Agentic
Directional only- GPT-5.6 Terra
- 60.4
- Supported · #18/154
- Ternary Bonsai 2 27B
- 49.9
- Estimated · #59/154
- Basis
- BenchAlign lane · 9 vs 3 public rows
- Reading
- Directional only
GPT-5.6 Terra vs Ternary Bonsai 2 27B
Decision reading
GPT-5.6 Terra has the higher public score, 71.13 versus 50.78, and the 90% score intervals do not overlap.
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. Rank says GPT-5.6 Terra 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
GPT-5.6 Terra
GPT-5.6 Terra 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 is 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: 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.
31 GPT-5.6 Terra only
2 Shared
19 Ternary Bonsai 2 27B only
- Shared results
- 2
- GPT-5.6 Terra only
- 31
- Ternary Bonsai 2 27B only
- 19
- 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- GPT-5.6 Terra
- 67.0
- Supported · #8/154
- Ternary Bonsai 2 27B
- 49.9
- Estimated · #64/154
- Basis
- BenchAlign lane · 9 vs 4 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.6 Terra
- 69.3
- Supported · #15/184
- Ternary Bonsai 2 27B
- 50.6
- Estimated · #78/184
- Basis
- BenchAlign lane · 8 vs 3 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.6 Terra
- 85.7
- #35/124
- Ternary Bonsai 2 27B
- 71.0
- #64/124
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.6 Terra
- 63.7
- #14/20
- Ternary Bonsai 2 27B
- 73.9
- Unranked · 1 rankable row
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Terra
- 96.8
- Unranked · 3 rankable rows
- Ternary Bonsai 2 27B
- 76.8
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Terra
- Not ranked
- Ternary Bonsai 2 27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.6 Terra
- 77.2
- #15/48
- Ternary Bonsai 2 27B
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.6 Terra | Ternary Bonsai 2 27B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 60.4Supported · #18/154 | 49.9Estimated · #59/154 | Directional onlyBenchAlign lane · 9 vs 3 public rows | Directional only |
| Coding | 67.0Supported · #8/154 | 49.9Estimated · #64/154 | Directional onlyBenchAlign lane · 9 vs 4 public rows | Directional only |
| Knowledge | 69.3Supported · #15/184 | 50.6Estimated · #78/184 | Directional onlyBenchAlign lane · 8 vs 3 public rows | Directional only |
| Instruction following | 85.7#35/124 | 71.0#64/124 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Reasoning | 63.7#14/20 | 73.9Unranked · 1 rankable row | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Math | 96.8Unranked · 3 rankable rows | 76.8Unranked · 4 rankable rows | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 77.2#15/48 | Not ranked | Not comparableProvisional lane · 1 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.
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.
GPQA
Knowledge
GPT-5.6 Terra: 92.9%Ternary Bonsai 2 27B: 85.8%Normalized gap 7.1GPT-5.6 Terra source Ternary Bonsai 2 27B source
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
- GPT-5.6 Terra
- $0.008
- Fits in one request
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- Fits in one request
Ternary Bonsai 2 27B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5.6 Terra
- $0.136
- Fits in one request
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- 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
- GPT-5.6 Terra
- $0.2
- Fits in one request
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
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.
SpecificationGPT-5.6 TerraTernary Bonsai 2 27B
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Terra
1.05M
OpenAI model catalogTernary Bonsai 2 27B
API model ID
GPT-5.6 Terra
gpt-5.6-terra
OpenAI model catalogTernary 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.
GPT-5.6 Terra
$0.2 per 1M cached input tokens
OpenAI pricingTernary Bonsai 2 27B
No comparable hosted API rate
PrismML Bonsai 2 collectionDocumented inputs
GPT-5.6 Terra
text, image
OpenAI model catalogTernary Bonsai 2 27B
Not sourced
Documented outputs
GPT-5.6 Terra
Ternary Bonsai 2 27B
Not sourced
Provider availability
GPT-5.6 Terra
Generally Available · OpenAI Responses API
OpenAI model catalogTernary Bonsai 2 27B
Not sourced
Reasoning profile
GPT-5.6 Terra
Reasoning
Ternary Bonsai 2 27B
Reasoning
Weight access
GPT-5.6 Terra
Proprietary
Ternary Bonsai 2 27B
Open Weight
License
GPT-5.6 Terra
Proprietary
Ternary Bonsai 2 27B
Open Weight
Release date
GPT-5.6 Terra
2026-07-09
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
- GPT-5.6 Terra has the higher public score, 71.13 versus 50.78, and the 90% score intervals do not overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.6 Terra has the larger documented window (1.05M).
