Trinity-Large-Thinking
trinity-large-thinking
Last refreshed 2026-05-11. Next refresh: weekly.
Trinity-Large-Thinking is worth evaluating for rag, agents, and long context when its provider route and context window match the workload.
Decision context: RAG task fit, 2 tracked provider routes, and research from 2026-04-19.
Use it for
- Teams evaluating rag, agents, and long context
- Workloads that can use a 256K context window
- Buyers comparing 2 tracked provider routes
Do not use it for
- Vision or document-understanding workloads
Cheapest output
$0.850
OpenRouter per 1M tokens
Provider routes
2
Tracked API hosts
Quality / dollar
Unknown
No task benchmark coverage yet
Freshness
2026-04-19
Researched 32d ago
Top use-case fit
RAG
Included by capability and metadata signals in the decision map.
Agents
Included by capability and metadata signals in the decision map.
Long context
Included by capability and metadata signals in the decision map.
Provider price ladder
Compare all 2| Provider | Input / 1M | Output / 1M | Route |
|---|---|---|---|
| OpenRouter | $0.220 | $0.850 | Serverless |
| Arcee AI | - | - | Partial |
Benchmark peer barsfor RAG
No task-mapped benchmark peers are available for this model yet.
Migration checks
No linked migration route is available for this model yet.
About
Arcee AI's flagship 400B sparse MoE reasoning model with 13B active parameters per token. Trained on 20T tokens with a STEM-focused curriculum. Designed for agentic workflows, chain-of-thought reasoning, and long-context tasks up to 256K tokens (BF16 API). Open-source under Apache 2.0. Available via Arcee AI API.
Trinity-Large-Thinking has a 256K-token context window.
Trinity-Large-Thinking input tokens at $0.22/1M, output at $0.85/1M.
Capabilities
Benchmark Scores(1)
| Benchmark | Score | Version | Source |
|---|---|---|---|
| Google-Proof Q&A | 89.2 | diamond | https://docs.arcee.ai/language-models/trinity-large-thinking |