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Data

Data as of October 29, 2026 · How the score is built

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 scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticWeight 22%3 benchmarksVerified
Score pending
CodingRank Not rankedWeight 20%4 benchmarksVerified
43.2
ReasoningWeight 17%0 benchmarksNot measured
Not measured
MultimodalWeight 12%5 benchmarksVerified
Score pending
KnowledgeRank Not rankedWeight 12%3 benchmarksVerified
75.0
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingRank #73 of 157Percentile 54thWeight 5%2 benchmarksVerified
74.0
MathRank Not rankedWeight 5%4 benchmarksVerified
86.2

21 of 782 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How 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.

  1. Agentic3/3 verified
  2. Coding4/4 verified
  3. ReasoningNot measured
  4. Multimodal5/5 verified
  5. Knowledge3/3 verified
  6. MultilingualNot measured
  7. Inst. Following2/2 verified
  8. Math4/4 verified
Verified sourceProvisionalNot measured

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

  1. AgenticNot ranked
  2. CodingNot ranked
  3. ReasoningNot ranked
  4. MultimodalNot ranked
  5. KnowledgeNot ranked
  6. MultilingualNot ranked
  7. Inst. Following#73/157
  8. MathNot ranked
Top decileTop quartileMid-fieldNot eligible
Peer-median outline not drawn: no peer percentiles in this snapshot

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
Coding benchmark values, best verified comparison, weight, and source status
Terminal-Bench 2.1Score52.8%Versus best verified row

Best verified: SWE-2 · 92.8%

Gap40 behindWeightScored in Agentic
LiveCodeBench v6Score90.1%Versus best verified row

Best verified: Sakana Fugu-Ultra · 93.2%

Gap3.1 behindWeightDisplay only
BigCodeBenchScore58.1%Versus best verified row

Best verified: Ternary Bonsai 2 27B · 58.1%

GapBest verifiedWeightDisplay only
SWE-bench VerifiedSoftware Engineering Benchmark VerifiedScore60.8%Versus best verified row

Best verified: Claude Opus 5 · 96%

Gap35.2 behindWeightDisplay only
Agentic3 rows
Agentic benchmark values, best verified comparison, weight, and source status
Terminal-Bench 2.1Score52.8%Versus best verified row

Best verified: SWE-2 · 92.8%

Gap40 behindWeight8% ref. weight
τ²-bench resultsτ²-Bench Tool-Agent-User EvaluationScore80.2%Versus best verified row

Best verified: GPT-5.4 · 98.9%

Gap18.7 behindWeightDisplay only
BFCL v3Berkeley Function Calling Leaderboard v3Score74.9%Versus best verified row

Best verified: Ternary Bonsai 2 27B · 74.9%

GapBest verifiedWeightDisplay only
Multimodal5 rows
Multimodal benchmark values, best verified comparison, weight, and source status
CharXiv (overall)CharXiv Descriptive and Reasoning CombinedScore80.0%Versus best verified row

Best verified: Ternary Bonsai 2 27B · 80.0%

GapBest verifiedWeightDisplay only
A-OKVQAA Benchmark for Visual Question Answering using World KnowledgeScore86.8%Versus best verified row

Best verified: Ternary Bonsai 2 27B · 86.8%

GapBest verifiedWeightDisplay only
OmniDocBench 1.6OmniDocBench v1.6Score89.1%Versus best verified row

Best verified: Ternary Bonsai 2 27B · 89.1%

GapBest verifiedWeightDisplay only
RealWorldQAScore80.1%Versus best verified row

Best verified: Qwen3.8-Flash-Next · 88.5%

Gap8.4 behindWeightDisplay only
OCRBench V2Score56.9%Versus best verified row

Best verified: Qwen3.8 Max · 74.2%

Gap17.3 behindWeightDisplay only
Knowledge3 rows
Knowledge benchmark values, best verified comparison, weight, and source status
GPQAGraduate-Level Google-Proof Q&AScore85.8%Versus best verified row

Best verified: GPT-6 Astra · 96%

Gap10.2 behindWeight3% ref. weight
MMLU-ReduxScore89.1%Versus best verified row

Best verified: Qwen3.7 Max · 95%

Gap5.9 behindWeightDisplay only
GPQA-DGPQA DiamondScore85.8%Versus best verified row

Best verified: GPT-6 Astra · 96.0%

Gap10.2 behindWeightDisplay only
Inst. Following2 rows
Inst. Following benchmark values, best verified comparison, weight, and source status
IFBenchInstruction Following BenchmarkScore74%Versus best verified row

Best verified: Flint · 89.2%

Gap15.2 behindWeightWeighted 70%
IFEvalInstruction-Following EvalScore91.3%Versus best verified row

Best verified: LFM2.5-24B-A2B · 96.8%

Gap5.4 behindWeightDisplay only
Math4 rows
Math benchmark values, best verified comparison, weight, and source status
AIME26AIME 2026Score95.8%Versus best verified row

Best verified: GLM-5.2 · 99.2%

Gap3.4 behindWeightWeighted 25%
GSM8KGrade School Math 8KScore96.7%Versus best verified row

Best verified: OrcaCyber-Nano-1.0 · 97.5%

Gap0.8 behindWeightDisplay only
MATH-500MATH-500 Problem SetScore98.8%Versus best verified row

Best verified: Ternary Bonsai 2 27B · 98.8%

GapBest verifiedWeightDisplay only
AIME 2025American Invitational Mathematics Examination 2025Score95%Versus best verified row

Best verified: GPT-5.2 · 100%

Gap5 behindWeightDisplay only

Bars run 0–100; the dark tick marks the best source-verified value

All 21 rows

Lineage

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.

  1. Jul 14, 2026

    Ternary Bonsai 27B

    Not publicly ranked · Price not listed

  2. Sep 17, 2026 · you are here

    Ternary Bonsai 2 27B

    Score 54.0 · Price not listed

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.

Inference hosts for Ternary Bonsai 2 27B with input, output and cached-input prices per million tokens.
HostInput /1MOutput /1MCached /1MContext
OpenRouter$0.075$0.50$0.037262K
Compare inference hosts

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 parameters

Questions

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.

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Compare Ternary Bonsai 2 27B with every tracked model1003 comparisons