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ModelCap

Trinity Large Thinking

Current board #111Arena #185Official publisher releaseRestricted license

Arcee AI·arcee-ai/trinity-large-thinking

Trinity Large Thinking is ModelCap current-board rank #111 with Index 45.9 (Measured), from the ModelCap Index as of 22 Sept 2026, 02:49 UTC.

Trinity Large Thinking listed OpenRouter input price is $0.25 per 1M tokens and listed output price is $0.80 per 1M tokens, from OpenRouter listed prices as of 22 Sept 2026, 02:49 UTC.

Trinity Large Thinking has a published context limit of 262K tokens in the ModelCap catalogue as of 22 Sept 2026, 02:49 UTC.

Trinity Large Thinking has 1 published OpenRouter serving endpoint, from OpenRouter as of 22 Sept 2026, 02:49 UTC.

Trinity Large Thinking weight access is restricted license, from ModelCap classification of published repository metadata as of 22 Sept 2026, 02:49 UTC.

Snapshot facts · download the public dataset · methodology

ModelCap Index · Current board
45.9#111
Measured · 82% support · score interval 39.6–52.2
Evidence as of 19 Sept 2026, 22:46 UTC
Market Gravity
22.7Decreased by 0.1
Secondary market signal
Input
$0.25
per 1M tokens
Output
$0.80
per 1M tokens
Context
262K
tokens
Providers
1
endpoint
Gateway spend
latest · Vercel share

Providers

Uptime measured over the last 30 minutes
ProviderInputOutputContextQuantUptime
Arcee AI$0.25$0.80262K100.0%

Reported evaluation leads (4)

Not independent measurement

Structured rows reported in this exact model repository at the pinned revision below. They are the publisher talking about itself: never measured evidence, never a score input on their own. They remain quarantined until dataset revision, harness, configuration and identity pass ModelCap’s independent admission review.

Dataset / taskMetricValueRevisionProvenance
collinear-ai/yc-bench
medium
32,667
Unpinned
not published
Reported
Quarantined
Idavidrein/gpqa
diamond
76.3
Unpinned
not published
Reported
Quarantined
SWE-bench/SWE-bench_Verified
swe_bench_%_resolved
63.2
Unpinned
not published
Reported
Quarantined
TIGER-Lab/MMLU-Pro
mmlu_pro
83.4
Unpinned
not published
Reported
Quarantined

ModelCap Index · Current board

Methodology
45.9
Measured · 3 public benchmark observations across 3 boards
Current board
#111
Public rank basis
Measured (measured)
Index support
82.0% · strong
Index interval
39.6–52.2
Rank posterior
Not available
Top 5 / Top 10 probability
Not available / Not available
Posterior as of
Snapshot timestamp unavailable
Evidence as of
19 Sept 2026, 22:46 UTC
Identity binding
Exact catalogue product · arcee-ai/arcee-ai/trinity-large-thinking · aggregates disclosed benchmark configurations · endpoint configuration not claimed · artifact metadata arcee-ai/Trinity-Large-Thinking@dc6a99b6b74202880e31491a445a94884c97890c (not the evaluation revision)
Index observations used
3
Qualified Index evidence point
45.9
Bayesian Evidence Score · measured evidence input only

BES normalizes admitted public benchmark evidence for measured Index rows. Its legacy score and observables prior do not define the public language rank.

Observed capability
45.9
Evidence status
confirmed · 83% mass
General preference
46.6
Coding
40.9
Agents & tools
Reasoning
Evidence breadth
92%
Evidence coverage
82%
Market and catalogue signals · secondary, never the language Index rank
Usage (OpenRouter popularity)
6.7
Liquidity (providers × uptime)
35.8
Open reach (HF downloads)
Surface (context / tools / modalities)
75.3
Freshness
51.7
  • · OpenRouter popularity #219
  • · 1 live provider
  • · Catalogue: context 262144, reasoning, tools
  • · First seen 2026-04-01

These adoption and deployment observations remain context only. They do not change this model's ModelCap Index score or rank.

BES evidence selected · measured input only

The bounded 0–100 capability score blends each source’s competitive placement with its published achievement, then combines capability families. Evidence breadth adds a modest uncertainty adjustment; price and popularity are not part of benchmark-led rank.

Deployment readiness

Metadata index
84.7
strong · 98% metadata coverage
Not capability
Artifact reproducibility
100.0
Access & legal clarity
92.5
Deployability
74.0
Evaluation provenance
70.1

Missing: evaluationProvenance.evaluation-dataset-revisions.

A metadata completeness and deployability index—not a safety certification, quality grade, or production-readiness claim.

Market activity

Venue coverage
Market Gravity
22.7
OpenRouter weekly popularity
#219 of 322

Market Gravity uses OpenRouter’s full-catalogue popularity order. Sparse Vercel top-ten observations appear only when published and do not affect the score.

Arena preference

Status
Ranked
Source
LMArena
Official rank
#185 of 402
Rating
1369.0
95% confidence interval
1364–1374
Votes
29K
Source published at
13 Sept 2026, 00:00 UTC
Category
overall
Identity match confidence
98%

Market Gravity breakdown

Usage55%
6.7

OpenRouter weekly popularity across the full model catalogue.

