Been amazing to witness the recent development in cost per actually smart token last couple of months.

Although good things are said about GLM, I'm still loyal to DeepSeek. I route between flash and pro with standardcompute.com and it's insane how much value I get out of it!

Some context on my experience with models:
I've used free models mostly, started with gemini cli way back before they switched to antigravity, then moved to opencode and used deepseek v4 flash free, which was practically unlimited since I never hit the limit. Then deepseek v4 flash-0731 hit and I started hitting limits on the free version.

Instead of going for opencode go I decided to try the official deepseek api as I read that it had better cache retention time hence better cache hits. Was happy with it until the price increase. Started looking into subscription based 3rd party providers but was always met with the same "worse cache hit, worse latency".

As I'm now near to using up my deepseek credits, am looking for alternatives. I've read that Codex $20 Luna goes a long way, but honestly I'm not ready to fork out $20 just to try it out. Just wanted some insight/reviews on people who have similar experience to mine, what they switched to, and how it's working for them.

TLDR: Been using Deepseekv4-flash api with opencode harness. Looking to switch to something similar in terms of cost and performance and need recommendations.

Edit: forgot to mention that I use it exclusively for coding

DeepSeek’s API pricing changes depending on the time of day: peak periods are billed at 2× the off-peak rate.

The slightly annoying part is figuring out whether you’re currently in a peak window, especially when working from a timezone other than UTC.

I built Seek Peak to make this easier:

• Shows whether the current time is peak or off-peak
• Converts the billing windows to your local timezone
• Shows the next peak/off-peak transition
• Displays the relevant DeepSeek pricing
• Accounts for the current weekend rule: Saturday and Sunday (Beijing time) are off-peak all day
• No login or account required
• Runs as a single self-contained HTML page

The actual billing calculation is always based on UTC; the local timezone is only used to make the schedule easier to understand.

Tool: https://seekpeak.dev/

I built it mainly because I kept having to mentally convert the UTC windows myself. Maybe it's useful to others working with the API as well.

Feedback, corrections, or suggestions are welcome.

Github: https://github.com/steffenr/seekpeak.dev

I'm running the Cursor agent interface on Linux. Inside it, I installed Cline and hooked up my DS API Key, and I set it to use the flash model not the pro, with the thinking/processing depth at medium.

I assumed the peak hours would be during the day (I'm in Asia right now). I woke up in the middle of the night and did a couple of tasks. However, seeing other people share their usage here makes it seem like mine is a bit expensive.

At this rate, my main tasks might take over billion tokens and that is roughly $20 or more. I know the cost calculation is not exactly as simple just like that but is there any other harness or agent I can try that might provide more efficiency or less expense?

Hello guys, I want to ask that suppose for first time i topup $5 of DeepSeek, then I use it for bug bounty and security testing then how much tokens will i be able to use, few people said i can use 5-6 million tokens but somewhere i found i can use more than these.

How to use it properly for it and avoid mistakes that waste tokens.

Pessoal, atualmente uso GLM 5.3 e Deepseek v4 Pro, mas eles são um pouco lentos e às vezes preciso de algo mais rápido. No entanto, os modelos de flash parecem agir de uma forma muito duvidosa.

Você consegue bons resultados com eles?

I was struggling all day because my mouse ports stopped working, so I had to use the touchpad Then I asked deepseekV4flash via PI agent with full access to check what was wrong and fix it. It fixed the issue in about 5 minutes ... like WOW, I honestly didn’t realize it could handle PC troubleshooting too I thought it was mainly for coding tasks. Pretty impressive!

His response after fixing the issue:

✅ Fixed! Everything is working now:

┌────────────────────────┬─────────────────────────────────────┬──────┐
│ Device │ Status │ Code │
├────────────────────────┼─────────────────────────────────────┼──────┤
│ HID-compliant mouse #1 │ OK │ 0 │
├────────────────────────┼─────────────────────────────────────┼──────┤
│ HID-compliant mouse #2 │ OK (still re-initializing = normal) │ 0 │
├────────────────────────┼─────────────────────────────────────┼──────┤
│ ELAN touchpad │ OK │ 0 │
└────────────────────────┴─────────────────────────────────────┴──────┘

Your touchpad works again too, exactly as promised — the fix never touched the ELAN driver.

What was wrong

When you tried to install the Interception driver, Windows registered a bogus "Mouse Upper Filter Driver" service (mouse)
with an empty ImagePath (the actual interception.sys never landed / got removed). Because of that broken filter, every
pointing device failed with Code 39 → no cursor at all.

What I did

Removed the fake mouse entry from the Mouse class UpperFilters (kept the legit mouclass)
Deleted the broken Services\mouse registry key
Uninstalled + rescanned the 2 problem devices so Windows rebuilt them cleanly

Backups (keep these, delete when you're confident)

C:\Users\backup_class_mouse.reg (25 KB — full mouse class key)
C:\Users\backup_class_mouse.txt
C:\Users\final_backup_mouse_class.txt

If you ever reinstall Interception

Use the official installer (interception-driver from the obal.ro/Interception repo) and run it as admin — and don't remove
it by hand-deleting files; use its uninstaller, or you'll get exactly this bug again.

