Component scores are ranked in
tied for rank 133 of 291
Component scores
| Score | Result | Published value | Source |
|---|---|---|---|
| Freshness | 78 | AI Knowledge Cutoff | |
| Recency | — | — | AI Release Date |
| Context | 47 | 164K tokens, output 147K | AI Context Window |
| Parameters | — | — | AI Parameter Count |
| Price | 76 | $0.305 / 1M blended, $0.27 / 1M input, $0.41 / 1M output | AI Token Price |
132 listed models have a higher overall score. Among DeepSeek models, it ranks 5 of 11.
DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
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Frequently asked questions
What is the DeepSeek V3.2 Exp rank?
DeepSeek V3.2 Exp has an overall score of 67 out of 100, tied for rank 133 of 291. The published scores are price 76, context 47, and freshness 78. The overall score is their average. parameters and recency are not published and are left out of the average.
Where does DeepSeek V3.2 Exp rank?
DeepSeek V3.2 Exp is tied for rank 133 of 291. 132 listed models have a higher overall score. Among DeepSeek models, it ranks 5 of 11.
What scores make up DeepSeek V3.2 Exp?
DeepSeek V3.2 Exp scores price 76 (AI Token Price), context 47 (AI Context Window), and freshness 78 (AI Knowledge Cutoff). The overall score of 67 is the average of those 3.