u/gargetisha

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

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?

Hey everyone! GLM 5.3 is now available in ClinePass.

It is built on top of GLM 5.2, but pushed significantly more compute into post-training across diverse long-horizon tasks. The result is another strong jump in coding and agentic performance.

On Terminal-Bench, GLM 5.3 now comes in ahead of both Fable and the newly released DeepSeek V4-Pro 0813. Zai is also highlighting cybersecurity as a major area of improvement, calling it a new standard among open models.

The pace of open-weight releases right now is pretty wild. GLM 5.2 landed only a couple of months ago, DeepSeek V4-Pro 0813 dropped yesterday, and now GLM 5.3 is already pushing the benchmark forward again.

You can try GLM 5.3 through ClinePass, our subscription for ~5× discounted access to open-weight models including GLM, DeepSeek, Kimi, and more.

First month is currently $4.99, then $9.99/month.

To get started:

npm i -g cline

Curious to hear how GLM 5.3 performs on real coding tasks compared with the other open weight models you’ve been using.

DeepSeek just silently released V4 Pro 0813, and it looks like a pretty significant upgrade.

It’s up 15.8% on Terminal Bench compared to the April Preview model, while reaching Fable 5 level performance at roughly 57× lower cost.

1.6T parameters, 49B active, 1M context.

At this point, it might be the best price to performance model on the market for agentic coding.

And it’s now available in ClinePass.

ClinePass is $9.99/month and gives you ~5× discounted access to a curated set of open weight models including DeepSeek V4 Pro, Kimi, GLM, MiniMax, and more. No separate provider accounts or API keys to manage.

At this price point, usage is effectively close to unlimited for most users.

We’re also running a limited $4.99 introductory offer through the Cline CLI.

npm i -g cline

NVIDIA just released Nemotron 3.5 Lightning, and we’re making it FREE to use in Cline.

It’s a 30B MoE model with 3B active parameters, built for agents that spend a lot of time calling tools, checking results, and moving through repeated execution steps. NVIDIA says it can deliver up to 4x faster output than similar-sized models.

We’ve been seeing smaller models get a lot more capable lately, and this is another good example of that. For coding agents especially, faster and cheaper model calls can make a pretty big difference over longer tasks.

You can try Nemotron 3.5 Lightning for free in Cline now.

To use it, simply select Cline as your provider and choose `nvidia/nemotron-3.5-lightning` from the free models in the model picker.

Full details: https://cline.bot/blog/nvidia-nemotron-3-5-lightning-available-in-cline

Curious what the community thinks of the Nemotron 3.5 Lightning!

Hey everyone - Muse Glimmer is now available in Cline via Ollama

Meta just released Muse Glimmer, a new open weight 30B model built specifically for local, always-on agent workflows.

It’s designed to run entirely on consumer hardware, including Macs and PCs with a single performant GPU. Meta says the quantized model comes in under 20GB, making it particularly interesting if you want to run Cline locally without sending your code to a hosted model.

You can try it in Cline today with Ollama:

ollama launch cline --model muse-glimmer:30b-mlx

For better agent performance, you can also install Meta’s Muse Code harness:

cline plugin install muse-code-harness

Would love to hear how Muse Glimmer performs for the Cline community.

We just added /theme to Cline CLI.

You can now switch the entire CLI look without leaving the terminal, and the theme picker previews every option live as you move through them.

There are 11 built in themes right now, including Tokyo Night, Gruvbox Dark, Nord, Dracula, Catppuccin Mocha, One Dark, Solarized, plus Cline Dark and Light.

Just run:

/theme

You can also change it from the command palette or /settings.

And if you want to set it at startup, CLINE_THEME overrides your saved theme.

Which theme are you using?

Hey everyone,

A few days ago, we made the updated DeepSeek V4-Flash 0731 free to use in Cline as well as in ClinePass.

Since then, it has become the #1 most used model across Cline, with more users than the next two models combined.

Since the 0731 update:

- Usage is up 40%
- Token usage has 3x’d
- It has set a new all-time usage high four days in a row

We’ve been trying to make Cline one of the easiest places to try new open weights models as they come out, and making DeepSeek V4-Flash free was part of that.

