Agentic
Directional only- Ternary Bonsai 2 27B
- 49.9
- Estimated · #59/154
- Trinity-Large-Thinking
- 38.0
- Estimated · #127/154
- Basis
- BenchAlign lane · 3 vs 1 public rows
- Reading
- Directional only
Ternary Bonsai 2 27B vs Trinity-Large-Thinking
Decision reading
Ternary Bonsai 2 27B has the higher public score estimate, 50.78 versus 43.27, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Updated September 18, 2026. Rank says Ternary Bonsai 2 27B 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
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.
Long documents
Prompts that approach the documented context limit
Trinity-Large-Thinking
Trinity-Large-Thinking has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Ternary Bonsai 2 27B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Ternary Bonsai 2 27B and Trinity-Large-Thinking are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat 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-rates
Cache-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-fallback
Repository 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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
20 Ternary Bonsai 2 27B only
1 Shared
4 Trinity-Large-Thinking only
- Shared results
- 1
- Ternary Bonsai 2 27B only
- 20
- Trinity-Large-Thinking only
- 4
- Like-for-like categories
- 0 / 8
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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.
Coding
Directional only- Ternary Bonsai 2 27B
- 49.9
- Estimated · #64/154
- Trinity-Large-Thinking
- 27.3
- Supported · #148/154
- Basis
- BenchAlign lane · 4 vs 1 public rows
- Reading
- Directional only
Knowledge
Directional only- Ternary Bonsai 2 27B
- 50.6
- Estimated · #78/184
- Trinity-Large-Thinking
- 42.0
- Estimated · #126/184
- Basis
- BenchAlign lane · 3 vs 2 public rows
- Reading
- Directional only
Instruction following
Directional only- Ternary Bonsai 2 27B
- 71.0
- #64/124
- Trinity-Large-Thinking
- 66.3
- #68/124
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Ternary Bonsai 2 27B
- 73.9
- Unranked · 1 rankable row
- Trinity-Large-Thinking
- 47.1
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Ternary Bonsai 2 27B
- 76.8
- Unranked · 4 rankable rows
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Ternary Bonsai 2 27B
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Ternary Bonsai 2 27B
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Ternary Bonsai 2 27B | Trinity-Large-Thinking | Basis | Reading |
|---|---|---|---|---|
| Agentic | 49.9Estimated · #59/154 | 38.0Estimated · #127/154 | Directional onlyBenchAlign lane · 3 vs 1 public rows | Directional only |
| Coding | 49.9Estimated · #64/154 | 27.3Supported · #148/154 | Directional onlyBenchAlign lane · 4 vs 1 public rows | Directional only |
| Knowledge | 50.6Estimated · #78/184 | 42.0Estimated · #126/184 | Directional onlyBenchAlign lane · 3 vs 2 public rows | Directional only |
| Instruction following | 71.0#64/124 | 66.3#68/124 | Directional onlyProvisional lane · 1 vs 0 weighted rows | Directional only |
| Reasoning | 73.9Unranked · 1 rankable row | 47.1Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 76.8Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | 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 |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
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
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Trinity-Large-Thinking
- $0.0007
- Fits in one request
Ternary Bonsai 2 27B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Trinity-Large-Thinking
- $0.0152
- Fits in one request
Ternary Bonsai 2 27B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Ternary Bonsai 2 27B
- 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. Ternary Bonsai 2 27B has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationTernary Bonsai 2 27BTrinity-Large-Thinking
Context window
Maximum documented context; output-token limits may be lower.
Ternary Bonsai 2 27B
Trinity-Large-Thinking
512K
API model ID
Ternary Bonsai 2 27B
prism-ml/Ternary-Bonsai-2-27B-gguf
PrismML Bonsai 2 collectionTrinity-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.
Ternary Bonsai 2 27B
No comparable hosted API rate
PrismML Bonsai 2 collectionTrinity-Large-Thinking
Not published
Documented inputs
Ternary Bonsai 2 27B
Not sourced
Trinity-Large-Thinking
Not sourced
Documented outputs
Ternary Bonsai 2 27B
Not sourced
Trinity-Large-Thinking
Not sourced
Provider availability
Ternary Bonsai 2 27B
Not sourced
Trinity-Large-Thinking
Not sourced
Reasoning profile
Ternary Bonsai 2 27B
Reasoning
Trinity-Large-Thinking
Reasoning
Weight access
Ternary Bonsai 2 27B
Open Weight
Trinity-Large-Thinking
Open Weight
License
Ternary Bonsai 2 27B
Open Weight
Trinity-Large-Thinking
Open Weight
Release date
Ternary Bonsai 2 27B
2026-09-17
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
- Ternary Bonsai 2 27B has the higher public score estimate, 50.78 versus 43.27, but the 90% score intervals overlap.
