Capability
47.7/100
field median 58.2
#141 of 218 ranked models
Trinity-Large-Thinking
CurrentReleased Mar 10, 2026Open WeightReasoning512K context
Released Mar 10, 2026 — see all recent releases
Decision reading
Trinity-Large-Thinking scores 47.7 out of 100 and ranks #141 of 218. This profile shows 5 source-displayable benchmark rows; its strongest eligible category is Coding at #129. API pricing is $0.25 input and $0.9 output per million tokens.
Data as of August 15, 2026 · How the score is built
Strongest published evidence
Coding ranks #129. Particularly well-suited for software development and code generation tasks.
Validate before choosing
5 published rows leave some tracked benchmark slots empty.
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Price
$0.25input / $0.90 output
input median $1
blended $0.57
Speed
203tok/s
field median 94 tok/s
First token 11.07 s
Context
512Ktokens
field median 200,000
Reported for this model; direct source link not stored
Capability shape
Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.
Trinity-Large-Thinking category percentile values
- AgenticNot eligible
- Coding4th percentile
- ReasoningNot eligible
- KnowledgeNot eligible
- MathNot eligible
- MultilingualNot eligible
- MultimodalNot eligible
- Instruction followingNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- AgenticNot ranked
- Coding#129/135
- ReasoningNot ranked
- KnowledgeNot ranked
- MathNot ranked
- MultilingualNot ranked
- MultimodalNot ranked
- Inst. FollowingNot ranked
Top decileTop quartileMid-fieldNot eligible
What it costs to get this score
Published API price against the public score. The x-axis uses a log scale; the dashed path marks models that are not beaten by a cheaper, higher-scoring option. Price uses average of published input and output rates.
The chart opens on the current model. Scroll horizontally to inspect the full price axis.
Current modelTrinity-Large-Thinking · 47.7 score · $0.57 blended per million tokens
Horizontal: blended price per million tokens, log scale · Vertical: public score
How much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- Agentic1/1 verified
- Coding0/1 verified
- ReasoningNot measured
- Knowledge0/2 verified
- Math0/1 verified
- MultilingualNot measured
- MultimodalNot measured
- Inst. FollowingNot measured
Verified sourceProvisionalNot measured
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- Not published
- Context window
- 512K
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- Not sourced yet
- Cloud regions
- Not tracked yet
- Lifecycle
- Current
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing record
- Self-host
- Open weights available; hardware estimate not sourced
- Rate limits
- Not tracked yet
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.
Estimate VRAM from known parametersCategory score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticWeight 22%1 benchmarkVerified | Score pending | Not ranked | Not available | 22% | 1 benchmark | Verified |
| CodingRank #129 of 135Percentile 4thWeight 20%1 benchmarkReported | 31.2 | #129 of 135 | 4th | 20% | 1 benchmark | Reported |
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | Not ranked | Not available | 17% | 0 benchmarks | Not measured |
| KnowledgeWeight 12%2 benchmarksReported | Score pending | Not ranked | Not available | 12% | 2 benchmarks | Reported |
| MathWeight 5%1 benchmarkReported | Score pending | Not ranked | Not available | 5% | 1 benchmark | Reported |
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | Not ranked | Not available | 7% | 0 benchmarks | Not measured |
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | Not ranked | Not available | 5% | 0 benchmarks | Not measured |
Benchmark ledger
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Coding1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SWE-bench Verified*SWE-bench Verified (mini-swe-agent-v2) | Score63.2% | Versus best verified row | GapNo verified comparator | WeightDisplay only | Secondary exact |
Agentic1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Gert LabsGert Labs Composite Game Benchmark | Score32.55% | Versus best verified row Best verified: Claude Opus 4.8 · 72.97% | Gap40.4 behind | WeightDisplay only | Benchmark exact |
Knowledge2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| GPQA-DGPQA Diamond | Score76.3% | Versus best verified row Best verified: Sakana Fugu-Ultra · 95.5% | Gap19.2 behind | WeightDisplay only | Secondary exact |
| MMLU-Pro (Arcee)MMLU-Pro first-party comparison snapshot | Score83.4% | Versus best verified row Best verified: Trinity-Large-Preview · 75.2% | Gap8.2 behind | WeightDisplay only | Secondary exact |
Math1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| AIME25 (Arcee)AIME25 first-party comparison snapshot | Score96.3% | Versus best verified row Best verified: Trinity-Large-Preview · 24.0% | Gap72.3 behind | WeightDisplay only | Secondary exact |
Lineage
The sequence follows explicit supersedes links. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Mar 10, 2026 · you are here
Trinity-Large-ThinkingScore 47.7 · $0.25 / $0.9
Thinking
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
Trinity-Large-Thinking ranks #141 of 218 on the public leaderboard with a score of 47.71/100. Its source-verified position is #69 of 104.
Trinity-Large-Thinking is a open weight model with a 512K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
Official exact-value snapshot from Arcee AI's April 1, 2026 Trinity-Large-Thinking launch post. BenchLM stores the published chart values as display-only references so they do not overwrite the core weighted benchmark rows used elsewhere on the site.
Trinity-Large-Thinking sits in the Trinity Large family with Trinity-Large-Preview. 5 of 437 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Coding at #129. particularly well-suited for software development and code generation tasks.
Frequently asked questions
How does Trinity-Large-Thinking perform overall in AI benchmarks?
Trinity-Large-Thinking ranks #141 out of 218 models on the public BenchAlign leaderboard, with a score of 47.71/100. Its evidence status is Supported, and this profile shows 5 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is Trinity-Large-Thinking good for knowledge and understanding?
Trinity-Large-Thinking has source-displayable benchmark coverage for knowledge and understanding, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Trinity-Large-Thinking good for coding and programming?
Trinity-Large-Thinking ranks #129 out of 135 eligible models for coding and programming, with a public category score of 31.2/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Trinity-Large-Thinking good for mathematics?
Trinity-Large-Thinking has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Trinity-Large-Thinking good for agentic tool use and computer tasks?
Trinity-Large-Thinking has source-displayable benchmark coverage for agentic tool use and computer tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Trinity-Large-Thinking open source?
Trinity-Large-Thinking is an open-weight model from Arcee AI. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Which sibling models are related to Trinity-Large-Thinking?
Trinity-Large-Thinking belongs to the Trinity Large family. Related tracked variants include Trinity-Large-Preview. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.
Does Trinity-Large-Thinking have full benchmark coverage on BenchLM?
No. Trinity-Large-Thinking currently has 20 source-displayable rows across 437 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of Trinity-Large-Thinking?
Trinity-Large-Thinking has a reported context window of 512K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.
Last updated August 15, 2026. Runtime fields remain blank until a sourced snapshot exists.
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