Long documents
Prompts that approach the documented context limit
Trinity-Large-Thinking
Trinity-Large-Thinking has the larger documented context window.
Confidence: documented
Ornith-1.5-35B-A3B vs Trinity-Large-Thinking
Updated October 2, 2026. Rank says Trinity-Large-Thinking is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
Trinity-Large-Thinking has the higher public point estimate, 34.86 versus 32.61. Their conditional score ranges overlap. These ranges do not establish rank confidence. 1 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
34.86/100
Estimated · Public rank #146
Conditional range 25.1–44.6
- Shared results
- 1
- Ornith-1.5-35B-A3B only
- 17
- Trinity-Large-Thinking only
- 4
- Like-for-like categories
- 0 / 8
Estimated: Ornith-1.5-35B-A3B and 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Ornith-1.5-35B-A3B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Ornith-1.5-35B-A3B and Trinity-Large-Thinking are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedChat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallbackRepository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
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.
24.0Ornith-1.5-35B-A3B20.0Trinity-Large-Thinking
Directional only · BenchAlign v5.8
Ornith-1.5-35B-A3B has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
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.
17 Ornith-1.5-35B-A3B only
1 Shared
4 Trinity-Large-Thinking only
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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
Directional only- Ornith-1.5-35B-A3B
- 23.5
- Estimated · #95/119
- Trinity-Large-Thinking
- 12.7
- Estimated · #108/119
- Basis
- BenchAlign v5.8 lane · 7 vs 1 public rows
- Reading
- Directional only
Coding
Directional only- Ornith-1.5-35B-A3B
- 24.0
- Estimated · #110/144
- Trinity-Large-Thinking
- 20.0
- Supported · #118/144
- Basis
- BenchAlign v5.8 lane · 7 vs 1 public rows
- Reading
- Directional only
Knowledge
Directional only- Ornith-1.5-35B-A3B
- 34.5
- Estimated · #119/171
- Trinity-Large-Thinking
- 33.7
- Estimated · #120/171
- Basis
- BenchAlign v5.8 lane · 4 vs 2 public rows
- Reading
- Directional only
Reasoning
Not comparable- Ornith-1.5-35B-A3B
- Not ranked
- Trinity-Large-Thinking
- 48.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Ornith-1.5-35B-A3B
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Ornith-1.5-35B-A3B
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Ornith-1.5-35B-A3B
- Not ranked
- Trinity-Large-Thinking
- 66.3
- #68/125
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Ornith-1.5-35B-A3B
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Ornith-1.5-35B-A3B | Trinity-Large-Thinking | Basis | Reading |
|---|---|---|---|---|
| Agentic | 23.5Estimated · #95/119 | 12.7Estimated · #108/119 | Directional onlyBenchAlign v5.8 lane · 7 vs 1 public rows | Directional only |
| Coding | 24.0Estimated · #110/144 | 20.0Supported · #118/144 | Directional onlyBenchAlign v5.8 lane · 7 vs 1 public rows | Directional only |
| Knowledge | 34.5Estimated · #119/171 | 33.7Estimated · #120/171 | Directional onlyBenchAlign v5.8 lane · 4 vs 2 public rows | Directional only |
| Reasoning | Not ranked | 48.3Unranked · 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 |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 66.3#68/125 | Not comparableProvisional lane · 0 vs 0 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
- Ornith-1.5-35B-A3B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Trinity-Large-Thinking
- $0.0007
- Fits in one request
Ornith-1.5-35B-A3B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Ornith-1.5-35B-A3B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Trinity-Large-Thinking
- $0.0152
- Fits in one request
Ornith-1.5-35B-A3B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Ornith-1.5-35B-A3B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- Trinity-Large-Thinking
- $0.064
- Fits in one request
- Cached input priced at the published list-input rate
Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate. Ornith-1.5-35B-A3B has no comparable published API token 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.
SpecificationOrnith-1.5-35B-A3BTrinity-Large-Thinking
Context window
Maximum documented context; output-token limits may be lower.
Ornith-1.5-35B-A3B
Trinity-Large-Thinking
512K
API model ID
Ornith-1.5-35B-A3B
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.