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 evidence52 rows
Agentic
Terminal-Bench 3.0
GPT-5.6 Terra20.8%SourceTernary Bonsai 2 27B—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Terra87.4%SourceTernary Bonsai 2 27B—Not directly comparable
BrowseComp
GPT-5.6 Terra87.5%SourceTernary Bonsai 2 27B—Not directly comparable
OSWorld 2.0
GPT-5.6 Terra50.2%SourceTernary Bonsai 2 27B—Not directly comparable
CyberGym
GPT-5.6 Terra81.8%SourceTernary Bonsai 2 27B—Not directly comparable
ExploitGym
GPT-5.6 Terra23.2%SourceTernary Bonsai 2 27B—Not directly comparable
Toolathlon
GPT-5.6 Terra53.1%SourceTernary Bonsai 2 27B—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Terra77.5%SourceTernary Bonsai 2 27B—Not directly comparable
ApprenticeBench
GPT-5.6 Terra16%SourceTernary Bonsai 2 27B—Not directly comparable
τ²-bench results
GPT-5.6 Terra—Ternary Bonsai 2 27B80.2%SourceNot directly comparable
BFCL v3
GPT-5.6 Terra—Ternary Bonsai 2 27B74.9%SourceNot directly comparable
Terminal-Bench 2.1
GPT-5.6 Terra—Ternary Bonsai 2 27B52.8%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.6 Terra63.4%SourceTernary Bonsai 2 27B—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Terra87.4%SourceTernary Bonsai 2 27B—Not directly comparable
DeepSWE
GPT-5.6 Terra69.6%SourceTernary Bonsai 2 27B—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Terra55.8%SourceTernary Bonsai 2 27B—Not directly comparable
cursorBench32
GPT-5.6 Terra64.9%SourceTernary Bonsai 2 27B—Not directly comparable
VulcanBench v3
GPT-5.6 Terra87.0%SourceTernary Bonsai 2 27B—Not directly comparable
LiveCodeBench (Vals)
GPT-5.6 Terra85.9%SourceTernary Bonsai 2 27B—Not directly comparable
SWE-bench (Vals)
GPT-5.6 Terra95.4%SourceTernary Bonsai 2 27B—Not directly comparable
cursorBench40
GPT-5.6 Terra41.3%SourceTernary Bonsai 2 27B—Not directly comparable
LiveCodeBench v6
GPT-5.6 Terra—Ternary Bonsai 2 27B90.1%SourceNot directly comparable
BigCodeBench
GPT-5.6 Terra—Ternary Bonsai 2 27B58.1%SourceNot directly comparable
SWE-bench Verified
GPT-5.6 Terra—Ternary Bonsai 2 27B60.8%SourceNot directly comparable
Terminal-Bench 2.1
GPT-5.6 Terra—Ternary Bonsai 2 27B52.8%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GPT-5.6 Terra92.9%SourceTernary Bonsai 2 27B85.8%SourceGPT-5.6 Terra leads this result
GPQA-D
GPT-5.6 Terra92.9%SourceTernary Bonsai 2 27B85.8%SourceGPT-5.6 Terra leads this result
HLE-Verified
GPT-5.6 Terra51.1%SourceTernary Bonsai 2 27B—Not directly comparable
LABBench2
GPT-5.6 Terra81.2%SourceTernary Bonsai 2 27B—Not directly comparable
HealthBench Professional
GPT-5.6 Terra57.7%SourceTernary Bonsai 2 27B—Not directly comparable
HealthBench Hard
GPT-5.6 Terra32.7%SourceTernary Bonsai 2 27B—Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Terra90.9%SourceTernary Bonsai 2 27B—Not directly comparable
MMLU-Pro (Vals)
GPT-5.6 Terra86.7%SourceTernary Bonsai 2 27B—Not directly comparable
MMLU-Redux
GPT-5.6 Terra—Ternary Bonsai 2 27B89.1%SourceNot directly comparable
Math
FrontierMath (legacy)
GPT-5.6 Terra84.9%SourceTernary Bonsai 2 27B—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Terra84.900%SourceTernary Bonsai 2 27B—Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Terra68.300%SourceTernary Bonsai 2 27B—Not directly comparable
GSM8K
GPT-5.6 Terra—Ternary Bonsai 2 27B96.7%SourceNot directly comparable
MATH-500
GPT-5.6 Terra—Ternary Bonsai 2 27B98.8%SourceNot directly comparable
AIME 2025
GPT-5.6 Terra—Ternary Bonsai 2 27B95%SourceNot directly comparable
AIME26
GPT-5.6 Terra—Ternary Bonsai 2 27B95.8%SourceNot directly comparable
Multimodal
MMMU-Pro
GPT-5.6 Terra80.7%SourceTernary Bonsai 2 27B—Not directly comparable
MMMU-Pro w/ Python
GPT-5.6 Terra82%SourceTernary Bonsai 2 27B—Not directly comparable
CharXiv (overall)
GPT-5.6 Terra—Ternary Bonsai 2 27B80.0%SourceNot directly comparable
A-OKVQA
GPT-5.6 Terra—Ternary Bonsai 2 27B86.8%SourceNot directly comparable
OmniDocBench 1.6
GPT-5.6 Terra—Ternary Bonsai 2 27B89.1%SourceNot directly comparable
RealWorldQA
GPT-5.6 Terra—Ternary Bonsai 2 27B80.1%SourceNot directly comparable
OCRBench V2
GPT-5.6 Terra—Ternary Bonsai 2 27B56.9%SourceNot directly comparable
Instruction following
Questions
Which is better, GPT-5.6 Terra or Ternary Bonsai 2 27B?
GPT-5.6 Terra has the higher public score, 71.13 versus 50.78, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, GPT-5.6 Terra or Ternary Bonsai 2 27B?
GPT-5.6 Terra scores higher for coding on the public lane, 67 to 49.9. 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, GPT-5.6 Terra or Ternary Bonsai 2 27B?
GPT-5.6 Terra scores higher for agentic tasks on the public lane, 60.4 to 49.9. Ternary Bonsai 2 27B is 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, GPT-5.6 Terra 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, GPT-5.6 Terra or Ternary Bonsai 2 27B?
GPT-5.6 Terra has the larger documented context window: 1.05M, compared with 262K.
Related comparisons
Last updated September 18, 2026
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