DeepSeek Search ranks 17th on Artificial Analysis Search Index
The headline price is low, but the benchmark shows the real cost lives in tokens: ~7k-token search results make DeepSeek's model cost the 2nd highest on the board at ~US$0.023 per task. Developers building search-augmented agents should weigh that against the weak BrowseComp score of 63%, which trails Perplexity variants and Octen at 85% to 87% on multi-hop tasks. Compared with alternatives like Exa and Firecrawl, DeepSeek wins on price, not on factual accuracy.
Artificial Analysis benchmarked DeepSeek Search (launched 17 October) in its Stirrup agent harness using GPT-5.6 Luna (medium) as the calling model. DeepSeek Search scored 69 on the Artificial Analysis Search Index, ranking 17th of 27 products, with its best result on AA-Omniscience (70% accuracy) and its worst on BrowseComp (63%, 25th of 27). At US$0.75 per 1,000 searches, it is the cheapest paid Search API on the board, below Octen and Parallel at US$1, and costs about US$0.032 per task, 5th cheapest overall. Limitations include large search results of about 7k tokens per query and a 128-character query limit that rejected 6% of searches.
DeepSeek Search debuts on the Artificial Analysis Search Index at 69, 17th of 27 search products, at the lowest price of any paid Search API on the board
@deepseek_ai launched its Search API on 17 October. We benchmarked it in our Stirrup agent harness, where GPT-5.6 Luna (medium) calls DeepSeek Search as its web_search tool to complete a combination of tasks from BrowseComp, DeepSearchQA and AA-Omniscience.
At $0.75 per 1k searches, DeepSeek Search costs less per search than any other paid Search API we benchmark, below Octen, Parallel (turbo) and Parallel (fast) at $1. At about $0.032 per task, it is the 5th cheapest of 27 products, cheaper than 20 of the other 25 Search APIs (median $0.066) and level with Nimble (lite). TinyFish, Octen, Parallel (turbo) and Parallel (fast) cost less.
Key benchmarking results for DeepSeek Search:
➤ Artificial Analysis Search Index: DeepSeek Search scores 69, a 36-point lift over the same model with no search (33). It sits between TinyFish at 71 and Linkup (standard) at 68
➤ AA-Omniscience is DeepSeek Search's strongest benchmark: it scores 70% accuracy on our proprietary factual answer benchmark. This ranks 10th of 27, 3 points behind the joint leaders, Exa (auto, dynamic highlights) and Firecrawl (73%). BrowseComp, which needs multi-hop reasoning across several searches, is its weakest benchmark: at 63% it ranks 25th of 27, well behind the Perplexity variants and Octen (85% to 87%)
➤ Large search results: each search returns about 7k tokens, near the top of the board's range. Each result carries about 1.8k characters of passages from the page. This raises model cost: at about $0.023 per task, DeepSeek's model cost is the 2nd highest on the board, behind Linkup (flash, alpha) at $0.039
➤ 128-character query limit: DeepSeek rejects queries longer than 128 characters. 6% of the agent's searches hit this limit, and for 87% of these, the model's first shorter rewrite worked
Key details:
➤ Setup: GPT-5.6 Luna (medium) in Stirrup, DeepSeek Search API with need_content=false (page passages, no full page content) and 10 results per search
➤ Pricing: $0.75 per 1k successful searches. DeepSeek does not bill empty results
➤ Rate limit: 5 searches per second per account
