Trinity-Large-Thinking

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Trinity-Large-Thinking

Introduction

Trinity-Large-Thinking is a reasoning-optimized variant of Arcee AI's Trinity-Large family — a 398B-parameter sparse Mixture-of-Experts (MoE) model with approximately 13B active parameters per token. Built on Trinity-Large-Base and post-trained with extended chain-of-thought reasoning and agentic RL, Trinity-Large-Thinking delivers state-of-the-art performance on agentic benchmarks while maintaining strong general capabilities.

Trinity-Large-Thinking generates explicit reasoning traces wrapped in ... blocks before producing its final response. This thinking process is critical to the model's performance — thinking tokens must be kept in context for multi-turn conversations and agentic loops to function correctly.

Try it at chat.arcee.ai

More details on the training of Trinity Large are available in the technical report.

Key Highlights

• Agentic-first design: Purpose-built for tool calling, multi-step planning, and agent workflows
• State-of-the-art agentic performance: 94.7% on τ²-Bench, 91.9% on PinchBench, 98.2% on LiveCodeBench
• Native reasoning traces: Extended chain-of-thought via ... blocks
• Compatible with major agent frameworks: Works out of the box with OpenClaw and Hermes Agent
• Ready to use on OpenRouter: No setup required — full reasoning and tool calling support via API

Model Variants

The Trinity Large family consists of four checkpoints:

• Trinity-Large-Thinking (this release): Reasoning-optimized, agentic post-training with extended chain-of-thought
• Trinity-Large-Preview: Lightly post-trained, chat-ready instruct model (no reasoning_content).
• Trinity-Large-TrueBase: 10T-token pre-anneal pretraining checkpoint
• Trinity-Large-Base: Full 17T-token pretrained foundation model with mid-training anneals

Architecture

Trinity-Large-Thinking shares the same sparse MoE architecture as Trinity-Large-Preview.

Benchmarks

*All models evaluated in mini-swe-agent-v2

Thinking-in-Context: Important Usage Note

Trinity-Large-Thinking produces reasoning traces inside ... blocks before generating its final response.

This means:

1. Multi-turn conversations: When building chat applications, include the full assistant response (thinking + answer) in the conversation history for subsequent turns.
2. Agentic loops: When using Trinity-Large-Thinking as the backbone of an agent (OpenClaw, Hermes Agent, or custom), ensure your tool-calling loop preserves reasoning in the message history between steps.
3. Context window management: The 512k extended context window accommodates long reasoning chains across many agentic steps. If you must truncate history, prefer removing older turns entirely rather than stripping thinking tokens from recent turns.

How thinking works

The model reasons internally before producing its response. When served via vLLM, the reasoning is separated into a dedicated field in the API response:

// API response structure
{
  "message": {
    "role": "assistant",
    "reasoning_content": "The user wants flight information. I need to determine the date for next Tuesday, search for flights SFO → JFK, and filter by price < $300.",
    "content": "\n",
    "tool_calls": [{
      "function": {
        "name": "search_flights",
        "arguments": "{\"origin\": \"SFO\", \"destination\": \"JFK\", \"date\": \"2026-04-07\", \"max_price\": 300}"
      }
    }]
  }
}

Preserving reasoning in multi-turn conversations

When building multi-turn agentic loops, you must pass reasoning_content back on assistant messages in subsequent requests. The chat template reads this field and re-wraps it in ... tags during tokenization, maintaining the model's chain-of-thought across turns.

What happens if reasoning is omitted entirely? The model can lose prior chain-of-thought context. On simple tasks this may work fine, but on complex multi-step agentic tasks, the model can produce malformed tool calls (e.g., tool call XML appearing inside the reasoning field instead of as structured tool_calls). For best results, always preserve reasoning_content and use "" instead of null for content on tool-call turns.

For implementation details, pitfalls (reasoning vs reasoning_content), and Python/TypeScript examples, see Reasoning Traces.

Training Configuration

Pretraining

• Training tokens: 17