If you haven’t tried it yet, DeepSeek V4-Flash is still free in Cline. Simply signup via

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

Curious if it has become anyone else’s default model yet.

We tried using Meta's new Muse Code agent, but it has a bug that doesn't let it sign in from a docker container.

So we did a fun experiment: Meta claims Muse Spark 1.2 was co-trained with their Muse agent harness. So we extracted instructions from their system prompt and added them to the Cline harness.

TL;DR of this special prompting:
- Trust source code over the user prompt, so read every call site and existing tests before starting the task
- Weigh edge and error cases as heavily as the happy path
- Always reproduce the bug before fixing
- Don't trust the first passing test suite, and verify suspicious looking half-baked tests
- Never stop at just editing, keep working until the change is verified complete.

We then asked this modified harness to fix a real bug from our repo, and compared the results to the original Cline agent harness.

Results:
- Used 2.7x fewer tokens (19.7M → 7.2M)
- Finished 2x faster (49min → 24min)
- Cost 2.4x less ($7.69 → $3.25)

Same Muse Spark 1.2 model, same task, only the prompting changed.

Incredible how much of a performance gain Meta was able to achieve training it on these special instructions!

DeepSeek V4 Flash is significantly cheaper per token, but token pricing alone does not tell you what a model actually costs to use.

A cheaper model can still end up costing more if it needs additional turns, tool calls, or tokens to complete the same task.

According to Artificial Analysis reports DeepSeek V4 Flash completed the benchmark tasks at an average cost of just $0.03 per task, compared with $3.15 for Fable 5.

That makes DeepSeek V4 Flash 105x cheaper per completed task.

Btw, DeepSeek V4 Flash 0731 is free to use in Cline.

1. Sign up via: npm i -g cline
2. Open /settings > Cline provider
3. Select deepseek-v4-flash

Try it on your own coding tasks and let us know how the cost and performance compare.

Hey everyone,

We’re making the new DeepSeek-V4 Flash 0731 free in Cline.

It scores 82.7 on Terminal Bench 2.1, which puts it surprisingly close to some of the strongest coding models available today.

We’ve already shared a deeper breakdown of the benchmark results, but the more interesting part now is seeing how it performs on actual codebases. We’re excited for you to try it and feel how far Flash models have come.

To use it:

1. Install Cline CLI: npm i -g cline
2. Open /settings
3. Select Cline as your provider
4. Choose deepseek-v4-flash

It’s completely free to use right now.

Curious to hear how DeepSeek-V4 Flash 0731 performs for the you all.

DeepSeek silently updated its changelog with a new V4 Flash upgrade.

The updated model now scores 82.7 on Terminal-Bench 2.1, a massive +25.8 point leap from its initial April preview score of 56.9.

The upgrade is already live through the DeepSeek API, and the open weights have also now been released. Pretty significant upgrade.

You can also try DeepSeek V4 Flash through ClinePass.

ClinePass gives you access to open weight models such as Kimi K3, GLM 5.2, DeepSeek V4 Flash, Qwen 3.7 Max, and DeepSeek V4 Pro, with 2- 5x higher usage limits.

Use it on Cline CLI & extension with $1.99 special promo if sign up via: npm i -g cline

Curious to hear from people who have tried DeepSeek V4 Flash in ClinePass.

Hi everyone,

We’re happy to announce that Cline has signed the Open Weights letter, and we're proud to be a part of this movement.

Millions of developers use open-weight models in Cline because they are cheaper, help address data privacy concerns, or meet regulatory requirements. We signed the letter because we believe open-weight models are essential for developer choice, competition, and broader access to AI.

To celebrate, we’re making GLM 5.2 free to use across the Cline VS Code extension and CLI.

Open Cline, select GLM 5.2 from the model picker, and try it on something you’ve been meaning to hand off.

To install Cline in your CLI: npm i -g cline

What stood out most from our recent Terminal Bench 2.1 experiment was just how large the cost difference was between models.

