TLDR: I use Orca to orchestrate multiple DSV4.1 flash 1017 agents instead of using an expensive model and the results have been phenomenal. I have 2 setups:

1. Cheapest: DeepSeek V4.1 Flash 1017 orchestrating DeepSeek workers through OpenCode Go ($10/month for $80 of usage). Good for everyday coding, bug fixes, and extending existing apps.
2. Favorite: Claude Opus 5.5 orchestrating DeepSeek V4.1 Flash workers, with Sonnet 5.5 on UI/UX. Good for new features and complex work.

I also use obra's Superpowers and Codebase Memory so agents understand the codebase, plan properly, and verify their work.

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A few days ago I posted about how much I was loving DeepSeek V4.1 Flash 1017 and mentioned I was using it as a worker in Orca. I didn't expect that post to get so much attention and I see a lot of you asked how I set things up, what Orca actually does, and what it costs. So I thought I would write about it in detail and give you the prompt I use and the setup I use as well.

So here are the two setups I've been using. Both have worked well for me.

For context: I've been vibe coding since 2024 (was a coder before too), made over 4300 commits on GitHub in the last year (up from an average of around 500 commits a year, and built several apps and tools that got me to a comfortable five-figure MRR. I'm not sharing links because I'm not here to promote anything. I just think this setup has made a big difference in how much I can build and how fast. So I think it is worth it for any vibecoder who is building apps for clients and is expecting to make money out of it.

What is Orca? This is a question that many people asked me in my previous post. Orca is a coding agent that lets you connect Claude Code, Codex, OpenCode, and others, and have one agent orchestrate the rest. Opus can plan and supervise while DeepSeek workers do the actual coding.

I find this more efficient than having one expensive model do everything. Orca also handles task delegation, monitoring, and worker lifecycle, which saves headache when running multiple agents. You can know more about it here - https://www.onorca.dev/ (not affiliated at all)

Now here's my setup there. I first connect my claude code and opencode there (orca natively supports it there) and then use one of the 2 setups for most of my task -

Setup 1: Cheapest setup

DeepSeek v4.1 Flash 1017 from the official DeepSeek API orchestrating DeepSeek v4.1 Flash 1017 workers from OpenCode Go (No Claude involved)

Ridiculously cheap and works well when building on top of an existing codebase.

Orchestrator: DeepSeek V4.1 Flash 1017 through the official DeepSeek API. Workers: DeepSeek V4.1 Flash through OpenCode Go.

OpenCode Go costs $10/month and gives $80 worth of DeepSeek V4.1 Flash usage. That's 8x the subscription price in usage value, which is a lot considering how capable the model is.

The orchestrator runs on the DeepSeek API, so that costs very little too.

I use this for bug fixes, integrations, backend changes, refactoring, and general work on existing codebases.

Prompt I give my DeepSeek orchestrator:

Act strictly as the orchestrator and don't write any implementation code yourself. Use Orca's orchestration skill to delegate all coding work to workers. Always use the opencode agent, never opencode2, and don't specify any --model or --effort flags. Let OpenCode use its configured default model, but first verify that it's running opencode-go/deepseek-v4.1-flash with maximum reasoning effort. If the model doesn't match, stop and tell me instead of switching to something else. Use Orca's native Run/Task/Dispatch workflow to create tasks, launch workers, supervise their progress, review the results, run verification, and release workers when they're done. Make sure every worker uses Codebase Memory to understand the existing code before planning or changing anything. Always provide the actual launch receipt for each task, and if something fails, report the exact error rather than silently trying another model. Use Superpowers skill for planning and implementation, and whenever UI/UX work is involved, use the front-end design skills and Dream Design Team stations. Ask me if anything needs clarification before proceeding.

After that, I give it the actual development task and Orca handles the orchestration.

Okay so you maybe wondering why do this? Isn't the opencode go and DS api the same thing? Honestly, I don't think so. I sometimes have had terrible experience with opencode go DSv4.1 flash model (maybe due to quantization) where it just loses its intelligence. DSv4.1 flash 1017 from DeepSeek API directly as orchestrator ensures that OpenCode go DSv4.1 flash doesn't mess up.

