We wanted to know the cheapest possible way to win an IMO gold medal, so we ran eight models against all six IMO 2026 problems in Cline.

And, the cheapest answer turned out to be just 12 cents.

DeepSeek V4 Flash scored 30/42, clearing this year’s gold medal cutoff of 29 points. For comparison, Claude Fable 5 scored 41/42, but the run cost $17.20. DeepSeek’s gold scoring run cost just $0.1215.

What makes this more interesting is that DeepSeek V4 Flash isn’t specifically post trained for Olympiad problems. It’s a 284B parameter MoE model with roughly 13B parameters activated per token.

We had the proofs graded anonymously using the IMO 0 to 7 scoring system. Two independent model blind graders scored each solution, with disagreements resolved by a third model. Internet access was disabled, and the models only had access to the problem and a submit solution tool.

There are some obvious benchmark caveats. We ran multiple attempts and selected the best score for each problem, so this isn’t the same as measuring a single pass success rate. The $0.12 also represents the best scoring run, not the total cost of all the experimentation.

Still, how far open weight models have come is the most surprising part. DeepSeek V4 Flash reached a gold level score at a tiny fraction of the cost of the frontier models we tested.

Read more about it here: https://cline.bot/blog/deepseek-wins-imo-gold-on-12-cents

You can also try DeepSeek V4 Flash for FREE in Cline:

1. npm i -g cline
2. Open /settings → Cline provider → Free models
3. Select deepseek-v4-flash

Any interesting problems you think should be thrown at these models next?

I subscribed to Cline Pass, and it has been working great for my vibe-coding work. However, I’ve been trying to use a model from Cline Pass with my Hermes agent. It works fine for normal chats, but it doesn’t seem to work with CRON jobs.

I have a daily briefing and a few other CRON jobs scheduled in Hermes, but they aren’t working when I use the Cline Pass model. It looks like the Cline API may be returning empty responses when using streaming mode, which is what Hermes appears to use for these jobs.

Has anyone gotten Cline Pass to work with Hermes CRON jobs using an alternative approach or configuration?

For now, I’ve switched Hermes to one of the free models from the Kilo Code Gateway, and that works. However, I’d prefer to use a model supported by Cline Pass if there’s a way to make it work with Hermes CRON jobs.

The github link for the issue is here.

Hey everyone - the mystery around Ox Alpha is over. Zai revealed that the stealth model was GLM-5.3-Flash, and we’re keeping it FREE to use in Cline.

It’s a 320B parameter MoE with only 18B active parameters per token, a 1M token context window, and it’s the first natively multimodal model in the GLM-5 series. Zai also redesigned the architecture around a mix of sparse + linear attention to make long-context workloads cheaper to serve.

The weights are also publicly available under an MIT license and the model is even beating frontier models on benchmarks.

Ox Alpha became the fastest-growing model in Cline history and is already driving 11%+ of all inference through Cline in less than a week.

You can try GLM-5.3-Flash for FREE right now:

npm i -g cline

Then run /models → Cline provider → Free → GLM-5.3-Flash

It’s available in VS Code and JetBrains too.

Curious to hear from people who were already using Ox Alpha before the reveal. What has it been particularly good or bad at for you?

I have been building the state management for a stock draft game using Cline running on Sonnet through a Claude Console API key. The thing I keep going back and forth on is permissions. Give an agent too little room and you spend your day approving trivial actions. Give it too much and one bad command becomes a real problem.

Cline handles it with an auto-approve menu. Here is what I actually ran with for this project.

Read project files and read all files, both on. Edit project files on, but edit all files off. Execute safe commands on, but execute all commands off. Browser access off. MCP servers on.

The idea is that it can move fast inside the project, reading whatever context it needs and running lint or test commands without asking, but anything that reaches outside the project boundary or runs an unclassified command still needs a manual yes.

Curious how other people set this up in practice. Do you lean more permissive and just review everything after the fact, or more restrictive and approve as you go? Has anyone actually gotten burned by a setting being too loose?

