Claude Haiku 5.5 Vs Qwen 27B: Is Local Worth It?

Claude Haiku 5.5 vs Qwen 27B: AI pricing, speed, and when local LLMs save money, or waste it. Claude Haiku 5.5 costs $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100,000 tokens, a tenth of Haiku 4.5's price. On Artificial Analysis's ten-test Intelligence Index, Haiku 5.5 at medium effort (its default) ties Qwen 3.8 27B at Qwen's default top setting (xhigh): 34 to 34. On Terminal-Bench 4.0, which tests coding agents inside a terminal, Haiku solves 15% of tasks at medium and 33% at max; Qwen solves 6%. An average benchmark task costs about $0.05 on Haiku at medium. Rented Qwen on Alibaba's API costs $1.01 for the same average task: Alibaba bills $3 per million output tokens (six times Haiku's rate), and Qwen generated nearly 200M tokens across the benchmark versus Haiku's 54M. Decode time per task: about 2 minutes on Haiku, about 18 minutes for Qwen on Alibaba. By our arithmetic from community measurements, a four-bit Qwen on an RTX 5090 costs roughly 1, 2 cents of card electricity per task and takes 5, 11 minutes, depending on whether llama.cpp's MTP flag is on. A new $1,999 RTX 5090 needs about 49,000, 63,000 tasks, 6, 16 months of nonstop running, to pay for itself against Haiku. Max subscribers also get $100, $200 a month in API credits (usable in the Claude API and Agent SDK, not inside Claude Code). Local still makes sense if your code can't leave your machine, if you already own a 24, 32 GB card, or for sessions past 100,000 tokens, where Haiku's price jumps fivefold; according to a community tester, Qwen's full 262K context fits a 24GB card with a compressed cache. The verdict: as a way to save money, no. Use Haiku 5.5 at medium for everyday coding agents; keep Qwen on a card you already own for private code, long sessions and overnight batch jobs, with the MTP flag on; and send the hardest agentic bugs to Sonnet or Opus. Chapters: 0:00 Intro 1:38 Same Tests 4:03 Cost Per Task 7:57 Waiting Time 10:54 Local Wins 13:12 Verdict Tools & resources mentioned: - Claude Haiku 5.5: https://www.anthropic.com/claude-haiku-5-5 - Claude API Pricing: https://platform.claude.com/docs/en/about-claude/pricing - Claude Effort Levels: https://platform.claude.com/docs/en/build-with-claude/effort - Anthropic Model Deprecations: https://platform.claude.com/docs/en/about-claude/model-deprecations - Artificial Analysis Intelligence Benchmarking: https://artificialanalysis.ai/methodology/intelligence-benchmarking - Qwen 3.8 27B on Artificial Analysis: https://artificialanalysis.ai/models/qwen3-8-27b - Haiku 5.5 (Max) vs Qwen 3.8 27B, Artificial Analysis: https://artificialanalysis.ai/models/comparisons/claude-haiku-5-5-vs-qwen3-8-27b - Haiku 5.5 (Medium) vs Qwen 3.8 27B, Artificial Analysis: https://artificialanalysis.ai/models/comparisons/claude-haiku-5-5-medium-vs-qwen3-8-27b - llama.cpp: https://github.com/ggml-org/llama.cpp - qwen38-mtp community benchmarks: https://github.com/sudoingX/qwen38-mtp About The Stack The Stack helps you build with AI. Each video takes one tool, model, or workflow and shows how it works in a few focused minutes, with the real benchmarks and real costs. We go deep on Claude Code and Cursor for AI coding, AI agents and MCP servers, the open-source AI tools and GitHub repos most people miss, RAG and vector search, fine-tuning, and running local LLMs on your own machine with Ollama and LM Studio. We compare models like ChatGPT and Claude, test AI automation with Zapier, Make, and n8n, and flag the tools that actually ship. Subscribe for new breakdowns: https://www.youtube.com/@the-stack-ai?sub_confirmation=1 #claude haiku 5.5 #qwen 27b #local llm