- 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.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence25 rows
Agentic
τ²-bench results
Ternary Bonsai 2 27B80.2%SourceTrinity-Large-Thinking—Not directly comparable
BFCL v3
Ternary Bonsai 2 27B74.9%SourceTrinity-Large-Thinking—Not directly comparable
Terminal-Bench 2.1
Ternary Bonsai 2 27B52.8%SourceTrinity-Large-Thinking—Not directly comparable
Gert Labs
Ternary Bonsai 2 27B—Trinity-Large-Thinking32.55%SourceNot directly comparable
Coding
LiveCodeBench v6
Ternary Bonsai 2 27B90.1%SourceTrinity-Large-Thinking—Not directly comparable
BigCodeBench
Ternary Bonsai 2 27B58.1%SourceTrinity-Large-Thinking—Not directly comparable
SWE-bench Verified
Ternary Bonsai 2 27B60.8%SourceTrinity-Large-Thinking—Not directly comparable
Terminal-Bench 2.1
Ternary Bonsai 2 27B52.8%SourceTrinity-Large-Thinking—Not directly comparable
SWE-bench Verified*
Ternary Bonsai 2 27B—Trinity-Large-Thinking63.2%SourceNot directly comparable
Knowledge
MMLU-Redux
Ternary Bonsai 2 27B89.1%SourceTrinity-Large-Thinking—Not directly comparable
GPQA
Ternary Bonsai 2 27B85.8%SourceTrinity-Large-Thinking—Not directly comparable
GPQA-D
Ternary Bonsai 2 27B85.8%SourceTrinity-Large-Thinking76.3%SourceTernary Bonsai 2 27B leads this result
MMLU-Pro (Arcee)
Ternary Bonsai 2 27B—Trinity-Large-Thinking83.4%SourceNot directly comparable
Math
GSM8K
Ternary Bonsai 2 27B96.7%SourceTrinity-Large-Thinking—Not directly comparable
MATH-500
Ternary Bonsai 2 27B98.8%SourceTrinity-Large-Thinking—Not directly comparable
AIME 2025
Ternary Bonsai 2 27B95%SourceTrinity-Large-Thinking—Not directly comparable
AIME26
Ternary Bonsai 2 27B95.8%SourceTrinity-Large-Thinking—Not directly comparable
AIME25 (Arcee)
Ternary Bonsai 2 27B—Trinity-Large-Thinking96.3%SourceNot directly comparable
Multimodal
CharXiv (overall)
Ternary Bonsai 2 27B80.0%SourceTrinity-Large-Thinking—Not directly comparable
A-OKVQA
Ternary Bonsai 2 27B86.8%SourceTrinity-Large-Thinking—Not directly comparable
OmniDocBench 1.6
Ternary Bonsai 2 27B89.1%SourceTrinity-Large-Thinking—Not directly comparable
RealWorldQA
Ternary Bonsai 2 27B80.1%SourceTrinity-Large-Thinking—Not directly comparable
OCRBench V2
Ternary Bonsai 2 27B56.9%SourceTrinity-Large-Thinking—Not directly comparable
Instruction following
Questions
Which is better, Ternary Bonsai 2 27B or Trinity-Large-Thinking?
Ternary Bonsai 2 27B has the higher public score estimate, 50.78 versus 43.27, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Ternary Bonsai 2 27B or Trinity-Large-Thinking?
Ternary Bonsai 2 27B scores higher for coding on the public lane, 49.9 to 27.3. Ternary Bonsai 2 27B 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, Ternary Bonsai 2 27B or Trinity-Large-Thinking?
Ternary Bonsai 2 27B scores higher for agentic tasks on the public lane, 49.9 to 38. Ternary Bonsai 2 27B 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, Ternary Bonsai 2 27B 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, Ternary Bonsai 2 27B or Trinity-Large-Thinking?
Trinity-Large-Thinking has the larger documented context window: 512K, compared with 262K.
Related comparisons
Last updated September 18, 2026
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