What's the best AI model for OpenClaw?
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There's a question we all ask when setting up @OpenClaw: which model should I actually use?
What are your suggestions? Any preferences?
The "best" model definitely depends on your workflows and priorities. High success rate, fast completions, or cost efficient? For coding tasks, there's this thread [1] suggesting @Claude by Anthropic, @Gemini, and @OpenAI's GPT models, while open-weight models like @MiniMax are bridging the gap with every release. [2]
Curious what the community recommends for @OpenClaw?
Note: The poll is inspired by the current leaderboard on pinchbench.com by @KiloClaw
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It really depends on what you are doing mainly, I guess. Each model has some good and not so good characteristics. And sometimes it matters which one you are used to. I imagine, if I use openclaw to write some code, Opus is my choice. And If I want to do something more creative, or do a web search, then I read that Gemini does better results
yes, it definitely depends on your workflows and priorities. curious to have your opinions.
Great question — model choice really depends on the use case. For creative writing and long-form narrative (my domain with zz-novel), I've found that the models with stronger instruction-following and consistent tone over long contexts outperform pure 'intelligence' benchmarks. Gemini Flash has been surprisingly good for story generation at scale. Curious what workflows people are optimizing for here
@echosun thanks for the suggestion! any experiences with open-weight models from @MiniMax or @Mistral AI for example?
@fmerian Haven't tested MiniMax or Mistral specifically for long-form fiction generation, but my experience is that open-weight models still struggle with maintaining story coherence across chapters compared to top closed models.
For zz-novel (AI novel writing tool I'm building), we ended up going with Gemini 2.5 Pro — the extended context window and instruction-following quality made a real difference for multi-chapter narrative consistency. Curious if anyone's had better results with open-weight for creative writing use cases though!
Voted Claude. Been using Sonnet and Opus daily for the past few months while building solo and for coding tasks specifically nothing else comes close in terms of understanding context and getting the architecture right on the first pass.
That said, GPT leading the poll doesn't surprise me. It's still the default for most people and it's genuinely good at a wide range of tasks. The gap between the top models is shrinking fast though , six months ago this poll would've been way more lopsided.
Honestly the "best model" question is becoming less about which one is objectively better and more about which one fits how you think. Some people click with how Claude reasons through problems, others prefer GPT's style. At this point the tooling around the model matters almost as much as the model itself.
With the top-of-the-line models, it's definitely a matter of personal preference. My personal favorite is clearly Opus 4.6. My experience with local models—running on an M1 Mac Studio Ultra (e.g., Qwen-3-next 80b)—is that it's only worthwhile if you absolutely have to work on-site. Of course, if you have the performance of two of the latest, fully equipped Mac Studios, the situation is different. But then you're also looking at an additional €20,000. Smaller local models simply don't deliver the same results when it comes to decision-making (main agent).
@fmerian Claude Opus 4.6 with extended thinking for everything. Full stop.
We run it across virtually every workflow, coding, security analysis, content, strategy, architecture, all of it. The only exceptions are basic boilerplate and simple admin tasks. The cost premium is worth it because the accuracy and output quality eliminates the rework cycles you'd burn through with cheaper models. You save more in iteration time than you spend on tokens.
We're consistently hitting 95%+ accuracy across tasks, but here's the part nobody talks about: that's not just the model. We've built an extensive custom context memory and state management system that feeds the model exactly what it needs across sessions. Persistent memory, governance documents, project state, lessons learned, all structured and available in every interaction.
This is the biggest gap in how most people use these models. Even Opus 4.6 in extended thinking will underperform if you're starting every conversation from zero with no context, no memory, and no structured state. The model is the engine but the context system is the fuel. Without it, every model, no matter how powerful, is reasoning in a vacuum. The teams investing in memory architecture and context management are getting dramatically better results than teams just picking the "best model" and hoping for the best.
The model matters. How you feed it matters more.
I've been running Opus 4.6 for most work - coding, research, content drafting. The context window handles big projects well and I like how it reasons through multi-step tasks.
For quick one-off stuff I'll sometimes use Sonnet 4.5 to save cost, but when something matters I go back to Opus. The quality gap shows up fast on anything that requires judgment.
Honestly, I think the best model really comes down to whatever fits
the way you work. I started out on ChatGPT, moved over to Gemini for a
bit, and these days I'm mostly on Opus — for my workflow,
it's been a good fit, especially for longer coding sessions.
When I've got some downtime I'll test out new releases from the other
players, but it always takes a while to get comfortable with a new setup.
I never quite get around to trying everything, and that's the part that
bugs me a little — the space moves faster than I can keep up with.
Voted Claude, but I don't think there's a wrong answer here. Depends on
what you're optimizing for.
@fmerian given that I already work with Claude and assuming I would continue to leverage mostly Claude (since I don't pay for the other models - only the free plans), what are the things that OpenClaw does that Claude Co-Work and Code don't and would justify the set-up of this addl. layer? Really glad to get this view, as I am still considering whether to do it.