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BenchLM
Data

Hy4 preview vs Ternary Bonsai 2 27B

Updated October 29, 2026. Rank says Hy4 preview is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Hy4 preview has the higher public point estimate, 61.46 versus 53.95. Their conditional score ranges overlap. These ranges do not establish rank confidence. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Tencent logo

Tencent

61.46/100

Estimated · Public rank #48

Conditional range 47.1–75.8

Model B
Prism ML logo

Prism ML

53.95/100

Estimated · Public rank #78

Conditional range 39.6–68.3

Shared results
4
Hy4 preview only
29
Ternary Bonsai 2 27B only
17
Like-for-like categories
0 / 8
Estimated: Hy4 preview and Ternary Bonsai 2 27B. Conditional ranges do not establish rank confidence.How the comparison works

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

    Hy4 preview

    Hy4 preview 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 not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited
  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Ternary Bonsai 2 27B is not ranked on the public lane for agentic, so no winner is named for agentic.

    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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

56.3Hy4 preview—Ternary Bonsai 2 27B

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.

Agentic

Not comparable
Hy4 preview
57.3
Estimated · #37/135
Ternary Bonsai 2 27B
Not ranked
Basis
BenchAlign v5.8 lane · 17 vs 3 public rows
Reading
Not comparable

Coding

Not comparable
Hy4 preview
56.3
Supported · #26/156
Ternary Bonsai 2 27B
Not ranked
Basis
BenchAlign v5.8 lane · 8 vs 4 public rows
Reading
Not comparable

Reasoning

Not comparable
Hy4 preview
Not ranked
Ternary Bonsai 2 27B
77.9
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Hy4 preview
86.6
Unranked · 1 rankable row
Ternary Bonsai 2 27B
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Hy4 preview
64.2
Estimated · #37/179
Ternary Bonsai 2 27B
Not ranked
Basis
BenchAlign v5.8 lane · 4 vs 3 public rows
Reading
Not comparable

Multilingual

Not comparable
Hy4 preview
Not ranked
Ternary Bonsai 2 27B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Hy4 preview
Not ranked
Ternary Bonsai 2 27B
74.0
#73/157
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
Hy4 preview
Not ranked
Ternary Bonsai 2 27B
86.2
Unranked · 4 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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

Hy4 preview
Self-hosted; infrastructure cost varies
Fits in one request
Ternary Bonsai 2 27B
Self-hosted; infrastructure cost varies
Fits in one request

Hy4 preview 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

Hy4 preview
Self-hosted; infrastructure cost varies
Fits in one request
Ternary Bonsai 2 27B
Self-hosted; infrastructure cost varies
Fits in one request

Hy4 preview 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

Hy4 preview
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

Hy4 preview has no comparable published API token rate. Ternary Bonsai 2 27B has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Documented inputs

Hy4 preview

Not sourced

Ternary Bonsai 2 27B

Not sourced

Documented outputs

Hy4 preview

Not sourced

Ternary Bonsai 2 27B

Not sourced

Provider availability

Hy4 preview

Not sourced

Ternary Bonsai 2 27B

Not sourced

Reasoning profile

Hy4 preview

Reasoning

Ternary Bonsai 2 27B

Reasoning

Weight access

Hy4 preview

Open Weight

Ternary Bonsai 2 27B

Open Weight

License

Hy4 preview

Open Weight

Ternary Bonsai 2 27B

Open Weight

Release date

Hy4 preview

2026-08-28

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
Hy4 preview has the higher public point estimate, 61.46 versus 53.95. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Hy4 preview has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Hy4 preview or Ternary Bonsai 2 27B?

Hy4 preview has the higher public point estimate, 61.46 versus 53.95. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Hy4 preview or Ternary Bonsai 2 27B?

Ternary Bonsai 2 27B is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Hy4 preview or Ternary Bonsai 2 27B?

Ternary Bonsai 2 27B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Hy4 preview 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, Hy4 preview or Ternary Bonsai 2 27B?