Ornith-1.5-35B-A3B
No comparable hosted API rate
Ornith-1.5-35B-A3B model cardTrinity-Large-Thinking
Not published
Documented inputs
Ornith-1.5-35B-A3B
Not sourced
Trinity-Large-Thinking
Not sourced
Documented outputs
Ornith-1.5-35B-A3B
Not sourced
Trinity-Large-Thinking
Not sourced
Provider availability
Ornith-1.5-35B-A3B
Not sourced
Trinity-Large-Thinking
Not sourced
Reasoning profile
Ornith-1.5-35B-A3B
Reasoning
Trinity-Large-Thinking
Reasoning
Weight access
Ornith-1.5-35B-A3B
Open Weight
Trinity-Large-Thinking
Open Weight
License
Ornith-1.5-35B-A3B
Open Weight
Trinity-Large-Thinking
Open Weight
Release date
Ornith-1.5-35B-A3B
2026-08-18
Trinity-Large-Thinking
2026-03-10
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- Trinity-Large-Thinking has the higher public point estimate, 34.86 versus 32.61. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Trinity-Large-Thinking has the larger documented window (512K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Ornith-1.5-35B-A3B or Trinity-Large-Thinking?
Trinity-Large-Thinking has the higher public point estimate, 34.86 versus 32.61. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Ornith-1.5-35B-A3B or Trinity-Large-Thinking?
Ornith-1.5-35B-A3B scores higher for coding on the public lane, 24 to 20. Ornith-1.5-35B-A3B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Ornith-1.5-35B-A3B or Trinity-Large-Thinking?
Ornith-1.5-35B-A3B scores higher for agentic tasks on the public lane, 23.5 to 12.7. Ornith-1.5-35B-A3B and Trinity-Large-Thinking are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Ornith-1.5-35B-A3B or Trinity-Large-Thinking?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, Ornith-1.5-35B-A3B or Trinity-Large-Thinking?
Trinity-Large-Thinking has the larger documented context window: 512K, compared with 262K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence22 rows
Agentic
Terminal-Bench 2.1
Ornith-1.5-35B-A3B67.8%SourceTrinity-Large-Thinking—Not directly comparable
HLE w/ tools
Ornith-1.5-35B-A3B33.4%SourceTrinity-Large-Thinking—Not directly comparable
MCP Atlas
Ornith-1.5-35B-A3B70.2%SourceTrinity-Large-Thinking—Not directly comparable
Toolathlon-Verified
Ornith-1.5-35B-A3B48.7%SourceTrinity-Large-Thinking—Not directly comparable
WideResearch
Ornith-1.5-35B-A3B67.8%SourceTrinity-Large-Thinking—Not directly comparable
BrowseComp
Ornith-1.5-35B-A3B67.6%SourceTrinity-Large-Thinking—Not directly comparable
Claw-Eval
Ornith-1.5-35B-A3B72.5%SourceTrinity-Large-Thinking—Not directly comparable
Gert Labs
Ornith-1.5-35B-A3B—Trinity-Large-Thinking32.55%SourceNot directly comparable
Coding
Terminal-Bench 2.1
Ornith-1.5-35B-A3B67.8%SourceTrinity-Large-Thinking—Not directly comparable
SWE-bench Verified
Ornith-1.5-35B-A3B79%SourceTrinity-Large-Thinking—Not directly comparable
SWE-bench Pro
Ornith-1.5-35B-A3B59.6%SourceTrinity-Large-Thinking—Not directly comparable
SWE Multilingual
Ornith-1.5-35B-A3B71.4%SourceTrinity-Large-Thinking—Not directly comparable
DeepSWE
Ornith-1.5-35B-A3B22.0%SourceTrinity-Large-Thinking—Not directly comparable
frontierBench
Ornith-1.5-35B-A3B5.1%SourceTrinity-Large-Thinking—Not directly comparable
NL2Repo
Ornith-1.5-35B-A3B46.2%SourceTrinity-Large-Thinking—Not directly comparable
SWE-bench Verified*
Ornith-1.5-35B-A3B—Trinity-Large-Thinking63.2%SourceNot directly comparable
Knowledge
GPQA
Ornith-1.5-35B-A3B89.2%SourceTrinity-Large-Thinking—Not directly comparable
GPQA-D
Ornith-1.5-35B-A3B89.2%SourceTrinity-Large-Thinking76.3%SourceOrnith-1.5-35B-A3B leads this result
HLE
Ornith-1.5-35B-A3B25.6%SourceTrinity-Large-Thinking—Not directly comparable
HLE w/o tools
Ornith-1.5-35B-A3B25.6%SourceTrinity-Large-Thinking—Not directly comparable
MMLU-Pro (Arcee)
Ornith-1.5-35B-A3B—Trinity-Large-Thinking83.4%SourceNot directly comparable
Math
AIME25 (Arcee)
Ornith-1.5-35B-A3B—Trinity-Large-Thinking96.3%SourceNot directly comparable
22 public results · 1 shared
Watch Ornith-1.5-35B-A3B vs Trinity-Large-Thinking
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Last updated October 2, 2026