Liquidity25%
35.8

Independent providers versus the model's open or closed cohort, weighted by uptime.

Open reach15%
50.0

Hugging Face 30-day downloads for open models; neutral for closed models.

Freshness5%
51.7

Time since first public availability, on a six-month half-life.

Specification

Context window
262K tokens
Max output
80K tokens
Inputs
Text
Outputs
Text
Tokenizer
Other
Cached input tokens
$0.06 / 1M
First seen on OpenRouter
1 Apr 2026

Capabilities

  • Supported: Reasoning
  • Supported: Tool use
  • Not supported: Structured output
  • Not supported: Response format
  • Not supported: Moderated

Weights & access

Restricted license. Downloadable weights use a model-specific or use-restricted license. Review the linked license before use, redistribution, or commercial deployment.

Access
Restricted license
Pinned revision
dc6a99b6b742
Parameters
398.6B
Architecture
AfmoeForCausalLM
Model type
afmoe
Hugging Face downloads (30d)
1K
Downloads (all time)
70K
Likes
189
Repository updated
28 May 2026, 22:45 UTC

Compare Trinity Large Thinking with

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Benchmark leaderboards featuring Trinity Large Thinking

All leaderboards
Public benchmark boards on which Trinity Large Thinking has a published result
LeaderboardScoreSource rankConfigurationPublished
Arena coding leaderboardArena (LMArena)14141407–1422#186 of 397Default13 Sept 2026
Artificial Analysis Intelligence IndexArtificial Analysis10.8#130 of 151intelligence-index:v4.3:reasoning-unspecified19 Sept 2026
τ²-Bench Telecom (Artificial Analysis run)Artificial Analysis90.1%#68 of 436tau2:telecom:dual-control:pass-at-1:3-repeats:source-model="Trinity Large Thinking":reasoning=true9 Sept 2026
Humanity's Last Exam (Artificial Analysis run)Artificial Analysis15.8%#213 of 615hle:may-2025:text-only-2158:no-tools:pass-at-1:source-model="Trinity Large Thinking":reasoning=true9 Sept 2026

Alternatives to Trinity Large Thinking, by the numbers

Ranked neighbours

Models within 3 places of #111 on the ModelCap Index.

Cheaper at a similar or higher Index

Index score within 3 points of, or above, this model's, with a lower listed blended price (3:1 input:output).

Other ranked Arcee AI models

The same publisher's other current models with a public Index position.

Trinity Large Thinking: common questions

How does Trinity Large Thinking rank among AI models?

Trinity Large Thinking holds ModelCap Index position #111 of 248 ranked language models as of 22 September 2026, with an Index score of 45.9 (interval 39.6–52.2); evidence: measured. The Index combines public benchmark boards with published uncertainty; the methodology page explains the weighting.

How much does Trinity Large Thinking cost per 1M tokens?

Trinity Large Thinking is listed at $0.25 per 1M input tokens and $0.80 per 1M output tokens as of 22 September 2026. Prices come from the OpenRouter catalogue and re-observe on every refresh. For 1,000 requests using 2,000 input and 500 output tokens each, the listed rates imply $0.90 total (2M input + 0.5M output). This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the chosen endpoint's rate and limits before budgeting.

What is the context window of Trinity Large Thinking?

Trinity Large Thinking has a published context window of 262K tokens (262,144) and a maximum output of 80K tokens. Individual providers can serve less than the published maximum; the providers table lists each endpoint's own limit.

Is Trinity Large Thinking open-weight?

Restricted license (openmdw-1.1). Downloadable weights use a model-specific or use-restricted license. Review the linked license before use, redistribution, or commercial deployment. The repository is arcee-ai/Trinity-Large-Thinking on Hugging Face.

Which API providers serve Trinity Large Thinking?

One provider serves Trinity Large Thinking through OpenRouter as of 22 September 2026: Arcee AI. Each provider's price, context limit, quantization and measured uptime are in the providers table above.

Which benchmarks has Trinity Large Thinking been evaluated on?

Trinity Large Thinking has published results on 4 public boards tracked by ModelCap as of 22 September 2026: Arena coding 1414 (#186 of 397), AA Intelligence Index 10.8 (#130 of 151), AA τ²-Bench Telecom 90.1% (#68 of 436) and AA Humanity's Last Exam 15.8% (#213 of 615). Each board page ranks every tracked model on that benchmark; the ModelCap Index combines them with published uncertainty.

How does Trinity Large Thinking compare with gemma 4 E4B it?

Trinity Large Thinking ranks #111 (Index 45.9) and gemma 4 E4B it ranks #112 (Index 45.8) on the ModelCap Index as of 22 September 2026. The head-to-head page lines up their benchmarks, listed prices, context windows and provider counts side by side.

When was Trinity Large Thinking released?

Trinity Large Thinking first appeared in the catalogue on 1 Apr 2026. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.