🚨 Create Your Own Custom Coding Agent - Mix Fable 5, GPT 5.6 sol and Grok 4.5! Our newest feature allows you to mix and match your favorite LLMs and create your custom coding agent Create your favorite cocktail such as - Fable 5 for hard coding - 5.6... sol for backend - Opus 4.8 for frontend - Grok 4.5 for easy coding You can also only choose cheap LLMs for a cost optimized experience. Your use custom agent in API, chat or our agent platformshow more

Pick your poison now, 5.6 sol for backend and grok for the easy bits, just don't expect the models to agree on the architecture.

🤔🤔🤔🤔

😄😜😄😜

The cocktail framing is right. Fable 5 for the harder architectural decisions, Sol when you need raw throughput on backend. Muse Spark is worth adding as the fast, cheap third lane for high-volume, lower-stakes generation.

Gm guys 😁😊

Mixing models per task like that is such a smart way to balance cost and quality.

More models = more latency. one routed right wins every time

Lol

mixing fable 5 and grok 4.5 really does sound fun

The “cocktail” gets interesting at the handoff. If Fable plans and 5.6 Sol builds, can I inspect which constraints crossed the boundary and which model changed them? Model mixing without a shared task ledger will be hard to debug.

😲😲😲

Hey

Clever and interested way to wrap things up. Cool!

The future of AI development isn't about one model winning it's about combining the strengths of multiple models to build faster, smarter, and more cost-efficient workflows.

Which is best for Research

Looks interesting

This is actually genius Mixing Fable for the tough stuff and Grok 4.5 for the easy/fast parts is the perfect cocktail. Finally someone shipping real multi-LLM workflows!

Believe in your self you capable of achieving anything , you set your mind to

I've build it too gemini codex and claude answering together wanna check.....solo dev.

Good morning

Nice 👍

Great feature. Giving developers control over model selection makes coding agents far more flexible and cost-effective.

Mixing Fable for hard coding with another model for backend still drops the repo index between calls every time I've tried it

Model-per-task is the right cost/skill axis. But reliability is orthogonal: a cocktail of authors still ships the confident-but-wrong API call. The fix isn't which model writes each part — it's routing one to review another's diff before you accept.

mixing models feels really useful

Do these cocktails work?

Is this useful for me @grok

Ofe4vh

In the next year, we will get the next iteration on this as well. AI is becoming smarter by 50% every 5-6 months. No way humans can catch up.

Could we have options for thinking levels for each model?

.

Can I also add Claude 6 Ultra for my UI and GPT 7 for my database, or are we limited to just your current hallucinations? 🧠 Living in 2026 but benchmarking the 2030 lineup is a wild business model.

CR7 was already an Al even before Al started...✌️🙌