Setup 2: Best for UI work & complex new features

Opus 5.5 from Claude as the orchestrator with DeepSeek v4.1 Flash 1017 workers from the DeepSeek API. More expensive, but the results are worth it when design is involved or if you are making complex new features and I always use this when building completely new features or anything with substantial UI/UX.

Orchestrator: Claude Code running Opus 5.5 at medium effort. Workers: DeepSeek V4.1 Flash 1017 through the official DeepSeek API, running inside OpenCode. For UI-specific changes, Claude Sonnet 5.5 workers at medium effort.

Opus handles planning, task breakdown, coordination, and review while DeepSeek does most of the implementation. So I'm not burning Claude usage having Opus write thousands of lines of code. I rarely hit my five-hour usage limits when Opus is used mainly for orchestration.

For UI, I find Claude better at understanding design requirements and getting things to look and feel right.

My prompt for Opus:

Act strictly as the orchestrator and don't implement code yourself. Use Orca's orchestration skill to delegate all implementation work to Orca workers. For general coding tasks, always use the opencode agent, not opencode2. Don't pass --model or --effort flags, not even --model default. Let OpenCode use Orca's Config default, but verify that the active model is deepseek/deepseek-v4.1-flash-1017 with maximum reasoning effort before launching any workers. If it's not, stop and report the issue. For UI/UX changes, use a Claude Orca worker running Sonnet 5.5 with medium effort instead. Follow Orca's complete Run/Task/Dispatch lifecycle, including writing self-contained task specifications, launching workers, monitoring their execution, reviewing results, running verification, and releasing completed workers. Every OpenCode worker must use Codebase Memory to understand the existing codebase before planning anything. Always provide actual task launch receipts, and if a worker fails, report the exact error without substituting another model. Use Superpowers skill for planning and implementation, and use the front-end skill and Dream Design Team stations for UI/UX work. Ask me if anything is unclear before starting.

Two things I'd recommend regardless of setup

First, obra's Superpowers (https://github.com/obra/superpowers) - a must have skill. I use it constantly because it makes agents approach development more systematically instead of jumping straight into coding.

Second, Codebase Memory (https://github.com/DeusData/codebase-memory-mcp). Workers understanding the existing codebase before implementing has saved me from many unnecessary changes and broken features.

For UI/UX I also use front-end design skills and my own Dream Design Team skill. The latter has different design specialists review visual design, conversions, animations, accessibility, and performance. It's been helpful.

Please note - There is no skill called dream design team yet on github. It is my own collection of skills that serve as design stations. I will probably release it on GitHub soon once I have verified the individual skill licenses.

A few things I've learned

• Don't use expensive models for everything. Use them where their reasoning actually matters. That's how I have made 5-figure MRR with DS and $20 claude subscription :)
• Don't let multiple workers blindly edit the same files. Task isolation matters a lot and orca is great at it.
• Always verify the model being used. I once ended up using an expensive model for orca workers and it broke my heart :(
• Make sure workers understand the codebase before making changes and codebase memory MCP can really make a difference here.
• Don't blindly trust an agent saying something is finished unless and until the orchestrator has verified it.
• Orchestration isn't always necessary and for smaller changes, running DeepSeek directly is faster and cheaper. So don't use orca for everything, but for substantial features or working on multiple things at once, this approach has been effective for me.
• You don't need the most expensive model doing all the coding. You need a capable orchestrator that knows how to distribute the work.

I've attached a screenshot of one of my Orca sessions where Opus coordinates multiple DeepSeek workers.

I'm blown away by how much I get done with this setup, especially for the cost.

Btw I also have Codex (chatgpt) $20 subscription and sometimes using Sol for orchestration when my claude is exhausted but it is not my preferred setup and has proven to be less efficient and expensive for me. Also my go to harness is OpenCode (when I am not orchestrating stuff) and my favorite subscription is OpenCode Go at $10 per month. (not affiliated to any of these).