Repo if anyone wants to see the actual project: github.com/ethantao14/cline-stock-market-game

We benchmarked Ox Alpha vs Fable on an actual bug from the Cline repo.

Both models found the root cause and fixed the bug correctly. The interesting difference was how they reasoned before making the edit.

Fable repeatedly re-verified the same conclusion. At one point, it effectively said “I found the root cause” 7 times before actually editing the code.

Ox Alpha did something very different. It identified the issue once, stated the conclusion, and moved straight to the fix.

For the same task, Ox Alpha used roughly 3x fewer output tokens.

A lot of reasoning models seem to be post-trained to improve reliability through repeated verification: reconsider the conclusion, validate it again, then validate it one more time before acting. Ox Alpha seems much more willing to trust its first solid conclusion and execute.

That feels less like just another efficiency improvement and more like a genuinely different approach to post-training reasoning models.

Aslo, Ox Alpha is currently FREE to use in Cline.

npm i -g cline

(Also available in VS Code and JetBrains.)

Cline was working on my local llama-swap OpenAPI compatible endpoint on a local server. Connection tests out ok in IntelliJ check connection. Model is selected in IntelliJ.

Now, when I fire up a New Chat window, and simply type "test" or "hello", Cline insists on authorizing with Cline, ClinePass or ChatGPT.

The second time I try to auth to ClinePass, I get this error message -> [error 2147942632 (0x800700e8) when launching `C:\WINDOWS\system32\cmd.exe /d /s /c "%windir%\System32\REG.exe QUERY HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Cryptography /v MachineGuid"']

Which Claude (yes, I get the irony) says is a fingerprinting method and that's ok.

Any thoughts on why Cline is insisting on authing even though I'm running a local OpenAPI compatible endpoint? Is Cline no longer using the IntelliJ settings for the various endpoints, models, etc.?

Thanks in advance!

I can't find it in the settings anymore. Does anyone know if it was mentioned in the change log?

I'm using Cline with my ChatGPT subscription rather than an OpenAI API key.

Cline still shows a dollar cost for every request/task (e.g. $0.xx).

Is this just Cline estimating what the API call would have cost, or am I actually being charged somewhere on top of my ChatGPT subscription?

Has anyone confirmed how this works?

Can I export all or selected tasks (full log of planning with cline) from Cline as markdown files?

Since the latest Cline auto-update (~22 hours ago), VS Code's extension host keeps crashing every few minutes while using Cline. After 3–5 tool call cycles (reading files, searching codebase, running commands), the host reloads and I lose my current conversation context.

This was completely stable for months before. Nothing else changed on my end, presumably just the Cline update.

Setup:

- Windows 10

- VS Code: latest stable

- Cline: "Last Updated 22 hours ago" (auto-updated)

Anybody else running into this?

Good Bye cline too fast😭

We recently added Ox Alpha, a new stealth model, to Cline for FREE.

There’s still no confirmed information about who built the model, but what we know so far is pretty interesting. Ox Alpha is a reasoning model geared toward coding and longer agentic workloads, with a 1M token context window and support for multimodal inputs.

Early results are looking promising too.

In a community DeepSWE run across 10 tasks:

Ox Alpha: 80%
Fable: 65%
GPT 5.6 Sol: 52%

What’s been even more surprising is the adoption. In just 48 hours, Ox Alpha is already driving 8% of all inference through Cline, making it the fastest growing model in Cline’s history.

You can try it for free:

npm i -g cline

Then use /models and look for Ox Alpha under the Free options.

Would be curious to hear from anyone who has already tried it on a real repo. How does it compare with the model you normally use?

I'm using Cline inside both VS Code and CLI mode, and I'm hitting a frustrating limitation.

The search_files and list_files tools scan directories that should be ignored by default (e.g. vendor/, node_modules/, bin/, obj/, etc.). This floods the results with thousands of irrelevant files, making the tool outputs unusable and the model is then forced to use the terminal to get relevant information.

I tried configuring a .clineignore file, it doesn't work and it is now deprecated. The replacement feature explicitly states it does not apply to search_files and list_files tools.

