Trinity Large Thinking

Open source Arcee reasoning model with a 262K context window, 80K max output, and native reasoning and tool support for agentic workloads.

  • Reasoning
  • Tool Calling

Added Apr 1, 2026

Model weights

Pricing

Auto routing · per 1M tokens
Input
$0.25
Output
$0.90
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Specifications

Context window
262.1K
Max output
80K
Parameters
400B / 13B
Total / active
Avg output (7d)
555 tokens
Longer than 48% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

10.8

Better than 42% of models compared

Coding Index

25.8

Better than 32% of models compared

Agentic Index

1.0

Better than 16% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

1.3%

Better than 16% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

251 Elo

Better than 15% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

340 Elo

Better than 19% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

1.2%

Better than 9% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

38.0%

Better than 35% of models compared

Reasoning

HLE

Humanity's Last Exam

15.8%

Better than 63% of models compared

IFBench

Instruction-following benchmark

56.3%

Better than 67% of models compared

CritPt

Research-level physics reasoning

0.9%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.5%

Better than 35% of models compared

SciCode

Python programming for scientific computing

40.6%

Better than 29% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

22.5%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

85.9%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

75.2%

Better than 57% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

22.7%

Better than 62% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

90.1%

Better than 84% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

38.0%

Better than 35% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

0.0%

Last updated Oct 3, 2026

Artificial Analysis

Providers

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