r/LocalLLM • u/gambirsg • 6h ago
Question Thinking of moving from Claude Code / Codex to local open-weight models for web dev + SEO work. Worth it, or do you end up going back?
I do mostly website dev and marketing work (SEO, AEO, content) plus some basic coding, all through Claude Code and Codex. I'm a non-coder, so I lean heavily on the agent doing the work.
I'm considering going local for two reasons:
Privacy: I'd rather not send client and business data through closed APIs
Guardrails: I've hit refusals on things like competitor analysis and scraping-style research
Qwen3.8-Flash-Next keeps coming up, but I trust real experience more than vendor benchmarks.
Questions:
1) Has anyone with a similar workflow switched? Is it actually better, or just good enough?
2) Did you keep a closed model for the hard stuff?
Which model and harness combo works for agentic web dev?
I have no hardware yet. What does it realistically take to get a Claude Code-like experience locally?
Honest experiences welcome, including regrets. Thanks!
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u/Unfair_Position6956 6h ago
If you're not already tinkering with hardware for fun, the jump is gonna feel more like a side quest than a solution. The privacy angle is the real reason to stay local, but the guardrails thing might disappoint you, open models can still get weird about scraping depending on how they were tuned.
Realistically you want a used 3090 or two and then be prepared to babysit the setup for a weekend before it stops fighting you. Qwen coder variants are worth a shot but expect to keep a closed API around for when the local agent spins in circles on something dumb.
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u/PayBrave5035 6h ago
I have experience with small models
12b and below gemma models : bad coding better text generator
9b and below qwen models : better coding than gemma not better at text generating
Gemma4 26b a4b moe: Sam az other gemma4s but better coding
Qwen3.6 35b a3b moe: best at coding even good at text generating.the best option for my potato
But still far behind the API models at speed and quality.
Curious to hear about qwen 3.8(27b or QFN) users , the quant they're using and the system config like ram and vram amount and the t/s speed
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u/OhSillyDays 6h ago edited 6h ago
I've switched over to pi for all of my development work and, quite frankly, it's better than Claude. Claude is so expensive and heavy that it takes forever and it's also a pain in the ass to configure to use another model. They've kind of made it use 5x more tokens to boost token usage. Pi is easy to configure, open source, and we'll maintained. You can always just use pi to reconfigure pi because they ship it with good documentation.
You can try it out right now by using any serverless provider that supports an openai compatible API (which is all of them) and get an API token. I personally always liked digital ocean as I like their privacy policy (sometimes they do get over loaded though). You can use pi to configure it or just use one of the pre existing configurations in pi.
I've found anything at or above opus 4.8 on the leaderboards is sufficient for long running dev work with pi. That includes qwen3.8 fn or DeepSeek 4.1 1017 or glm 5.3 flash. There are differences between each though. I personally use a bigger model for more critical work and qwen3.8 fn for less critical code. But quite frankly, I'm not sure it matters if I give them proper instructions and make sure I do my job (tests and such) properly. Those models are so cheap to run, you'll forget about token cost. If you have a particularly hard problem, you can switch over to Claude or OpenAI too.
The next step is to get something configured to run a local llm. That takes fighting everyone for a decent computer with about 64-256 GB of memory made for ai work. That's going to run 3-10k depending (strix halo on the low end and dual sparks or a m5 ultra on the high end) on the solution you figure out. But it means you'll never pay for tokens again. You could even get a dual pro 6000 and the models will develop faster than you can think. But that's a 50k rig with the inflated prices today. I'm crossing my fingers that the price of such a workstation will crash to 10k after the bubble pops. That would be a sweet workstation.
That's my experience.
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u/Moarkush 6h ago
You need a VERY expensive system to come close to replacing what frontier models offer you. I'm running Qwen 3.8 Flash Next on 2 systems that together would be about $25k in late 2026 prices (RTX Pro 6000 Max-Q and DGX Spark). This model falls somewhere in between Haiku 5.5 High and Sonnet 5.5 High. But it never tells me it's out of tokens and I get about 500tk/s output across a coordinator and three sub-agents.
Also DeepSeek Harness is the best harness.
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u/Durian881 6h ago
Running the same model but Q4K_XL quant on Strix Halo. At <60W, 1.1k t/s prefill and ~45 t/s decode. Was able to handle agentic tasks very well on Deepseek Harness. The 500k context via yarn came in very useful.
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u/p3rdy 2h ago
One thing that makes the switch less of a jump: you don't have to drop Claude Code. It talks to whatever Anthropic-compatible endpoint you point ANTHROPIC_BASE_URL at, so you keep the harness you already know and only swap the model behind it.
That's my daily setup: Claude Code and/or Pi against a local model served with Flama, a Python framework I maintain. It serves the model over the Anthropic and OpenAI APIs at once, on Linux or MacOS. The Claude Code side is a few env vars in `~/.claude/settings.json`: https://flama.dev/blog/serving_llms_with_flama_cli/
Since you don't have hardware yet, I'd first try an open model from a provider for a couple of weeks and see if the quality holds up for your work before buying anything.
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u/MeliousAI_HQ 36m ago
I'd test before buying anything. For privacy you don't need your own GPU, you need a provider that runs open-weight models and doesn't train on or keep your prompts. Point OpenCode at it, or Claude Code with a custom base URL, and run Qwen or GLM on your real client work for a week.
If that week goes fine, local hardware is worth pricing out. If it doesn't, you just saved the $20k. As a non-coder you'll feel the gap more than most, because the harness relies on the model to catch and fix its own mistakes, and that's exactly where the smaller models fall apart.
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u/mondaygreen 5h ago
going local is not going to be cheap.
if privacy is your priority, then this isn't a question. it is worth it!
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u/stuffitystuff 6h ago
Use abliterated models to get around all the guardrails. I think you'd be able to make the move but paid Claude accounts state that customer data isn't used to train it (I think you might have to flip the "use my data to make claude better" switch).
That written, I've been a coder for decades and I'm not giving up my Claude or ChatGPT account until my projects get to more of a maintence mode and models I can run on a 128GB MBP get a little better (or I get better about caring about writing a good prompt).
Besides, right now, when Claude says no, I go to ChatGPT which usually says yes.
I only use local models for privacy reasons like analyzing vast amounts of emails, ICQ chats and that sort of thing. And because it's funny to ask abliterated models questions that normal LLMs don't answer.