OpenClaw is the most popular open source AI agent on the planet. Running it yourself? That's the hard part. KiloClaw is a fully managed, hosted version of OpenClaw. We handle the infrastructure, security, updates, and monitoring so you can focus on what your agent actually does - not keeping it alive.
This is the 2nd launch from KiloClaw. View more
PinchBench
Launched this week
PinchBench is a benchmarking system for evaluating LLM models as OpenClaw coding agents. We run the same set of real-world tasks across different models and measure success rate, speed, and cost to help developers choose the right model for their use case.
PinchBench is made with 🦀 by Kilo Code, the makers of KiloClaw.
Launch Team
When setting up your @OpenClaw, you might wonder what the best AI model for your agent is. PinchBench just lets you know.
TL,DR: It's @OpenAI's GPT-5.4... for now!
S/O to @realolearycrew for building it 👏👏 - Give it a star on GitHub and start contributing
@fmerian There should be a spoiler alert warning here😅
oops 🙈
ClawSecure
@realolearycrew @fmerian Benchmarking across success rate, speed, AND cost in one system is exactly what's been missing. Most model comparisons focus on one dimension, usually just quality, and ignore the tradeoffs that actually matter when you're running agents in production.
We operate multiple AI models across different workflows internally and the biggest decision isn't "which model is best" but "which model is best for THIS specific task at THIS cost threshold." A model that's 90% as good at 7% of the cost is the right choice for routine tasks. A model that catches edge cases other models miss is worth the premium for security-critical work. Having standardized benchmarks across real-world OpenClaw coding tasks gives developers the data to make that routing decision instead of guessing.
The fact that this runs against real-world tasks and not synthetic benchmarks is key. We see the same thing in security scanning: synthetic test cases tell you how a tool performs in ideal conditions. Real-world data tells you how it performs on the messy, unpredictable code that developers actually ship. Real-world benchmarks are always more valuable.
The OpenClaw ecosystem needed this. As the agent framework grows and more models compete for developer adoption, having an independent, standardized way to evaluate performance helps the entire community make better decisions. Congrats to the Kilo Code team on the launch!
@realolearycrew @jdsalbego Thanks for the kind words, JD!
What models are you using when building @ClawSecure? (and how do they stack up??)
ClawSecure
@realolearycrew @fmerian I'm faithful to my Opus 4.6 extended thinking models. I literally don't use anything else for any type of work, whether that's coding, social media content, operations, workflow building, research, analysis, or anything. I pretty much have worked with most of the top models and IMO my Opus 4.6 extended thinking is GOD mode
@jdsalbego @Claude by Anthropic models are embraced by the community here - see this thread: What's the best AI model for OpenClaw?
Product Hunt
Kilo Code
@curiouskitty I think that SWE bench is a great benchmark for Software Engineering tasks. The whole point of PinchBench is that u think OpenClaw goes so far beyond development work to all knowledge work and even personal assistant type tasks. So my goal is for PinchBench to reflect that more than just software engineering
Ollang DX
Oh wow, the timing is amazing. I installed OpenClaw for the first time yesterday and was genuinely confused about which model to choose. I ended up using an OpenRouter API key with auto model selection, but the model choices felt a bit random. I’m really glad this product launched today, I’ll definitely be using this benchmark.👏
@mazula95 love it! go give @KiloClaw a spin and let us know what you think in a review! producthunt.com/products/kiloclaw/reviews/new
This is exactly what I was looking for. However, tasks should be scoped and agents should be ranked depending on task category.
Imho the most important agent to determine is the main one, the orchestrator, the one you talk to. But then, you will eventually want different subagents specialized in different tasks (and ideally not as expensive, depending on the task at hand). For those, the "best" agent (in terms of value for money) could be something else (i.e., for a simple but broad internet search, gemini flash is often more than enough).
Kilo Code
@wtfzambo1 Totally agree! Have you tried Auto Balanced model model in KiloClaw? That's exactly the idea behind it: smarter, more expensive models for architecting and orchestrating - cheaper ones for execution
@wtfzambo1 Give it a spin and let us know what you think in a review! producthunt.com/products/kiloclaw/reviews/new
@fmerian @olesya_elf At the moment I'm too invested in a private OpenClaw instance that I spun up roughly 1 month ago to drop it and restart with another one, but I have a friend (non tech) who's seriously interested in having a setup similar to mine and I was wondering, how does the AI offering work with KiloClaw?
Great question - They do run benchmarks continuously as new models are released. For the record, the latest leaderboard update was on March 21st (5 days ago), and the current best scores:
@OpenAI's GPT-5.4: 90.5%
@Qwen 3.5-27B: 90.0%
@Qwen 3.5-397B-A17B: 89.1%
How does your model stack up? 😸
Kilo Code
@anusuya_bhuyan typically we have new models up within a few hours. Although we also have partnerships with inference providers that can make that even faster.
For example we had a “stealth” version of Nemotron 3 Super before it even launched 😃
@realolearycrew any on-going "stealth" models to play with? 👀
Okay, this is genuinely useful. I've been picking models for coding tasks based on whatever benchmark thread showed up in my feed that week, which is a terrible way to make that decision.
The cost dimension is what gets me. Success rate matters, but if a model takes 3x longer and costs 4x more to get there, that changes the math completely, depending on what you're building. Glad someone's actually measuring all three together.
Curious how you're defining task success — is it automated test output or is there a human eval component? That part always feels like the hardest thing to get right in coding benchmarks.
Congrats on shipping. The 🦀 was not lost on me.
Kilo Code
@ryszard_wisniewski Thank you for your support!
The best part is that you get to shape it because the benchmark is open source, and you can submit your own tests. More on this here: https://blog.kilo.ai/p/pinchbench-v2-call-for-contributors
oss ftw!
Great question. The benchmark currently includes 23 tasks across different categories. Each task is graded automatically, by an LLM judge, or both to ensure both objective and nuanced evaluation.
In details:
Automated: Python functions check workspace files and the execution transcript for specific criteria (file existence, content patterns, tool usage).
LLM Judge: @Claude by Anthropic evaluates qualitative aspects using detailed rubrics with explicit score levels (content quality, appropriateness, completeness).
Hybrid: Combines automated checks for verifiable criteria with LLM judge for qualitative assessment.
See the public repository on GitHub - hope it clarifies!
How do you make sure the results from PinchBench reflect real-world use especially when different projects have different complexity, tools and edge cases?
You're spot on - Most LLM benchmarks test isolated capabilities. PinchBench tests what actually matters:
Tool usage: Can the model call the right tools with the right parameters?
Multi-step reasoning: Can it chain together actions to complete complex tasks?
Real-world messiness: Can it handle ambiguous instructions and incomplete information?
Practical outcomes: Did it actually create the file, send the email, or schedule the meeting?
The benchmark currently includes 23 tasks across different categories, and the team is looking for contributors to add more (target: 100).
Let's build the best benchmark for @OpenClaw 🦞