Would love to hear if anyone else uses something similar, or if you've found a better combination. I'm always experimenting with this stuff.

I'm always trying to be around people building the same stuff, so if you're building too, let's connect on X or Reddit. It took me a long time to get here. I'm exhausted, honestly, but I'm happy where I am. After more than a year of vibecoding apps and solutions, I'm finally living comfortably.

I feel blessed and so appreciative of DS and Claude for changing my life.

If you are building something, I am always rooting for you!!!

Edit - Just realized this is too big of a post. So I have added some formatting to make it easier to read. Sorry for long the writeup. I have added TLDR at the top.

Simple task: Model the Gerald R. Ford-class aircraft carrier in blender from reference images.

Scenario A: It eyeballs and ignores all instructions and outputs an oval deck with 0 resemblance to the actual ship (second image)

Scenario B: It copies every single pixel from the reference image, copying things that arent the deck as if they were, causing a jagged edge. (first image)

Sonnet 5.5 did it in the free web chat first try (less detailed, but atleast its a proper model lol)

A review sub-agent doesnt fix it, whole sub-agent workflow doesnt fix it, Revisions dont fix it, Giving it to claude to write a follow up prompt doesnt fix it, writing up whole dedicated skills after mining the sessions doesnt fix it.

claude told me it either lowers the bar by comparing its last result to its newest result instead of checking if it actually matches the reference or it just writes off a problem as good enough instead of fixing it, or it just doesnt care about visual references and just does what it wants

I've used a plentitude of prompts, and i invite everyone here to try the same, to tell it to model the aircraft carrier in blender or three.js, it will 99% of the time output an unrecognizable oval deck, and sometimes it will do a bad job with the outline and cause a ragged edge in the model instead.

Hola gente, quisiese acudir a su sabiduría para una consulta, llevo 2 meses pagando OpenCode y uso mayormente Deepseek, pero quiero probar otra IA, entre Claude y Codex, mi uso es más quedo todo para proyectos personales y scripting. Se que es un tema repetitivo pero no en encontrado un hilo sobre usos, así que entre Claude Pro y GPT Plus cual plan es mejor en cuanto a límites y funcionalidades?
Me gustaría también generar imágenes o videos, entonces cuál sería mejor para empezar, quiero probar las 2 pero aún no tengo presupuesto para probarlas al tiempo.
Gracias por leer y por sus respuestas

I'm literally a vibe-coder and I've been experimenting with DeepSeek to save costs since even Claude Max is not enough for my building needs. Asking seasoned developers and engineers: do you think DeepSeek's output is better than Claude, in terms of code quality and design?

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Hi everyone, I'm a heavy user of the 20x Max Claude Code plan, and I recently switched to the Claude Code harness with DeepSeek V4.1 Flash. For my usual coding work, I estimate I'd spend about $15–25 a day to get the same amount of work done that I get on Claude Code 20x Max. Is that normal? If so, that would come to roughly $600–800 a month or more. That seems too high to be true. Is my estimate wrong?
This morning from 8:00AM to about 10:45 AM it consumes tokens 7.45 USD already.
This must be a joke, right?

Deep Seek V4.1 is so efficient, it can deliver so many work with just the $10 Open Code Go Plan.

Deep Seek please keep this good work, don't increase the price, just keep the existing price, it's a sweet spot for many people like me who are sensitive on the prices, now my open code go plan only uses deep seek v4.1 flash model, no other model is enabled.

Only Deep Seek V4.1, all the way every day!!

DeepSeek nella nuova versione è talmente lobotomizzato che quando carichi delle foto chiedendo di descriverle, si inventa che ci sono mani e piedi aggiuntivi.

Give a different answer! 🫣

Direction first: on this benchmark #1 = sloppiest and #58 = cleanest, across 58 models.

I run a thing called the Slop Index that scores how much a model's writing reads like AI slop. The ranks here are the mechanical score only (padding, templating, even rhythm, and the usual AI tells), measured against pre-2022 human writing. No LLM judge grades anything, and these mechanical ranks only move when the roster refreshes.

