This is what I’ve been using for non-confidential projects for about a week now (soon after v4 came out). I honestly can’t tell the difference, but I’m not doing anything crazy with it either.

Worth noting that I don’t think DeepSeek‘s API lets you opt out of training. Once this is up on other providers though… (OpenRouter is just proxying to DeepSeek atm)

I have been wanting to try CC with different models since Opus went downhill last month..

What limitations or issues have you noticed when using DeepSeek with Claude Code if any?

Also, the author checked in their apparently effective social media advertising plan: https://github.com/aattaran/deepclaude/commit/a90a399682defc... (which seems to be working)

is flash version on level of gpt 5.4 mini

Claude code, on the other hand, is the most subsidized one, both for consumers (through max subscription) and for enterprises (token discounts). It is also heavily optimized for cost, specially token caching and reduced thinking, at the expense of quality.

The best two are Codex and Forge Code.

However I am using plugins and skills that are only compatible with Claude Code or work best with Claude Code.

So, for me, Claude Code with plugins like claude-meme, Context Mode, Superpowers and Get Shit Done is better than other tools.

I think everyone should test multiple models and multiple agent harness for his specific needs, codebase and way of working.

https://api-docs.deepseek.com/quick_start/agent_integrations...

   ANTHROPIC_BASE_URL="https://openrouter.ai/api" ANTHROPIC_AUTH_TOKEN="$OPENROUTER_API_KEY" ANTHROPIC_DEFAULT_SONNET_MODEL="deepseek/deepseek-v4-flash" CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1 claude

If you are interested, I've built an agentic terminal that helps manage these types of things better: https://deepbluedynamics.com/hyperia

that surprised me too. The intelligence is at the client, and by making that open, anthropic has commoditized the coding agent.

I could see a serious cost reduction story by using opus for design and deepseek for implementation.

Personally I would avoid anthropic entirely. But I get why people don't.

Once you've found the path, patches are trivial and the savings are tiny unless you're doing refactoring/cleanup.

testing gets more and more complicated. Take a look at opencode go, and you see this:

>Includes GLM-5.1, GLM-5, Kimi K2.5, Kimi K2.6, MiMo-V2-Pro, MiMo-V2-Omni, MiMo->V2.5-Pro, MiMo-V2.5, Qwen3.5 Plus, Qwen3.6 Plus, MiniMax M2.5, MiniMax M2.7, >DeepSeek V4 Pro, and DeepSeek V4 Flash

and now on your own with bugs, all of these models can produce at scale. Am i missing anything in this picture. What is the real use of cheaper models?

Also opencode tracks you by default. Its not safe. Every first prompt you send is routed through their servers, logged and they can use your data however they want

The American firms are not demonstrating escape velocity and as long as china offers something somewhat comparable and offers it at a very low price to compensate for any difference in quality, they will not be generating enough in cash flows to finance reinvestment. I highly doubt they’ll be able to continue raising external financing for numerous periods from here on out - they gotta start showing strong financials and that they are running away from the open source models.

https://debugml.github.io/cheating-agents/#sneaking-the-answ...

Maybe I need to switch to some news publication that actually does real research and writing still. Because public forums like this have been completely destroyed by LLMs.

Already DeepSeek v4 is being hosted on Huawei Ascend 950. What do you think those cost relative to NVIDIA gear?

While those are nice, Claude Code has the largest amount of plugins and skills I want to use.

Looked into this one. Thought it was suspicious that it only had 7 open issues on github. Turns out they have a bot that auto-closes every single issue just because.

I honestly have no words.

> Maintainers review auto-closed issues daily and reopen worthwhile ones. Issues that do not meet the quality bar below will not be reopened or receive a reply.

Seems like not an unreasonable way to deal with the problem of large numbers of low quality issues being submitted.

https://github.com/badlogic/pi-mono/blob/main/CONTRIBUTING.m...

The token cost makes it tempting to use for token-heavy tasks like this

It's proven useful for me, and I figure others might appreciate how light of a shim it is between you and the models.

-Claude-style subagents -an MCP layer for higher-level tools -Cursor-style control plane modes like Ask, Plan, Debug, and Build.

The MCP layer lets the harness use things like GitHub file/code read, PR creation, web search/fetch, structured user questions, plan-mode switching, user skills, and subagents.

So the improvement is mostly from better ui/ux orchestration and tool access. There's some things from hermes that are interesting as well.

Most of my focus has been on applying this stack to sandboxed cloud agents so you can properly code and work from mobile devices.

I can't definitively say that the stack is better or worse than Claude code, more just tuned for my use case I guess.

Using the API from DeepSeek or OpenRouter also requires a fee, but it's a different, pay-as-you-go payment model.

all doable but all vaguely squishy and nuanced problems operationally. kinda like harness design in general.

