Data as of October 29, 2026 · How the score is built
Released Sep 17, 2026262K contextPrismML Ternary Bonsai 2 27B model card
Ternary Bonsai 2 27B
Decision readingTernary Bonsai 2 27B scores 54 out of 100 and ranks #82 of 216. This profile shows 21 source-displayable benchmark rows; its strongest eligible category is Instruction Following at #73. Published weights can be self-hosted, but infrastructure cost varies and is not a comparable API token rate.
Released Sep 17, 2026 — see all recent releases
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Capability
54/100
field median 50.5#82 of 216 ranked models
Public
#82of 216
Verified —
Price
Self-hosted; infrastructure cost varies
input median $0.75No comparable first-party hosted token rate
Speed
Not measured
field median 92 tok/sTime to first token not measured
Context
262Ktokens
field median 256,000Maximum output length is tracked separately
Strongest published evidence
Instruction Following ranks #73. A well-rounded choice across a range of tasks.
Validate before choosing
21 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
Source-linked · 21 displayable benchmark rows
Category score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticWeight 22%3 benchmarksVerified | Score pending | 3 benchmarks | Verified | |||
| CodingRank Not rankedWeight 20%4 benchmarksVerified | 43.2 | 4 benchmarks | Verified | |||
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultimodalWeight 12%5 benchmarksVerified | Score pending | 5 benchmarks | Verified | |||
| KnowledgeRank Not rankedWeight 12%3 benchmarksVerified | 75.0 | 3 benchmarks | Verified | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingRank #73 of 157Percentile 54thWeight 5%2 benchmarksVerified | 74.0 | 2 benchmarks | Verified | |||
| MathRank Not rankedWeight 5%4 benchmarksVerified | 86.2 | 4 benchmarks | Verified |
21 of 782 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- Agentic3/3 verified
- Coding4/4 verified
- ReasoningNot measured
- Multimodal5/5 verified
- Knowledge3/3 verified
- MultilingualNot measured
- Inst. Following2/2 verified
- Math4/4 verified
Capability shape
Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.
Ternary Bonsai 2 27B category percentile values
- AgenticNot eligible
- CodingNot eligible
- ReasoningNot eligible
- MultimodalNot eligible
- KnowledgeNot eligible
- MultilingualNot eligible
- Instruction following54th percentile
- MathNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- AgenticNot ranked
- CodingNot ranked
- ReasoningNot ranked
- MultimodalNot ranked
- KnowledgeNot ranked
- MultilingualNot ranked
- Inst. Following#73/157
- MathNot ranked
Benchmark ledger
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Coding4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Terminal-Bench 2.1 | Score52.8% | Versus best verified row Best verified: SWE-2 · 92.8% | Gap40 behind | WeightScored in Agentic | Provider exact |
| LiveCodeBench v6 | Score90.1% | Versus best verified row Best verified: Sakana Fugu-Ultra · 93.2% | Gap3.1 behind | WeightDisplay only | Provider exact |
| BigCodeBench | Score58.1% | Versus best verified row Best verified: Ternary Bonsai 2 27B · 58.1% | GapBest verified | WeightDisplay only | Provider exact |
| SWE-bench VerifiedSoftware Engineering Benchmark Verified | Score60.8% | Versus best verified row Best verified: Claude Opus 5 · 96% | Gap35.2 behind | WeightDisplay only | Provider exact |
Agentic3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Terminal-Bench 2.1 | Score52.8% | Versus best verified row Best verified: SWE-2 · 92.8% | Gap40 behind | Weight8% ref. weight | Provider exact |
| τ²-bench resultsτ²-Bench Tool-Agent-User Evaluation | Score80.2% | Versus best verified row Best verified: GPT-5.4 · 98.9% | Gap18.7 behind | WeightDisplay only | Provider exact |
| BFCL v3Berkeley Function Calling Leaderboard v3 | Score74.9% | Versus best verified row Best verified: Ternary Bonsai 2 27B · 74.9% | GapBest verified | WeightDisplay only | Provider exact |
Multimodal5 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| CharXiv (overall)CharXiv Descriptive and Reasoning Combined | Score80.0% | Versus best verified row Best verified: Ternary Bonsai 2 27B · 80.0% | GapBest verified | WeightDisplay only | Provider exact |
| A-OKVQAA Benchmark for Visual Question Answering using World Knowledge | Score86.8% | Versus best verified row Best verified: Ternary Bonsai 2 27B · 86.8% | GapBest verified | WeightDisplay only | Provider exact |
| OmniDocBench 1.6OmniDocBench v1.6 | Score89.1% | Versus best verified row Best verified: Ternary Bonsai 2 27B · 89.1% | GapBest verified | WeightDisplay only | Provider exact |
| RealWorldQA | Score80.1% | Versus best verified row Best verified: Qwen3.8-Flash-Next · 88.5% | Gap8.4 behind | WeightDisplay only | Provider exact |
| OCRBench V2 | Score56.9% | Versus best verified row Best verified: Qwen3.8 Max · 74.2% | Gap17.3 behind | WeightDisplay only | Provider exact |
Knowledge3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| GPQAGraduate-Level Google-Proof Q&A | Score85.8% | Versus best verified row Best verified: GPT-6 Astra · 96% | Gap10.2 behind | Weight3% ref. weight | Provider exact |
| MMLU-Redux | Score89.1% | Versus best verified row Best verified: Qwen3.7 Max · 95% | Gap5.9 behind | WeightDisplay only | Provider exact |
| GPQA-DGPQA Diamond | Score85.8% | Versus best verified row Best verified: GPT-6 Astra · 96.0% | Gap10.2 behind | WeightDisplay only | Provider exact |
