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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
88th of 442
Top 20%
Coding Index
34.6
52nd of 352
Top 15%
Math Index
59.0
117th of 268
Top 44%
Price/1M
$0.32
341st cheapest
2% above median
Top 51%
Speed
35 tok/s
Top 56%
TTFT
1.35s
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 $0.31/1M tokens.
Daily
$0.32
Monthly
$9.45
35
tokens/sec
Faster than 44% of models
1.35
seconds
Faster than 20% of models
1.35
seconds
Faster than 32% of models
Market Median
46 tok/s
22% slower
Median TTFT
0.42s
223% slower
Throughput/Dollar
113
tok/s per $/1M
Speed Comparison
Context Window
164K
tokens
Larger than 59% of models