Where DeepSeek lands, mechanically:

• DeepSeek V4: 44th of 58
• DeepSeek V4.1 Flash (1017): 46th
• DeepSeek V4.1 Flash: 51st
• DeepSeek V4 (0813): 55th

All four sit in the cleaner half, which is not where most flagship closed models land. Several of the big Anthropic and OpenAI models sit in the top 10 sloppiest.

The split is the interesting part. DeepSeek V4 is the single cleanest model of all 58 on email (58th) and 2nd cleanest on Slack-style messages (57th), but it is one of the sloppier models on social posts (9th). So it writes very clean short work messages, then slops up when you ask for a social caption.

One honest limit: this only covers short-form (email, Slack, social, essay), there is no fiction yet, and the mechanical axes miss things like the same sentence construction repeated over and over.

Mechanical card with the full ranking: https://theslopindex.com/mechanical-card.png Leaderboard: https://theslopindex.com/leaderboard Methodology: https://theslopindex.com/methodology Open dataset: https://huggingface.co/datasets/hgaddipati/slop-index Arena if you want to play a few rounds and vote blind: https://theslopindex.com/arena

For context, I'm one of the people behind https://slashy.com, an email client that writes drafts that sound like you. This benchmark started as how we pick models.

DeepSeek V4.1 Flash 1017 inside Qoder.

DeepSeek has cooked very hard with DeepSeek 4.1 Flash 1017 ,The model is super Impressive ,A Flash model cooking this kind of stuff is just amazing

Honest part first , it is very slow . It took almost 3-4 hrs and it was still running when i posted this . My laptop also struggled to render the video

Is anyone else seeing 1017 take this long on big builds ? what are u getting ?

is this happening with any one of you? earlier the initial question (including loading some context) would consume ~25% of the context window max. Now it's 50+% before giving any answer.
Open Code + DS Flash from official API.

I am switching to DeepSeek API from local inference. I have well-configured Pi for my needs, but want to keep my costs low - thus thinking whether i should switch my harness also for DSH.

Is cache hit % better in DeepSeek Harness vs Pi coding agent? Should i consider Reasonix?

I don't know if this is normal but on most of my tasks, it's looping back and forth. "Now let me check that again", "Let me re-read that" and similar. And then like 40 - 50 "final" tool calls, which rarely end up being final. It's EATING through my tokens so much. I do give it big prompts but not something it should take DS 6+ hours to finish.

I just had it run on a change overnight and it ate up 3k+ tool calls, 200m input tokens and close to 1 bil cache with 1.7m output. The prompt was ~200 lines but still.

For reference, using Kilo Code and DS4.1F 1017 as default agent on "High" reasoning.

What am I doing wrong?

I gave V4.1 Flash one prompt in AgenC Core at max effort: build a real-time black hole simulator in Three.js with gravitational lensing, Doppler beaming, a probe you can drop in, and no external assets.

47 minutes and 328 model calls later:

- a raymarched Schwarzschild black hole where the far side of the accretion disk arcs over and under the shadow, one side blue-white from Doppler beaming and the other dim amber

- a starfield that lenses around it, with a photon ring and an Einstein ring

- a probe that stretches, reddens and freezes at the horizon

- a fly-in camera with time dilation on the HUD, plus a cinematic recording mode

- 144 fps at 1440x813 on my M4 Pro, with automatic quality scaling

The part I didn't expect: it ported the shader's light integrator to plain JS so the physics could be unit tested. The shadow edge matches the analytic formula at 2, 3, 6 and 15 Schwarzschild radii, and deflection matches Einstein's two-term result within 1%.

Writing those tests is how it caught three of its own bugs:

1. rays launched in local instead of Schwarzschild coordinates, which made the shadow far too big up close

2. disk turbulence that looked like wood grain

3. the probe spawning off screen

It fixed all three and pinned them with tests: 109 tests across 8 files, and the production build passes.

