Arcee AI
Trinity Large Thinking
Tool callingReasoning

Reasoning-optimized 398B MoE agent model with extended thinking for long-horizon and multi-turn tool use
- Context
- 262K
- Released
- 6mo ago
- Parameters
- —
- Input / 1M tokens
- $0.25
- Output / 1M tokens
- $0.80
- Cached input / 1M tokens
- $0.06
Sources:models.devOpenRouter
Benchmarks
DeepSWE is the primary coding benchmark where an exact model evaluation exists. Other sourced evaluations appear below; missing scores are not zero.
Not evaluated in DeepSWE 1.1 as of 2026-09-22. Results are only shown for exact model versions in the official dataset. Official leaderboard.
No benchmark results have been matched to this model yet.
Specifications
- Developer
- Arcee AI
- Model family
- Trinity
- Type
- Language model
- Predecessor
- —
- Successor
- —
- Released
- April 1, 2026
- Model ID
arcee-ai/trinity-large-thinking- Openness
- Open weights
- License
- OpenMDW-1.1
- Parameters
- —
- Input modalities
- text
- Output modalities
- text
- Modality
- text->text
- Tokenizer
- Other
- Max output tokens
- 80K
- Hugging Face
- arcee-ai/Trinity-Large-Thinking
- Source freshness
- Metadata synced Oct 8, 2026, 10:17 PM UTC
Additional source evidence
- Source model
- arcee-ai/trinity-large-thinking
- Source family
- trinity
- Last updated
- 2026-05-28
- Model context
- 524K
- Model output limit
- 262K
- Open weights
- Yes
- File attachments
- No
- Reasoning
- Yes
- Tool calling
- Yes
- Temperature control
- Yes
Downloadable weights
- Hugging Facesafetensors
Official references
Complete source record (JSON)
{
"id": "arcee-ai/trinity-large-thinking",
"name": "Trinity Large Thinking",
"description": "Reasoning-optimized 398B MoE agent model with extended thinking for long-horizon and multi-turn tool use",
"family": "trinity",
"last_updated": "2026-05-28",
"attachment": false,
"reasoning": true,
"tool_call": true,
"temperature": true,
"modalities": {
"input": [
"text"
],
"output": [
"text"
]
},
"limit": {
"context": 524288,
"output": 262144
},
"release_date": "2026-04-01",
"open_weights": true,
"license": "OpenMDW-1.1",
"links": [
{
"url": "https://huggingface.co/arcee-ai/Trinity-Large-Thinking",
"label": "Model card",
"type": "model_card"
},
{
"url": "https://www.arcee.ai/blog/trinity-large-thinking",
"label": "Announcement",
"type": "announcement"
},
{
"url": "https://huggingface.co/arcee-ai/Trinity-Large-Thinking/blob/main/LICENSE",
"label": "License",
"type": "license"
}
],
"weights": [
{
"label": "Hugging Face",
"url": "https://huggingface.co/arcee-ai/Trinity-Large-Thinking",
"format": "safetensors"
}
]
}Infrastructure
Provider availability
| Provider | Endpoint | Context | Input /M tokens | Output /M tokens | Cached input /M tokens | Cache write /M tokens | Uptime 1d | Data handling |
|---|---|---|---|---|---|---|---|---|
| arcee-ai | 262K | $0.25 | $0.80 | $0.06 | — | 100.00% | ZDR: No |
Provider data:OpenRouter
Deployment graph
Can I run this?
Vertical Tabs only shows compatibility when it has structured evidence tying this exact model to hardware, runtime, quantization, memory use, and observed performance.