AI tool comparison
Arcee Trinity-Large-Thinking vs Ternary Bonsai
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Models
Arcee Trinity-Large-Thinking
399B open-weight reasoning model, 13B active params, Apache 2.0
75%
Panel ship
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Community
Paid
Entry
Arcee AI, a 30-person startup, has released Trinity-Large-Thinking — a 399B sparse mixture-of-experts reasoning model under Apache 2.0. Only 13B parameters activate per token, giving it inference speed 2-3x faster than comparable dense models. In internal benchmarks and early community testing, it ranks #2 on PinchBench, trailing only Anthropic's Opus 4.6, at a list price of $0.90/M output tokens — roughly 96% cheaper than frontier closed models. The model was trained in a $20M, 33-day run on 2,048 NVIDIA Blackwell GPUs. Arcee trained it using a constitutional AI-style process with synthetic chain-of-thought data generated from multiple frontier models, then applied a reinforcement learning phase using outcome-based rewards on math, code, and logic benchmarks. Trinity-Large-Thinking is the strongest open-weight reasoning model released to date on a commercial-friendly license. For companies with privacy requirements or custom deployment needs, it represents a credible alternative to frontier closed APIs — especially for code generation, mathematical reasoning, and structured data tasks where the gap between open and closed models has historically been widest.
Open Source Models
Ternary Bonsai
1.58-bit LLMs that run at 82 tok/s on M4 Pro and on your iPhone
75%
Panel ship
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Community
Free
Entry
PrismML's Ternary Bonsai is a family of aggressively quantized language models that take the BitNet concept to its logical extreme. Each weight is constrained to one of three values — {-1, 0, +1} — with a shared FP16 scale factor per 128-weight group. No higher-precision escape hatches, no hybrid layers. The result is a 9x reduction in memory footprint versus standard 16-bit models. The numbers are striking: the 8B model fits in 1.75 GB and hits 82 tokens per second on an M4 Pro. More impressively, it runs at 27 tokens per second on an iPhone 17 Pro Max — fast enough for real-time conversation on-device. The 8B variant scores 75.5 average across standard benchmarks, outperforming many models that are 9-10x larger. The 4B and 1.7B variants push further into mobile-optimized territory. All three models are released under the Apache 2.0 license, available on Hugging Face and GitHub, and integrated into the Locally AI iOS app for immediate on-device deployment. For developers building privacy-sensitive applications or anyone tired of paying cloud inference costs, Ternary Bonsai offers a compelling on-device alternative that doesn't require a beefy GPU.
Reviewer scorecard
“A #2 benchmark result from a 30-person startup under Apache 2.0 is legitimately shocking. The sparse MoE architecture means you can run 399B at a reasonable cost — and $0.90/M output is almost too cheap to believe for this performance tier. This is going in our eval suite immediately.”
“82 tokens per second on M4 Pro in 1.75 GB is a genuinely impressive engineering achievement. For local tooling, code assistants, or any latency-sensitive workload where I don't want cloud round-trips, this hits a sweet spot that larger quantized models miss. Apache 2.0 means I can embed it in commercial apps without legal headaches.”
“Benchmark numbers from the releasing company always look better than real-world deployment. PinchBench is also relatively new and the community hasn't stress-tested whether it correlates with production quality. Wait for independent evals before betting a product on this.”
“A 75.5 benchmark average sounds good until you compare it against 8B models quantized with GGUF Q8 — which score similarly and have years of tooling, community support, and production deployments behind them. The 9x memory savings matter on constrained devices but less so on any machine with 16GB+ RAM. Niche but real use case.”
“This is the model that closes the open vs. closed frontier gap. When a 30-person startup can train a near-frontier reasoner for $20M on a commercial license, the economics of AI completely change. Enterprises that couldn't afford frontier APIs will rebuild their stacks around self-hosted models like this.”
“On-device AI at 27 tokens per second on a phone is the inflection point that makes LLMs a platform primitive rather than a cloud service. Once inference is this cheap and fast on commodity hardware, the entire economic model of AI-as-API-call collapses. Ternary quantization is an early signal of where efficiency research is heading.”
“For long-form creative work requiring multi-step reasoning — worldbuilding, complex narrative planning, detailed research synthesis — a 399B model at this price point is transformative. The chain-of-thought always-on design means it actually shows its reasoning, which helps when I need to redirect it mid-task.”
“The prospect of running a capable LLM entirely on my iPhone without sending any data to a server is genuinely exciting for creative work with sensitive material. Drafting, editing, and ideation without a cloud subscription or privacy concerns — I'd pay for that, and here it's free.”
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