Chat turn cost
1K fresh input + 500 output tokens
Trinity-Large-Thinking
Trinity-Large-Thinking has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Trinity-Large-Preview vs Trinity-Large-Thinking
Updated October 19, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty. This is a same-family comparison, so migration details appear when the source data supports them.
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Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Model B
Arcee AI
34.48/100
Estimated · Public rank #160
Conditional range 24.8–44.2
- Shared results
- 3
- Trinity-Large-Preview only
- 1
- Trinity-Large-Thinking only
- 4
- Like-for-like categories
- 0 / 8
Estimated: Trinity-Large-Thinking. Conditional ranges do not establish rank confidence.How the comparison works
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Trinity-Large-Thinking
Trinity-Large-Thinking has the lower estimated token cost for this stated workload. Trinity-Large-Preview has no published cached-input rate, so cached tokens use its listed input rate. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Trinity-Large-Thinking
Trinity-Large-Thinking has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Trinity-Large-Preview is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Trinity-Large-Preview is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
—Trinity-Large-Preview17.9Trinity-Large-Thinking
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 Trinity-Large-Preview only
3 Shared
4 Trinity-Large-Thinking only
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Agentic
Not comparable- Trinity-Large-Preview
- Not ranked
- Trinity-Large-Thinking
- 14.0
- Estimated · #119/130
- Basis
- BenchAlign v5.8 lane · 0 vs 2 public rows
- Reading
- Not comparable
Coding
Not comparable- Trinity-Large-Preview
- Not ranked
- Trinity-Large-Thinking
- 17.9
- Supported · #125/151
- Basis
- BenchAlign v5.8 lane · 0 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Trinity-Large-Preview
- Not ranked
- Trinity-Large-Thinking
- 51.1
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Trinity-Large-Preview
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Trinity-Large-Preview
- Not ranked
- Trinity-Large-Thinking
- 34.2
- Estimated · #139/186
- Basis
- BenchAlign v5.8 lane · 3 vs 2 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Trinity-Large-Preview
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Trinity-Large-Preview
- Not ranked
- Trinity-Large-Thinking
- 28.9
- #132/148
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- Trinity-Large-Preview
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Trinity-Large-Preview | Trinity-Large-Thinking | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 14.0Estimated · #119/130 | Not comparableBenchAlign v5.8 lane · 0 vs 2 public rows | Not comparable |
| Coding | Not ranked | 17.9Supported · #125/151 | Not comparableBenchAlign v5.8 lane · 0 vs 1 public rows | Not comparable |
| Reasoning | Not ranked | 51.1Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 34.2Estimated · #139/186 | Not comparableBenchAlign v5.8 lane · 3 vs 2 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 28.9#132/148 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- Trinity-Large-Preview
- $0.00075
- Fits in one request
- Trinity-Large-Thinking
- $0.0007
- Fits in one request
Trinity-Large-Thinking has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Trinity-Large-Preview
- $0.0155
- Fits in one request
- Trinity-Large-Thinking
- $0.0152
- Fits in one request
Trinity-Large-Thinking has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Trinity-Large-Preview
- $0.065
- Fits in one request
- Cached input priced at the published list-input rate
- Trinity-Large-Thinking
- $0.064
- Fits in one request
- Cached input priced at the published list-input rate
Trinity-Large-Thinking has the lower modeled cost
Trinity-Large-Preview has no published cached-input rate, so cached tokens use its listed input rate. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationTrinity-Large-PreviewTrinity-Large-Thinking
Context window
Maximum documented context; output-token limits may be lower.
Trinity-Large-Preview
512K
Trinity-Large-Thinking
512K
API model ID
Trinity-Large-Preview
Not sourced
Trinity-Large-Thinking
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Trinity-Large-Preview
Not published
Trinity-Large-Thinking
Not published
Documented inputs
Trinity-Large-Preview
Not sourced
Trinity-Large-Thinking
Not sourced
Documented outputs
Trinity-Large-Preview
Not sourced
Trinity-Large-Thinking
Not sourced
Provider availability
Trinity-Large-Preview
Not sourced
Trinity-Large-Thinking
Not sourced
Reasoning profile
Trinity-Large-Preview
Non-Reasoning
Trinity-Large-Thinking
Reasoning
Weight access
Trinity-Large-Preview
Open Weight
Trinity-Large-Thinking
Open Weight
License
Trinity-Large-Preview
Open Weight
Trinity-Large-Thinking
Open Weight
Release date
Trinity-Large-Preview
2026-01-27
Trinity-Large-Thinking
2026-03-10
If you are choosing between sibling variants
- Deployment change
- Both entries list Arcee AI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.0155 vs $0.0152. Cache-heavy agent loop: $0.065 vs $0.064.
- Context tradeoff
- Both models list 512K.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Trinity-Large-Preview or Trinity-Large-Thinking?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Trinity-Large-Preview or Trinity-Large-Thinking?
Trinity-Large-Preview is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Trinity-Large-Preview or Trinity-Large-Thinking?
Trinity-Large-Preview is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Trinity-Large-Preview or Trinity-Large-Thinking?
For the stated presets, chat costs $0.00075 on Trinity-Large-Preview and $0.0007 on Trinity-Large-Thinking; repository review costs $0.0155 and $0.0152; the cache-heavy agent loop costs $0.065 and $0.064. Trinity-Large-Preview has no published cached-input rate, so cached tokens use its listed input rate. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Trinity-Large-Preview or Trinity-Large-Thinking?
Both models list the same context window, 512K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Trinity-Large-Preview
API / mo$938
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Trinity-Large-Thinking
API / mo$863
Self-host / mo$13,981
Break-even1.0B/day
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence8 rows
Agentic
Coding
SWE-bench Verified*
Trinity-Large-Preview—Trinity-Large-Thinking63.2%SourceNot directly comparable
Knowledge
MMLU
Trinity-Large-Preview87.2%SourceTrinity-Large-Thinking—Not directly comparable
MMLU-Pro (Arcee)
Trinity-Large-Preview75.2%SourceTrinity-Large-Thinking83.4%SourceTrinity-Large-Thinking leads this result
GPQA-D
Trinity-Large-Preview63.3%SourceTrinity-Large-Thinking76.3%SourceTrinity-Large-Thinking leads this result
Instruction following
IFBench
Trinity-Large-Preview—Trinity-Large-Thinking52.3%SourceNot directly comparable
Math
8 public results · 3 shared
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Last updated October 19, 2026