Qwen vs Claude isn't a scorecard for me, it's a daily decision. For routine work, and for anything that shouldn't leave my own network for privacy reasons, I run the terminal agent Pi against a local Qwen3.8-27B on my DGX Spark. For complex refactorings and long agent runs with many steps, Claude Code stays my first choice. Both run side by side for me, neither fully replaces the other, and that's the honest answer to a question clients ask me more often now.
I'm not judging this from theory. My own AI workforce of eight AI employees runs entirely on Claude, that's my production environment. In parallel, I've been running Qwen3.8 under SGLang on my own hardware for a few weeks, to give clients who can't use the cloud a solid answer instead of a guess. Everything I write about local AI and Claude is collected on the AI page.
You won't get a benchmark with percentages here. The English-language comparison sites on this keyword mostly pit two cloud models against each other, with no test environment of their own, no prices, no real tasks. A locally running Qwen against Claude Code on the same task doesn't show up in any of them, so what follows is my own experience, clearly marked as such.
Qwen vs Claude: two categories that don't compare directly
Qwen3.8-27B is an open, dense model that I run locally under SGLang as an NVFP4 checkpoint, with 262,000 tokens of native context, tool calling, thinking, and vision. Inference runs entirely on my own hardware, with no call to a vendor API and no bill per request. Tools an agent uses alongside it are a separate matter and can still reach the internet. Claude, on the other hand, is a proprietary frontier model from Anthropic that I use as a cloud service through a subscription or API. The two have little in common as a basis for comparison beyond a similar context window: one is a device on my desk, the other a service Anthropic runs and maintains.
That's exactly why "Qwen vs Claude" is less a question of which model ranks higher on some leaderboard for me, and more a question of which tool fits which task. A comparison aimed at a single model like Opus falls short here: I test against Claude Code as a whole system of agent, tools, and model, not against one specific model version.
How I compare: Pi against Qwen3.8, Claude Code alongside
My setup is deliberately symmetric: on one side, the terminal coding agent Pi (pi.dev) runs against my local SGLang server with Qwen3.8-27B, on the other side, the same kind of task runs in Claude Code, which my AI workforce uses every day. Both get comparable work drawn from my own day-to-day, from small scripts to code research to changes touching several files at once.
Pi brings enough tooling to the comparison: it reads, writes, and edits files, runs commands, and searches through a project. It picks up AGENTS.md and CLAUDE.md from the project, has skills, sessions, and subagents, offers a plan mode and a todo list, connects through MCP adapters, and can search the web. One difference is worth knowing before you point it at a real project: Pi has no permission system, it goes ahead and does what it decides to do, without asking first.
What I deliberately don't do is compute a score from five tasks and sell it as a percentage. My sample is too small and too tied to my own workflow to pass off as a general truth. What I do collect instead is a recurring pattern that has held for weeks now.
Where Claude stays ahead for me: complex refactorings, long agent runs
For tasks that need many steps while keeping track of several files at once, say a rename that ripples through an entire project, or a refactoring that has to respect dependencies between modules, Claude Code stays ahead for me. The agent holds context more reliably across many turns, loses the thread less often on tasks with several intermediate steps, and asks a sensible clarifying question more often instead of a confident but wrong assumption.
That's also why my entire AI workforce runs on Claude: for productive work with real client tasks, I want that reliability across long runs, not just on the first request. Local models like Qwen3.8 have kept up in my tests on short, clearly scoped tasks, but on long, nested agent runs the gap to Claude Code has been clearer for me.
Where Qwen3.8 is enough for me: routine work, privacy, offline operation
On the other side, there's a whole category of tasks where Qwen3.8 under Pi is entirely enough for me: individual scripts, formatting changes, looking up documentation, smaller pieces of code that don't need much context from the rest of the project. Here I notice no disadvantage in daily use compared to Claude Code that would justify the wait.
More important than raw task complexity, though, is a second point: as soon as data can't leave my own network for legal or contractual reasons, or there's simply no stable internet connection where the work happens, Qwen3.8 wins automatically, regardless of how fast or slow it is next to Claude. For clients who need exactly that, a hard offline requirement, a locally running model like Qwen3.8 is the only option on the table in the first place. I describe my setup and the measured speeds in Qwen 3.8 Locally: DGX Spark Experience.