It also wrote a README section called "Approximations, stated plainly": light bending is Schwarzschild with no frame dragging, the disk has no vertical structure, and the fly-in stops at 2.5 rs because below that the captured light cone is bigger than any normal field of view, so the view is legitimately black.

Cost was under a dollar, with a 97% cache hit rate.

AgenC Core is open source: github.com/tetsuo-ai/agenc-core

I'm currently using it via open router and harness is cursor.

Does shifting to deepseek harness reduce costs? Or can I purchase from somewhere where it'll be cheaper?

Claude plus a local DeepSeek V4 Flash is all I needed. It runs on a dual DGX Spark, 60-70 tps, and these are the four things it has built for me so far:

1. DeepSeek coded its own server rack trays. It generated the geometry for the 3D printed trays that hold the two Sparks, power bricks included, from my requirements, inside a React 3D workspace that Claude Code and Antigravity built. My 2 DGX spark has run almost 24x7 in that rack for three days with no thermal issues. STLs on MakerWorld: https://makerworld.com/en/models/3446142-dual-dgx-spark-trays-for-10-3u-rack
2. A Node.js to Rust port, 167k lines. The concurrency model changed completely, from single threaded Node to Tokio, so it was not a mechanical refactor.
3. A Rust port of the claude-agent-acp codebase, 100k+ lines. This is still WIP
4. A KMP mobile app for my open source agent orchestrator, which ran for almost 48 hours continuously.

For anyone with two Sparks, these are the community recipes I run it with (not mine, credit to their authors):

• DeepSeek V4 Flash 0731: native checkpoint weights with an NVFP4 KV cache, 1M context, tensor parallel 2 across both Sparks, DSpark speculative decoding, vLLM. https://github.com/tonyd2wild/DeepSeek-v4-Flash-0731-DSpark-1M-NVFP4-KV-2x-DGX-Spark
• DeepSeek V4.1 Flash 1017 (what I run now): an EXL3 2.9 bpw quant, 1M context, tensor parallel 2 over the dual-rail RDMA link, TensorFold engine with DSpark drafting. https://github.com/MiaAI-Lab/DeepSeek-v4.1-Flash-1017-2x-DGX-Sparks

All of it goes through one SDLC skill: Sonnet writes the plan, Opus reviews it, and Sonnet then fires a DeepSeek subagent for every phase of every plan, turn by turn. I wrote the skill and the orchestrator that coordinates it in the web-ui, and both are open source: skill https://github.com/fastestdevalive/vibekit and orchestrator https://github.com/fastestdevalive/vibe-station -> mobile app coming soon.

I'd like to know if anyone else runs it as a worker under a stronger planner and compare some notes on the SDLC skill.

This model is incredible it can do impressive motion designs ,amazing at 3d tasks with three.js

This motion design was created with pure code no assets were used and it built proper motion design video

It took 2 prompts to create it ,In the 1st round the voice was not audible so it took one more prompt to fix

I was just surprised that it is keeping with Opus 5.5 which is extremely good at motion design videos ,A Flash model which is 15x cheaper than Opus 5.5 doing such great things

This model really feels a nice upgrade from DeepSeek models

Has anyone tried DeepSeek v4.1 Flash 1017 with motion design videos or any other cool things

I'll admit it: I've been sleeping on DeepSeek. I've only ever used API calls for basic text rewrites, but seeing the recent 3D engine demo made me realize the model is far more capable than I gave it credit for. I want to take it seriously now.

Can anyone recommend a genuinely useful power user guide or point me to a trusted YouTuber who actually explains advanced workflows (agentic coding, tool use, complex prompting) rather than just beginner fluff? I'm looking for sources that are respected in the community. Thanks!

r/freevibecoding A community for people who want to build amazing things with AI but can't afford expensive subscriptions or paid APIs. Whether you're using OpenCode, free OpenRouter models, free API providers, or other creative workarounds to make vibe coding possible on a $0 budget. The goal is simple: help people build more with less**.** Share free tools, discover alternative AI models, find ways around usage limits, exchange cost-saving techniques, and learn how to bring your projects to life without spending money you don't have.