{
  "id": 16058122,
  "title": "DeepSeek V4.1-Flash-1017 vs. Claude Haiku 5.5: More accurate at a fraction of the price",
  "url": "https://urgent.news/2026/10/22/deepseek-v4-1-flash-1017-vs-claude-haiku-5-5-more-accurate-at-a",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-22T14:22:50.000Z",
  "source": {
    "name": "The New Stack",
    "slug": "the-new-stack",
    "url": "https://thenewstack.io/deepseek-flash-haiku-test/"
  },
  "original_language": "en",
  "account": "On October 17, DeepSeek unveiled V4.1-Flash-1017, an upgrade to their Flash model, with the update promising to reduce task runtime and output length. This new model can be accessed via DeepSeek’s API or a service such as OpenRouter, and those with the technical know-how can even download it from Hugging Face and run it on their own servers. For a hands-on comparison, I turned to V4.1-Flash-1017, testing it alongside Anthropic’s Claude Haiku 5.5, which was launched on October 7 as their fastest model for standard tasks like classification, summarization, and database queries. While DeepSeek's model card compared V4.1-Flash-1017 to other rivals, it did not directly contrast it with Haiku 5.5; thus, I selected Haiku 5.5 for this comparison. Anthropic claims that their Opus models, of which Haiku 5.5 is one, operate faster in Fast Mode. The models were pitted against each other in three tests, chosen to highlight each company’s strengths. In the first test, an agentic bug fix involving a Python job-queue library with three bugs and one incorrect test, DeepSeek V4.1-Flash-1017 achieved full accuracy, scoring 6 out of 6. Haiku 5.5, however, scored 5 out of 6, missing one of six hidden tests. In the second test, a database query involving a fake CI platform database with 20 questions, both models scored perfectly, 20 out of 20, catching every trap and refusing to hallucinate on the two questions that fell outside the data’s scope. Finally, in the triage task, which involved sorting 400 developer error reports into categories, severity, service, and team, DeepSeek V4.1-Flash-1017 scored 100% accuracy, correctly categorizing all 1,600 fields. Haiku 5.5, on the other hand, managed 1,596 out of 1,600 fields correctly, missing two severity ratings. The cost comparison revealed a significant disparity. With DeepSeek's off-peak rates at $0.15 per million uncached input tokens, $0.003 per million cached input tokens, and $0.60 per million output tokens, the total cost for the tests came to $0.07. In contrast, Haiku 5.5 charged $0.10 per million input tokens and $0.50 per million output tokens, resulting in a total cost of $0.30 for the same tests. While Haiku 5.5 demonstrated slightly faster performance in tool-based tests, DeepSeek V4.1-Flash-1017 was the faster model in the longer triage prompt test. Therefore, for these three tests, I would recommend DeepSeek V4.1-Flash-1017 due to its superior accuracy across all tasks and lower cost, particularly when the prompt exceeds 100,000 tokens.",
  "summary": "DeepSeek released V4.1-Flash-1017 on October 17 as an update to its Flash model. Its change log says the update cuts The post DeepSeek V4.1-Flash-1017 vs. Claude Haiku 5.5: More accurate at a fraction of the price appeared first on The New Stack .",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}