Cost comparison: subscription price against power and hardware
On cost, I deliberately don't compare number against number, because the two are billed on completely different principles. Claude runs on a subscription for me, with clear tiers according to the existing post Claude Pricing and Plans Explained: Free at no cost, Pro 20 dollars a month, Max 100 or 200 dollars a month, Team from 25 dollars per person, as of September 2026. That's a running, predictable bill that stays roughly constant with usage.
Qwen3.8 on the DGX Spark runs with no bill per request and no subscription with Anthropic, but I put money into the hardware upfront and into electricity on an ongoing basis. The investment sits at the start, not on every single request, that's the fundamental difference from the subscription model. Economically this pays off for me mainly because I'd need the hardware for other purposes anyway, in my case for testing and for local speech recognition, not just for this one use case.
| Criterion |
Qwen3.8-27B local (SGLang, DGX Spark) |
Claude (subscription) |
| License and access |
open model, own hardware |
proprietary model, cloud service |
| Billing |
no ongoing bill, power and hardware upfront |
Free 0, Pro 20, Max 100 or 200, Team from 25 dollars a month |
| Internet required |
no, inference runs locally |
yes, cloud service |
| Strong for me at |
routine work, privacy cases, offline operation |
complex refactorings, long agent runs |
How I split the work today
In practice that means: if a task doesn't run smoothly under Pi against Qwen3.8, I switch that same task to Claude Code rather than losing time cleaning up the local result. Conversely, I now deliberately start routine tasks locally first, because my hit rate there is high enough to reserve Claude for the cases where it really makes a difference. This isn't a fixed rulebook, it's a judgment call that shifts with every new Qwen release and every new Claude model.
Frequently asked questions
Is Qwen 3.8 as good as Claude?
On short, clearly scoped tasks I barely notice a difference in daily use. On long agent runs with many intermediate steps and several affected files, Claude Code stays noticeably ahead for me. I can't draw a general ranking from that, only my own experience over several weeks.
How does Qwen compare specifically to Claude Opus?
I honestly can't say much here: I test against Claude Code as a system of agent and tools, not specifically against a single model like Opus. A clean Qwen-versus-Opus comparison, isolated from the rest of the agent setup, is something I still owe you.
Does Ollama with Qwen replace the detour through Claude entirely?
Not for me, and technically my setup doesn't run through Ollama either, it runs through SGLang, because I wanted an OpenAI- and Anthropic-compatible API plus speculative decoding. Ollama is installed on my machine, but with no models loaded. As a simpler entry point for others, Ollama is still a reasonable option, just with less throughput than my SGLang setup.
What does a local LLM cost compared to Claude over time?
Without an invented number, I can only give you the structure: a local LLM like Qwen3.8 costs hardware upfront and electricity ongoing, but no bill per request and no subscription. Claude costs a predictable monthly subscription per Claude Pricing and Plans Explained, without the hardware or maintenance on your side. Which path is cheaper depends on your own usage, which I won't put a blanket number on here.
Who is the local route with Qwen and Pi worth it for?
In my experience, mainly for cases with a hard privacy or offline requirement, and for routine tasks where Claude Code's speed doesn't make the difference. For anything that needs complex reasoning across many files and steps, Claude Code stays the more reliable choice for me.
How to compare it yourself
If you want to try this out yourself: start with a task you already do regularly, run it once under Pi against a local model and once against Claude Code, and compare not just the result but how often you have to fix it up afterward. I describe my own Qwen3.8 setup step by step in Qwen 3.8 Locally: DGX Spark Experience, and my experience with Pi as a cloud-free Claude Code alternative in Claude Code Alternative Without Cloud. If you'd rather see two cloud models pitted against each other, that's in Claude vs ChatGPT 2026: Compared.
Official background on the tools I use in this setup is at Anthropic, in the SGLang project on GitHub, and directly at Pi.