Inst. Following2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| IFBenchInstruction Following Benchmark | Score74% | Versus best verified row Best verified: Flint · 89.2% | Gap15.2 behind | WeightWeighted 70% | Provider exact |
| IFEvalInstruction-Following Eval | Score91.3% | Versus best verified row Best verified: LFM2.5-24B-A2B · 96.8% | Gap5.4 behind | WeightDisplay only | Provider exact |
Math4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| AIME26AIME 2026 | Score95.8% | Versus best verified row Best verified: GLM-5.2 · 99.2% | Gap3.4 behind | WeightWeighted 25% | Provider exact |
| GSM8KGrade School Math 8K | Score96.7% | Versus best verified row Best verified: OrcaCyber-Nano-1.0 · 97.5% | Gap0.8 behind | WeightDisplay only | Provider exact |
| MATH-500MATH-500 Problem Set | Score98.8% | Versus best verified row Best verified: Ternary Bonsai 2 27B · 98.8% | GapBest verified | WeightDisplay only | Provider exact |
| AIME 2025American Invitational Mathematics Examination 2025 | Score95% | Versus best verified row Best verified: GPT-5.2 · 100% | Gap5 behind | WeightDisplay only | Provider exact |
Bars run 0–100; the dark tick marks the best source-verified value
All 21 rowsLineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
27b
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- prism-ml/Ternary-Bonsai-2-27B-ggufPrismML Bonsai 2 collection
- Context window
- 262KPrismML Bonsai 2 collection
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- Prism ML publishes Ternary Bonsai 2 27B under Apache 2.0 as a self-host checkpoint. The GGUF release ships two packings of the language model, PTQ1_0 at 1.76 bits per weight (5.93 GB) and PQ2_0 at 2.16 bits per weight (7.25 GB), plus an optional mmproj vision tower; an MLX 2-bit package is published for Apple silicon. Backends are llama.cpp/CUDA and MLX, with the full 262K context.
- Cloud regions
- Not tracked yet
- Lifecycle
- Current
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing recordPrismML Bonsai 2 collection
- Self-host
- Open weights available; hardware estimate not sourced
- Rate limits
- Not tracked yet
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
Ternary Bonsai 2 27B ranks #82 of 216 on the public leaderboard with a score of 53.95/100. It does not yet have enough sourced coverage for a verified position.
Ternary Bonsai 2 27B is a open weight model with a 262K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
Prism ML publishes Ternary Bonsai 2 27B under Apache 2.0 as a self-host checkpoint. The GGUF release ships two packings of the language model, PTQ1_0 at 1.76 bits per weight (5.93 GB) and PQ2_0 at 2.16 bits per weight (7.25 GB), plus an optional mmproj vision tower; an MLX 2-bit package is published for Apple silicon. Backends are llama.cpp/CUDA and MLX, with the full 262K context.
Its explicit predecessor is Ternary Bonsai 27B. 21 of 782 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Instruction Following at #73. a well-rounded choice across a range of tasks.
Last updated October 29, 2026. Runtime fields remain blank until a sourced snapshot exists.
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Where to run Ternary Bonsai 2 27B by API
OpenRouter is the one host we track that publishes a per-token price for it. Prices read from each host’s own pages on October 20, 2026.
| Host | Input /1M | Output /1M | Cached /1M | Context |
|---|---|---|---|---|
| OpenRouter | $0.075 | $0.50 | $0.037 | 262K |
Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.
Estimate VRAM from known parametersQuestions
How does Ternary Bonsai 2 27B perform overall in AI benchmarks?
Ternary Bonsai 2 27B ranks #82 out of 216 models on the public BenchAlign leaderboard, with a score of 53.95/100. Its evidence status is Estimated, and this profile shows 21 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is Ternary Bonsai 2 27B good for knowledge and understanding?
Ternary Bonsai 2 27B has source-displayable benchmark coverage for knowledge and understanding, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Ternary Bonsai 2 27B good for coding and programming?
Ternary Bonsai 2 27B has source-displayable benchmark coverage for coding and programming, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Ternary Bonsai 2 27B good for mathematics?
Ternary Bonsai 2 27B has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Ternary Bonsai 2 27B good for agentic tool use and computer tasks?
Ternary Bonsai 2 27B has source-displayable benchmark coverage for agentic tool use and computer tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Ternary Bonsai 2 27B good for multimodal and grounded tasks?
Ternary Bonsai 2 27B has source-displayable benchmark coverage for multimodal and grounded tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Ternary Bonsai 2 27B good for instruction following?
Ternary Bonsai 2 27B ranks #73 out of 157 eligible models for instruction following, with a public category score of 74/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Ternary Bonsai 2 27B open source?
Ternary Bonsai 2 27B is an open-weight model from Prism ML. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Does Ternary Bonsai 2 27B have full benchmark coverage on BenchLM?
No. Ternary Bonsai 2 27B currently has 22 source-displayable rows. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of Ternary Bonsai 2 27B?
Ternary Bonsai 2 27B has a documented context window of 262K. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.