Hy4 preview has the larger documented context window: 1M, compared with 262K.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Hy4 preview
API / mo$0
Self-host / mo$27,962
Break-even—
Ternary Bonsai 2 27B
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence50 rows

Agentic

  • Terminal-Bench 2.1

    Hy4 preview85.4%
    Source
    Ternary Bonsai 2 27B52.8%
    Source

    Hy4 preview leads this result

  • CyberGym

    Hy4 preview78.4%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • WideResearch

    Hy4 preview83.9%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • DRACO

    Hy4 preview77.2%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • MCP Atlas

    Hy4 preview83.7%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • Toolathlon-Verified

    Hy4 preview74.1%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • APEX-Agents

    Hy4 preview37.1%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • skillsBench

    Hy4 preview62.9%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • JobBench

    Hy4 preview61.7%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • Agents' Last Exam

    Hy4 preview22.8%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • AutomationBench

    Hy4 preview32.1%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • BankerToolBench

    Hy4 preview78.6%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • HLE w/ tools

    Hy4 preview55.4%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Hy4 preview55.1%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • CWE-bench v1

    Hy4 preview53.0%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • Hermes Index

    Hy4 preview26.1%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • Hermes Bench

    Hy4 preview58.6%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • τ²-bench results

    Hy4 preview—
    Ternary Bonsai 2 27B80.2%
    Source

    Not directly comparable

  • BFCL v3

    Hy4 preview—
    Ternary Bonsai 2 27B74.9%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    Hy4 preview85.4%
    Source
    Ternary Bonsai 2 27B52.8%
    Source

    Hy4 preview leads this result

  • SWE-bench Pro

    Hy4 preview65.7%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • SWE Multilingual

    Hy4 preview82.9%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • DeepSWE

    Hy4 preview64.3%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • NL2Repo

    Hy4 preview58.9%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • ProgramBench

    Hy4 preview17.5%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • PostTrain Bench

    Hy4 preview35.6%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • sweMarathon

    Hy4 preview31.9%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • LiveCodeBench v6

    Hy4 preview—
    Ternary Bonsai 2 27B90.1%
    Source

    Not directly comparable

  • BigCodeBench

    Hy4 preview—
    Ternary Bonsai 2 27B58.1%
    Source

    Not directly comparable

  • SWE-bench Verified

    Hy4 preview—
    Ternary Bonsai 2 27B60.8%
    Source

    Not directly comparable

Reasoning

  • STEMBench

    Hy4 preview2.59
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • CritPt

    Hy4 preview16.9%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Hy4 preview66.2%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • CharXiv (overall)

    Hy4 preview—
    Ternary Bonsai 2 27B80.0%
    Source

    Not directly comparable

  • A-OKVQA

    Hy4 preview—
    Ternary Bonsai 2 27B86.8%
    Source

    Not directly comparable

  • OmniDocBench 1.6

    Hy4 preview—
    Ternary Bonsai 2 27B89.1%
    Source

    Not directly comparable

  • RealWorldQA

    Hy4 preview—
    Ternary Bonsai 2 27B80.1%
    Source

    Not directly comparable

  • OCRBench V2

    Hy4 preview—
    Ternary Bonsai 2 27B56.9%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Hy4 preview92.3%
    Source
    Ternary Bonsai 2 27B85.8%
    Source

    Hy4 preview leads this result

  • GPQA-D

    Hy4 preview92.3%
    Source
    Ternary Bonsai 2 27B85.8%
    Source

    Hy4 preview leads this result

  • HLE

    Hy4 preview55.4%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • HLE w/o tools

    Hy4 preview43.4%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • MMLU-Redux

    Hy4 preview—
    Ternary Bonsai 2 27B89.1%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Hy4 preview—
    Ternary Bonsai 2 27B91.3%
    Source

    Not directly comparable

  • IFBench

    Hy4 preview—
    Ternary Bonsai 2 27B74%
    Source

    Not directly comparable

Math

  • Apex

    Hy4 preview74.2%
    Source
    Ternary Bonsai 2 27B—

    Not directly comparable

  • GSM8K

    Hy4 preview—
    Ternary Bonsai 2 27B96.7%
    Source

    Not directly comparable

  • MATH-500

    Hy4 preview—
    Ternary Bonsai 2 27B98.8%
    Source

    Not directly comparable

  • AIME 2025

    Hy4 preview—
    Ternary Bonsai 2 27B95%
    Source

    Not directly comparable

  • AIME26

    Hy4 preview—
    Ternary Bonsai 2 27B95.8%
    Source

    Not directly comparable

50 public results · 4 shared

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Last updated October 29, 2026