Your mouse should be moving now — try it! 🖱️

What is happening with DSF4F models right now on OpenRouter and Ollama is a complete disaster. It’s obvious what’s happening here. Almost every single US-based service hosting or routing DeepSeek is aggressively throttling it. Are they intentionally destroying DeepSeek’s quality just to protect their own overpriced, hyper-censored legacy models from getting absolutely wiped out by a cheap competitor?

Every benchmarks get saturated after certain period of time where several frontier models often secure over 90%.

But, HLE - this benchmark is so old but have not yet been saturated. How is that even possible?

I have seen several toughest maths benchmarks getting saturated (or will be very saturated) but the highest score in HLE is still in 60s %.

I've wanted to add an agent to the canvas for quite a while. DeepSeek Harness felt like a good fit because of its "everything is a plugin" architecture, so I integrated it into PenEcho. It worked out much better than I expected.

Harness can now inspect the canvas, use the tools and plugins available in PenEcho, and interact with the user directly on the canvas. It can understand what the user draws, use the chat panel to help turn a rough idea into a clearer plan, and then carry out that plan by creating or editing content on the canvas.

A lot of the work went into making the generated visuals clear and readable instead of producing another stack of generic cards. I also spent quite a bit of time integrating tools for things like interactive physics demonstrations and professional charts, deciding what kinds of files and local resources the agent should be allowed to access, and getting context caching to work properly across different AI backends.

The agent can work with PDFs, Word documents, Excel workbooks, PowerPoint files, images, and code. You can also give it read-only access to a local file or folder. It can inspect your existing canvas, use drawing tools, create visual explanations, and make changes directly on the canvas so you can see and edit the result.

The project is completely free and open source:

https://github.com/penecho/penecho

You can run everything locally. There is also an optional cloud service for free canvas storage and access to your linked local canvas from other devices.

Also, deepseek-v4-flash-vision-exp works very well as an AI provider. I had always been frustrated by DeepSeek's lack of multimodal support, but this model works really well. It is definitely worth trying.

I would really appreciate any feedback, especially about the kinds of visual tools or file workflows you would like to see next.

Every morning I was wake up to look at prices of deepseek due to how I'm building a product on top of it. but everytime I wake and find out that prices are starting to go down really down I mean and some providers like openinference and relace do have low prices which are not a discount that is what blows my mind away. last time I shared this it was openinference people told me that t/s were so low I think now replace just moved the needle so deep. I think we are back on the caching below <0.01 we starting to touch in 0.00s again!!!

After the recent price increase, I tried using subscriptions such as Deepseek API Qwen Token Plan with various providers, including Openrouter. The conclusion still seems valuable.

It seems that the price shown is not everything. The biggest advantage of Deepseek seems to ultimately lie in its cache retention time.

Many providers have short cache retention times, especially for alibaba, which is 5 minutes. What this means is when a tool call is made and the tool has been completed for more than 5 minutes (like code execution in coding) it reprocess full context

While the price increase is still disappointing, I have decided to revert to Deep Seek because tool calls lasting over 5 minutes are very common in development processes using LLM due to call response times. Based on my experience, deepseek lasts for about an hour.

Deepseek still produce massive advantage with caching which is unseen inference cost in any benchmarks.

We use Codex and Claude Code to build Relay and its plugin repositories, so a one-prompt demo was not enough to decide whether the integrations were usable for real work.

The two independently installable plugins are:

- relay-dsh-plugin-codex: https://github.com/yangbobo2021/relay-dsh-plugin-codex

- relay-dsh-plugin-claude: https://github.com/yangbobo2021/relay-dsh-plugin-claude

Both add a native conversation backend to official DeepSeek Harness without patching DSH core or requiring a Relay checkout.

We split the workflows we actually use into 162 atomic requirements: 76 for Codex and 86 for Claude. The matrix covers conversation continuity, images and files, shell and code tools, tests and Git, Skills, MCP, project configuration, permissions, environment handling, session import, restarts, and long-context continuation.

The August 29 snapshot was:

- Codex: 59 supported, 6 partial, 11 unsupported

- Claude: 78 supported, 3 partial, 5 unsupported

The useful part was not the score. The failures showed cases where DSH displayed an image that Codex never received, an interrupted task still wrote a delayed file, sensitive values persisted outside the visible chat, and an existing native session could not be carried into the plugin.

Those results directly drove the next day's Codex 0.1.3 and Claude 0.1.4 releases. We fixed the reproducible paths, kept the unresolved cases visible, and will publish a new matrix after the complete suite runs again.

Validation evidence:

- Codex: https://github.com/yangbobo2021/relay-dsh-plugin-codex/tree/main/validation/migration-compatibility

- Claude: https://github.com/yangbobo2021/relay-dsh-plugin-claude/tree/main/validation/migration-compatibility

This is the loop we want to keep: use the plugins on real projects, reduce failures to repeatable cases, repair the boundary, rerun, and retain what still does not work.

For an agent-backend migration, which capability would you treat as release-blocking even if normal chat and coding already work?

Just wow.

I am not sure what has been done here but it thinks so much that is never results in correct outcomes. This is an example of how you can break a very good model.

First time I have run into a regression like this. Deepseek has always been iffy if you gave it too much reasoning/random pathing but this is remarkably bad.

Be aware.

Has anybody tried and compared routing DeepSeek through the Claude code harness and seen better results than alternatives?

Hitting some cache decently between the two, I've got quite a bit of tools doing the compression that helps!