My understanding is that DeepSeek V4 Pro is going to be uniquely good at working on consumer platforms with SSD offload, due to its extremely lean KV cache. Even if you only have a slow consumer platform, you should be able to just let it grind on a huge batch of tasks in parallel entirely unattended, and wake up later to a finished job.

AIUI, people are even experimenting with offloading the KV cache itself to storage, which may unlock this batching capability even beyond physical RAM limits as contexts grow. (This used to be considered a bad idea with bulky KV caches, due to concerns about wearout and performance, but the much leaner KV cache of DeepSeek V4 changes the picture quite radically.)

I finally get fed up and started using GPT 5.5 the past 4 days and its a breath a fresh air despite feeling much more minimal. With Claude I had to write so many hooks to enforce behaviors it wouldn't remember and it lacked common sense on. GPT 5.5 does a much better job with things like knowing the AWS CDK CLI can hang on long CloudFormation deployments and it should actively check the deployment status using CloudFormation API rather than hanging for 30+ minutes - and it does this all without asking.

Maybe there's better tooling built into Codex too, but at least on the surface level it seems like how smart the model is makes a significant difference because Claude has more tools than I can count and still struggles to use "grep".

Edit: Like just now - I can't tell you how many times I day I see this sequence:

"Sorry, I'll run in parallel"

"Error editing file"

"File must be read first"

Repeat 10x for the 10 subagents Claude spawned and then it gets stuck until you press escape and it says "You rejected the parallel agents. Running directly now"

Like if they are going to sort through all the issues eventually (like they claim), why not just close the ones that are not worthy when they get to them instead of closing all by default?

Is it just so that the project doesnt have open issues on its github page? But they are open issues in reality because the maintainer will eventually go through them?

Nothing is "unreasonable" in the sense that an open source project should have the right to do what it wants with its rules but its definitely a weird stance.

If we touch grass in person and swap certificate requests, we can actually rebuild a trust network.

This is a pretty old problem with regards to clubs / secret societies and whatnot. And with certificates / PKI, our modern security tools have solved all the technical problems.

Not only that but other countries are very unlikely to follow suit, so it is just a straight-up productivity tax on the US.

It is a guardrail against burnout and tracker spam

Its based on their implied perspective that the majority of submissions don't follow those guidelines which helps determine their quality threshold.

https://github.com/badlogic/pi-mono/blob/main/CONTRIBUTING.m...

- https://news.ycombinator.com/item?id=46930961 - https://github.com/mitchellh/vouch

> AIUI, people are even experimenting with offloading the KV cache itself to storage, which may unlock this batching capability even beyond physical RAM limits as contexts grow.

Especially this point. Any reason that this idea was considered bad? Is it due to the speed difference between the GPU VRAM to the RAM?

No sub-agents. There's many ways to do this. Spawn Pi instances via tmux, or build your own with extensions, or install a package that does it your way.

No permission popups. Run in a container, or build your own confirmation flow with extensions inline with your environment and security requirements.

No plan mode. Write plans to files, or build it with extensions, or install a package.

No built-in to-dos. Use a TODO.md file, or build your own with extensions.

No background bash. Use tmux. Full observability, direct interaction.

> Any reason that this idea was considered bad?

Because the KV cache was too big, even for a small context. This is still an issue with open models other than DeepSeek V4, though to a somewhat smaller extent than used to be the case. But the tiny KV of DeepSeek V4 is genuinely new.

Its an Elixir agent runtime with a thin Go TUI (bubble-tea). Im building it mostly to explore agent orchestration: planner/workers/finalizer flows, local file/code-edit tools, MCP tools, permission gates, run context, compaction, and eventually larger swarms. Erlang/Elixir is interesting for this because the actor/supervision model maps pretty naturally to lots of isolated agents and long-running supervised tasks.

As i said, The main lesson so far is that everything around contracts is much more fragile than I expected unless you use a very strong model. Planners return Markdown instead of JSON, tools get called with subtly wrong args, subagents repeat broken tool calls, finalizers lie about success after workers failed. And various permissions may be interpreted by agents in unexpexted way

I also started with too many modes too early instead of making agentic path extremely solid. That made me understand better why these codebases become huge: there are endless corner cases if you want a harness to work across models, providers, tools...

Stronger models hide a lot of harness weakness and weaker models expose. Making weaker models good enough requires a surprising amount of contract hardening. But that hardening tends to make the system better for stronger models too.

Also elixir http stack was causing a lot of problems (needed to use gun eventually)

The promise of this chip was “write your code locally, then deploy to the same architecture in the data centre!”

Which is nonsense, because the GB10 is better described as “Hopper with Blackwell characteristics” IMO.

Still great hardware, especially for the price and learning. But we are only just starting to get the kernels written to take advantage of it, and mma.sync is sad compared to tcgen05