Arcee Trinity-Large-Thinking vs Gemma 4 (Google)

Which one should you pick? Here's the full breakdown.

Arcee Trinity-Large-Thinking

A
8.1/10

Arcee AI's US-made open-weight frontier reasoning model -- launched 2026-04-01. 398B total params, ~13B active. Sparse MoE (256 experts, 4 active = 1.56% routing). Apache 2.0, trained from scratch. #2 on PinchBench trailing only Claude 3.5 Opus. ~96% cheaper than Opus-4.6 on agentic tasks

Our Pick

Gemma 4 (Google)

A
8.3/10

Google DeepMind's open-weights model family -- multimodal, 256K context, runs on edge devices

CategoryArcee Trinity-Large-ThinkingGemma 4 (Google)
Ease of Use6.07.0
Output Quality9.08.0
Value9.510.0
Features8.08.0
Overall8.18.3

Pricing Comparison

FeatureArcee Trinity-Large-ThinkingGemma 4 (Google)
Free TierYesYes
Starting Price$0$0

Benchmark Head-to-Head

Gemma 4 31B benchmarks — Arcee Trinity-Large-Thinking has no published benchmarks

BenchmarkScore
MMLU83%
GPQA Diamond84.3%
AIME 202689.2%
HumanEval85%

Which Should You Pick?

Pick Arcee Trinity-Large-Thinking if...

  • Higher output quality (9 vs 8)

Teams that need a US-made, Apache 2.0, frontier-tier open-weight model and can either rent multi-GPU infrastructure or pay OpenRouter API pricing at ~$0.90/M output tokens. Particularly valuable for US government, defense, or regulated enterprise contexts where country-of-origin matters for procurement. Also good for agentic reasoning workloads where the ~96% cost savings vs Claude Opus actually changes what you can build.

Visit Arcee Trinity-Large-Thinking

Pick Gemma 4 (Google) if...

  • Easier to use (7 vs 6)

Developers and businesses who need a permissively licensed multimodal LLM they can self-host or fine-tune. Especially good for multilingual use cases and on-device deployment.

Visit Gemma 4 (Google)

Our Verdict

Arcee Trinity-Large-Thinking and Gemma 4 (Google) are extremely close overall. Your choice comes down to specific needs -- Arcee Trinity-Large-Thinking is better for teams that need a us-made, apache 2, while Gemma 4 (Google) works best for developers and businesses who need a permissively licensed multimodal llm they can self-host or fine-tune.