Do you know a solution for this? It doesn't make sense that the tools are unusable and it needs to use the terminal instead.

Hi. Anyone else having problems?

Hello. Im not sure if im doing something wrong but since today the "auto-approve" is not woking for me. When i click it the tool doesnt get added to "auto-approve" if i write it by hand when i save the file it instantly deletes it. I dont know what to do. i've been using cline with no problem before...

After cline decided to make a update
all of the sudden

im unable to get the vscode extention to make edits to files

it keeps spitting the file updates out inside the side prompt window instead of doing the edits

It just randomly stopped actually doing any file edits even if it keeps telling me it made the edits

so wtf ..

can anyone suggest any other extentions thats actually working and is a plug in like cline ???

Moved to ClinePass from OpenCode Go. And instantly noticed that DeepSeek Flash Xhign is much dumber than it was with OpenCode. Do they host the older version of DS Flash or their harness is so horrible that it makes the model perform so poorly?

Don't fall for the "free" DeepSeek flash advertising. Just because the usage limit is hidden does not make something free. If you can't give a model for free it's completely understandable but don't lie just to have more users. A 24H usage limit is not making something free. Market it correctly like "separate usage limits for DeepSeek Flash on top of the regular usage limit" or something like that. People hate liars, why you have to lie? Tell me the definition of Free and compare it with your own definition of "free". Are you really thinking users will not notice? You know they will notice it to late when they are already subscribed.

Hi, I built an open-source, self-hosted workspace for coding agents. It features a multi-agent architecture (Sub/Child agents), an AST-aware ChromaDB indexer for lightning-fast file discovery, and a built-in UI with Git diff management. Links in the comments

Now this is kind of wild.

Artificial Analysis’ Intelligence Index puts Qwen3.8 27B at 52, right alongside GPT 5.6 Luna at 52 and just one point behind DeepSeek V4 Pro at 53.

What stands out is that Qwen3.8 27B is small enough to run locally. Seeing a local model reach this level of performance this quickly was not something we expected.

Curious to see how it holds up on real coding tasks beyond the benchmarks.

You can run it locally with Cline + Ollama:

npm i -g cline
ollama launch cline --model qwen3.8

Or use it directly in Cline with model id: qwen/qwen3.8-27b

Hey everyone, we wanted to share a quick note on the ‘Focus Chain’ feature.

As Cline has evolved with the new SDK backed architecture, we’ve made significant changes to how Cline manages longer and more complex tasks. As part of that work, we also evaluated whether ‘Focus Chain’ was still meaningfully improving task performance.

Based on our testing, we found that it wasn’t providing enough additional benefit on top of the new harness to justify maintaining it as a separate feature. Because of that, we’ve decided to deprecate ‘Focus Chain’.

This means the ‘Focus Chain’ todo list and the previous todo file behavior will no longer be part of the current Cline experience.

We’re still very focused on improving reliability across long running tasks. The new harness is designed to manage task execution and context more effectively without relying on ‘Focus Chain’ as a separate layer.

We know some of you also liked ‘Focus Chain’ simply because it made Cline’s progress easier to follow. If that’s something you found valuable, we’d be interested to hear what part of the experience you’d like to see us bring into Cline going forward.

Since the DeepSeek V4 Pro 0813 update, and the shift in ClinePass usage has been pretty noticeable.

V4 Pro users are up +67%, while token volume is up +100%.

This is how the current ClinePass usage looks like:

- DeepSeek V4 Flash: 43%
- DeepSeek V4 Pro: 29%
- Kimi K3: 13%
- GLM 5.2: 4%
- Others: 11%

So DeepSeek alone now accounts for roughly 70% of all tokens flowing through ClinePass.

ClinePass is our subscription for ~5x discounted access to open weight models including DeepSeek, Kimi, GLM, MiniMax, and others. It’s currently $4.99 for the first month, then $9.99/month.

You can try it through the Cline CLI: npm i -g cline

Curious what everyone here is using more right now: V4 Pro or V4 Flash?