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DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism that reduces training and inference cost while preserving quality in long-context scenarios. A scalable reinforcement learning post-training framework further improves reasoning, with reported performance in the GPT-5 class, and the model has demonstrated gold-medal results on the 2025 IMO and IOI. V3.2 also uses a large-scale agentic task synthesis pipeline to better integrate reasoning into tool-use settings, boosting compliance and generalization in interactive environments. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)
Quality Index
32.1
89th of 444
Top 20%
Coding Index
34.6
53rd of 354
Top 15%
Math Index
59.0
117th of 268
Top 44%
Price/1M
$0.32
347th cheapest
5% above median
Top 51%
Speed
33 tok/s
Top 56%
TTFT
1.55s
Context Window
164K
135th largest
Top 41%
Input
$0.28
per 1M tokens
Output
$0.42
per 1M tokens
Blended
$0.32
per 1M tokens
Cheaper than 49% of models. Median price is $0.30/1M tokens.
Daily
$0.32
Monthly
$9.45
33
tokens/sec
Faster than 44% of models
1.55
seconds
Faster than 18% of models
1.55
seconds
Faster than 31% of models
Market Median
45 tok/s
27% slower
Median TTFT
0.42s
271% slower
Throughput/Dollar
105
tok/s per $/1M
Speed Comparison
Context Window
164K
tokens
Larger than 59% of models