Agentic
Directional only- BTL-4
- 50.4
- Estimated · #51/154
- Ternary Bonsai 2 27B
- 49.9
- Estimated · #59/154
- Basis
- BenchAlign lane · 1 vs 3 public rows
- Reading
- Directional only
BTL-4 vs Ternary Bonsai 2 27B
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.
2 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. We do not rank this pair: at least one has no public score. 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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
BTL-4 and Ternary Bonsai 2 27B are 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
BTL-4 and Ternary Bonsai 2 27B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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: listed-rates
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.
1 BTL-4 only
2 Shared
19 Ternary Bonsai 2 27B only
- Shared results
- 2
- BTL-4 only
- 1
- Ternary Bonsai 2 27B only
- 19
- Like-for-like categories
- 0 / 8
2 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- BTL-4
- 50.3
- Estimated · #61/154
- Ternary Bonsai 2 27B
- 49.9
- Estimated · #64/154
- Basis
- BenchAlign lane · 2 vs 4 public rows
- Reading
- Directional only
Reasoning
Not comparable- BTL-4
- Not ranked
- Ternary Bonsai 2 27B
- 73.9
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- BTL-4
- Not ranked
- Ternary Bonsai 2 27B
- 50.6
- Estimated · #78/184
- Basis
- BenchAlign lane · 0 vs 3 public rows
- Reading
- Not comparable
Math
Not comparable- BTL-4
- Not ranked
- Ternary Bonsai 2 27B
- 76.8
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- BTL-4
- Not ranked
- Ternary Bonsai 2 27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- BTL-4
- Not ranked
- Ternary Bonsai 2 27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- BTL-4
- Not ranked
- Ternary Bonsai 2 27B
- 71.0
- #64/124
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | BTL-4 | Ternary Bonsai 2 27B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 50.4Estimated · #51/154 | 49.9Estimated · #59/154 | Directional onlyBenchAlign lane · 1 vs 3 public rows | Directional only |
| Coding | 50.3Estimated · #61/154 | 49.9Estimated · #64/154 | Directional onlyBenchAlign lane · 2 vs 4 public rows | Directional only |
| Reasoning | Not ranked | 73.9Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 50.6Estimated · #78/184 | Not comparableBenchAlign lane · 0 vs 3 public rows | Not comparable |
| Math | Not ranked | 76.8Unranked · 4 rankable rows | Not comparableProvisional lane · 0 vs 1 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 |
| Instruction following | Not ranked | 71.0#64/124 | Not comparableProvisional lane · 0 vs 1 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.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
SWE-bench Verified
Coding
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
- BTL-4
- Self-hosted; infrastructure cost varies
- Fits in one request
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- Fits in one request
BTL-4 has no comparable published API token rate. Ternary Bonsai 2 27B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- BTL-4
- Self-hosted; infrastructure cost varies
- Fits in one request
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- Fits in one request
BTL-4 has no comparable published API token rate. Ternary Bonsai 2 27B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- BTL-4
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
BTL-4 has no comparable published API token 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.
SpecificationBTL-4Ternary Bonsai 2 27B
Context window
Maximum documented context; output-token limits may be lower.
BTL-4
Ternary Bonsai 2 27B
API model ID
BTL-4
Not sourced
Ternary Bonsai 2 27B
prism-ml/Ternary-Bonsai-2-27B-gguf
PrismML Bonsai 2 collectionCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
BTL-4
No comparable hosted API rate
Bad Theory Labs BTL-4 model cardTernary Bonsai 2 27B
No comparable hosted API rate
PrismML Bonsai 2 collectionDocumented inputs
BTL-4
Not sourced
Ternary Bonsai 2 27B
Not sourced
Documented outputs
BTL-4
Not sourced
Ternary Bonsai 2 27B
Not sourced
Provider availability
BTL-4
Not sourced
Ternary Bonsai 2 27B
Not sourced
Reasoning profile
BTL-4
Reasoning
Ternary Bonsai 2 27B
Reasoning
Weight access
BTL-4
Open Weight
Ternary Bonsai 2 27B
Open Weight
License
BTL-4
Open Weight
Ternary Bonsai 2 27B
Open Weight
Release date
BTL-4
2026-08-05
Ternary Bonsai 2 27B
2026-09-17
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
- 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
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 262K.
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 evidence22 rows
Agentic
Coding
SWE-bench Verified
BTL-478.4%SourceTernary Bonsai 2 27B60.8%SourceBTL-4 leads this result
LiveCodeBench v6
BTL-466.1%SourceTernary Bonsai 2 27B90.1%SourceTernary Bonsai 2 27B leads this result
BigCodeBench
BTL-4—Ternary Bonsai 2 27B58.1%SourceNot directly comparable
Terminal-Bench 2.1
BTL-4—Ternary Bonsai 2 27B52.8%SourceNot directly comparable
Knowledge
Math
Multimodal
CharXiv (overall)
BTL-4—Ternary Bonsai 2 27B80.0%SourceNot directly comparable
A-OKVQA
BTL-4—Ternary Bonsai 2 27B86.8%SourceNot directly comparable
OmniDocBench 1.6
BTL-4—Ternary Bonsai 2 27B89.1%SourceNot directly comparable
RealWorldQA
BTL-4—Ternary Bonsai 2 27B80.1%SourceNot directly comparable
OCRBench V2
BTL-4—Ternary Bonsai 2 27B56.9%SourceNot directly comparable
Instruction following
Questions
Which is better, BTL-4 or Ternary Bonsai 2 27B?
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, BTL-4 or Ternary Bonsai 2 27B?
BTL-4 scores higher for coding on the public lane, 50.3 to 49.9. BTL-4 and Ternary Bonsai 2 27B are 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, BTL-4 or Ternary Bonsai 2 27B?
BTL-4 scores higher for agentic tasks on the public lane, 50.4 to 49.9. BTL-4 and Ternary Bonsai 2 27B 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, BTL-4 or Ternary Bonsai 2 27B?
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, BTL-4 or Ternary Bonsai 2 27B?
Both models list the same context window, 262K.
Related comparisons
Last updated September 18, 2026
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