A full 89 task run cost:

- Kimi K3: $49.80
- GPT 5.6 Terra: $400
- Fable 5: $552

That made running the benchmark with Kimi K3 around 11x cheaper than Fable 5.

The larger recursive self improvement experiment ran for around 17 hours and cost approximately $680 in total. The $49.80 figure is specifically for the final full benchmark run.

Full breakdown: https://cline.bot/blog/recursive-self-improvement-for-coding-agents

Hey everyone! Anthropic’s new Claude Opus 5 is now available in Cline.

Opus 5 currently ranks #1 on SWE Bench Verified with a 97% score, ahead of GPT 5.6 Sol at 96.2% and Claude Fable 5 at 95%.

Anthropic claims Opus 5 delivers close to Fable 5 level intelligence at half the price. It costs $5/M input tokens and $25/M output tokens, the same price as Opus 4.8.

The model is built for complex coding and long running agentic tasks, with improvements in debugging, root cause analysis, and verifying its own work. On CursorBench 3.2, Opus 5 performed within 0.5% of Fable 5 at roughly half the cost per task.

It is incredible to see Anthropic release another major model only a month after Fable 5. The pace of improvement across frontier coding models continues to accelerate.

Try it in Cline with model ID: anthropic/claude-opus-5

To install Cline in your CLI: npm i -g cline

Claude Opus 5 is also available through the Cline extensions for VS Code and JetBrains.

Full details: https://www.anthropic.com/news/claude-opus-5

Curious to hear how Claude Opus 5 performs for the Cline community.

We recently added Kimi K3 to ClinePass, and its adoption has been much faster than we expected.

Within three days of launch, Kimi K3 went from 0% to 16% of all open weight model tokens used through ClinePass, making it the #3 most used model on the platform.

DeepSeek V4 Pro and DeepSeek V4 Flash still account for the largest share of usage, but Kimi K3 is already closing in on them despite being the newest model in the lineup.

This is the fastest climb we’ve seen in open weights usage in Cline’s history.

Try Kimi K3 on ClinePass!

ClinePass is Cline's new subscription plan which provides 2-5x discounted access to open weights models like Kimi K3, GLM 5.2, Kimi K2.6, Qwen3.7-Max, DeepSeek V4 Pro and many others.

Use it on Cline CLI & extension with $1.99 special promo if sign up via: npm i -g cline

Curious to hear from people who have tried K3 in ClinePass.

Hey everyone - we wanted to give you an early heads-up that we’re planning to deprecate .clineignore soon.

.clineignore was originally introduced to reduce context noise by controlling which files Cline automatically loads, such as dependencies, build artifacts, generated files, and other files that are unlikely to be relevant to a task.

However, it only affects files that Cline loads automatically into context. It was never intended to act as a security or access-control boundary. Models can still access project files through other methods, particularly shell commands, so .clineignore cannot reliably prevent access to specific files.

To avoid giving users a false sense of security, we’re removing it as a supported feature.

Still want context filtering? Here’s an alternative

For users who still want context filtering, we have a plugin that offers similar behavior:

https://docs.cline.bot/sdk/plugin-examples#block-ignored-file-access

You can Install it with:

cline plugin install https://github.com/cline/cline/blob/main/sdk/examples/plugins/gitignore-read-files-guard.ts --cwd .

The plugin uses a runtime beforeTool hook to block read_files, editor, and apply_patch calls when they target files ignored by your workspace’s .gitignore.

Important limitations

There are a few important limitations to be aware of. This is not a 1:1 replacement for .clineignore, and it should still be treated as best-effort context filtering rather than a security guardrail.

• It guards file reads and edits only.
• It does not filter search_files or list_files results.
• It does not guard shell commands.
• It uses .gitignore, not a separate .clineignore.
• It requires a Git repository.
• Plugins currently run on the Cline SDK, CLI, and Kanban. VS Code and JetBrains support will arrive as those extensions migrate onto the SDK.

For now, the .clineignore documentation will remain available for existing users while the feature is phased out, but we no longer recommend adopting it for new workflows.

Thanks for your understanding: we’re making this change so Cline’s file access behavior is clearer and less likely to be mistaken for a security boundary.