Arcee Trinity-Large-Thinking vs MiniMax M2 / M2.5
Which one should you pick? Here's the full breakdown.
Arcee Trinity-Large-Thinking
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
MiniMax M2 / M2.5
MiniMax's open-weights frontier -- first open model to match Claude Opus 4.6 on SWE-Bench at 10-20× lower cost
| Category | Arcee Trinity-Large-Thinking | MiniMax M2 / M2.5 |
|---|---|---|
| Ease of Use | 6.0 | 6.5 |
| Output Quality | 9.0 | 9.0 |
| Value | 9.5 | 9.5 |
| Features | 8.0 | 8.5 |
| Overall | 8.1 | 8.4 |
Pricing Comparison
| Feature | Arcee Trinity-Large-Thinking | MiniMax M2 / M2.5 |
|---|---|---|
| Free Tier | Yes | Yes |
| Starting Price | $0 | $0 |
Benchmark Head-to-Head
MiniMax M2.5 (230B/10B active MoE) benchmarks — Arcee Trinity-Large-Thinking has no published benchmarks
| Benchmark | Description | Score |
|---|---|---|
| MMLU-Pro | Harder multi-subject reasoning | 82.1% |
| GPQA Diamond | Graduate-level science questions | 76.8% |
| SWE-Bench Verified | 80.2% | |
| HumanEval | Python code generation | 91% |
| AIME 2025 | 85.3% |
Which Should You Pick?
Pick Arcee Trinity-Large-Thinking if...
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-ThinkingPick MiniMax M2 / M2.5 if...
Agentic coding and tool-use workflows on a budget. Best price-to-SWE-Bench ratio of any open-weights model in 2026.
Visit MiniMax M2 / M2.5Our Verdict
MiniMax M2 / M2.5 edges out Arcee Trinity-Large-Thinking with a 8.4 vs 8.1 overall score. Both are solid picks, but MiniMax M2 / M2.5 has the advantage in features.