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Trinity-Large-Thinking

399B open MoE reasoning model that's 96% cheaper than Claude Opus

Price$0.90/M output tokens (Arcee API) / Free weights (Apache 2.0)Reviewed2026-04-03

Expert verdict

Ship

3-1
3 Ships1 Skips
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The Panel's Take

Trinity-Large-Thinking is a 399-billion-parameter open mixture-of-experts (MoE) reasoning model from Arcee AI, released under Apache 2.0. It's designed specifically for long-horizon multi-turn tool use and autonomous agentic tasks — thinking before responding with an explicit reasoning chain. The model ranked #2 on PinchBench (behind only Claude Opus 4.6) while costing $0.90/M output tokens via the Arcee API — roughly 96% cheaper than Opus. The full weights are freely downloadable from Hugging Face, making it one of the most capable openly-downloadable models available anywhere. Architecturally it draws on MoE efficiency to activate only a fraction of parameters per forward pass, enabling the massive 399B count without proportional compute cost. For teams building production agents that need serious reasoning but can't afford closed-model pricing at scale, Trinity-Large-Thinking is the most compelling open alternative that's appeared in a long time.

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Trinity-Large-Thinking verdict: SHIP 🚀

3 ships · 1 skip from the expert panel

Full review: shiporskip.io/tool/arcee-trinity-large-thinking-399b

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Ship · 7.5/10
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The reviews

Near-Opus-level reasoning at $0.90/M tokens is the pricing inflection I've been waiting for. Apache 2.0 weights mean I can self-host for compliance-sensitive use cases. Already benchmarking it as a drop-in for my agent evaluation pipeline.

Helpful?

Preview weights and PinchBench rankings tell part of the story — real-world agentic performance on messy production tasks is another matter. Arcee AI isn't Anthropic or Google; sustaining a 399B model with quality ongoing RLHF is expensive and the preview label is a yellow flag.

Helpful?

A US-built, Apache-licensed frontier reasoning model competitive with closed offerings fundamentally changes the open-source AI landscape. The talent and capital required to do this was thought to only exist at the biggest labs. Arcee just proved otherwise.

Helpful?

The thinking chain output is remarkably coherent for creative briefs and long-form narrative planning. At this price point I can run draft-then-refine pipelines at scale without budget anxiety. A genuine Ship for creative workflows.

Helpful?

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