We've been dealing with an incredible amount of abuse on ClinePass recently as well.
That's 8 billion with a B.
Thank you @stripe 🙏
Ox Alpha (stealth model) is now free in Cline.
Early benchmarks shows marginal improvement over Fable and GPT.
Try it with:
npm i -g cline
and use /models to see it under Free options
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PSA: use medium thinking effort instead of xhigh.
Opus 5 medium ranked #2# on FrontierCode at 53.4% for $4.31/task, while xhigh ranked #16# at 43.6% for $9.14/task.
1/2 the cost and 10% better.
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Thank you @OpenAI for the GPT price cuts in Cline ❤️
GPT-5.6 Sol is 50% off
GPT-5.6 Terra is 20% off
GPT-5.6 Luna is 80% off
For reference, Sol is now over 3x cheaper than Fable.
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Qwen3.8-27B is now the #1# local model in Cline after just 4 days.
This ends a *4 month* streak by the previous winner Qwen2.5-Coder-7B which has been the top local model since April.
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A local 27B model scoring frontier performance! Huge thanks to @cline for the shoutout.🥳 This is just the beginning — Qwen3.8-27B will keep finding its way into more fields.🌱
Artificial Analysis Intelligence Index puts Qwen3.8-27B at DeepSeek V4-Pro and GPT 5.6 Luna performance. This is the first time a local model has scored frontier model capability.
We weren’t expecting this pace of local progress anywhere near this soon.
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GLM-5.3 has released, beating Fable & the new DeepSeek V4-Pro 0813 from just yesterday on Terminal-Bench.
It used GLM-5.2 as the base model with gains from more compute spent than ever before on post-training on diverse long-horizon tasks.
Open weights have hit escape velocity.
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24h since the 0813 update: DeepSeek V4-Pro users in ClinePass up +67%, token volume up +100%.
DeepSeek models are now ~70% of all tokens flowing through ClinePass.
DeepSeek is winning.
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DeepSeek silently released V4-Pro 0813, up 15.8% on Terminal Bench from their April Preview model, with Fable 5 performance at ~57x cheaper cost.
1.6T param, 49B active, 1M context. This is the best price-to-perfomance model on the market right now.
Available in ClinePass now!
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We’re making NVIDIA’s new Nemotron 3.5 Lightning free in Cline.
This 30B param open model is great in coding agents, and delivers up to 4x faster output than similar options.
Better smaller models make agent use cheaper & accessible to more, great work @NVIDIAAI ❤️
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databricks switched from opus to glm, which their evals found had 92% of the pass rate at 1/4th the cost.
...their evals on their own proprietary codebases, not on public benchmarks that models could have hillclimbed.
the chinese benchmaxxing accusations are officially cope.
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Today @databricks we're publishing a detailed analysis of techniques we used to drastically reduce our internal AI spend while aggressively growing adoption. Savings come from layering in several techniques, which combine to drive unit costs down as much as 90% in some scenarios. Tl;dr, the wins come from:
1. Shifting defaults to more efficient models, including OSS models such as GLM. Maximum intelligence models simply aren't needed for many coding tasks, and "good enough" models are quickly becoming very cheap. We shift traffic between models using Unity AI Gateway. Approximate savings: 50% or more.
2. Using smart routing to automate model selection. Routing can further squeeze efficiency by dynamically selecting the model or harness that can most efficiently execute a particular task. Our task-level routing leverages @omnigent_ai. Approximate savings: 30%.
3. Providing user visibility and adaptive budgeting. Every user can see how much they spend, and users receive hints on how to contain spend. Heavy spenders encounter progressive friction as they ratchet spend above certain levels. Approximate savings: 10%.
4. Managing context bloat by pruning tool call results and tuning harness settings. Extraneous context costs $$ and delivers no value. Tuning cache settings also help lower average token costs. Approximate savings: 10%.
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DeepSeek V4-Flash is now the #1# most used model in Cline.
Since the 0731 update, usage is up +40% and tokens have 3x’d. It has more users than the next two models combined, and just set all-time highs four days in a row.
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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!
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just added /theme in cline cli 😎
Try npm i -g cline on Cline!👀
Qwen3.8-Max is now available in ClinePass, a subscription for ~5x discounted access.
Use it on Cline CLI w/ $4.99 special promo:
npm i -g cline
This is currently the most affordable way to access Qwen.
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We've been asked why Cline works better with open weights models like DeepSeek than other harnesses and want to share some detail (more in our blog below).
Our internal evals show that many open weights models like DeepSeek, GLM, and Kimi are RL trained to spend more tokens verifying their work: run tests, check the build, re-read diffs before completing. We believe this is how they were able to close the gap with closed model performance.
Meanwhile closed models and their harnesses tend to optimize for efficiency and fewer tokens (which makes sense when massively subsidizing subscription plans).
So we've been intentional in building the Cline harness to let the model work however it was trained to work. At open weights pricing, the baked-in paranoia burning extra tokens verifying work gets significantly better results for less money.
In our benchmark runs, we've seen ~20% gains over competing harnesses just from changes that let the model lean into its RL training.
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5 months ago the highest score on Artificial Analysis Intelligence Index was 51 (GPT-5.4 xhigh). This week DeepSeek V4-Flash scored 50 on the same index.
r/LocalLLaMA are already running V4-Flash on a Mac M2 Ultra. We predict local models become the majority choice in 2 years.
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Two orders of magnitude improvements are quite rare. This is a big deal.
