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Model Comparison

Granite 4.2 8B vs Ternary Bonsai 2 27BWhich Is Better in 2026?

IBM Granite•
Prism ml

Granite 4.2 8B vs Ternary Bonsai 2 27B: which should you choose in 2026?

Granite 4.2 8B (by IBM Granite) and Ternary Bonsai 2 27B (by Prism ml) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.

There are not enough shared, protocol-compatible benchmark results to declare a performance leader.

Granite 4.2 8B is about 1.7× cheaper on a blended 3:1 input/output basis ($0.1075 vs $0.18125 per 1M tokens).

Ternary Bonsai 2 27B has the larger context window (262,144 tokens vs 131,072 tokens).

Choose Granite 4.2 8B if…

  • • lower blended API cost matters for your workload

Choose Ternary Bonsai 2 27B if…

  • • you work with longer documents, transcripts, or codebases
  • • you need image inputs

The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.

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Performance benchmarks

Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.

Granite 4.2 8B and Ternary Bonsai 2 27B benchmark results
BenchmarkGranite 4.2 8BTernary Bonsai 2 27B
AutomationBench-AA

A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework.

Guardrail-aware score

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BrowseComp

A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing.

Accuracy

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OSWorld-Verified

A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state.

Mean task reward

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SWE-Bench Verified

A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests.

Resolved

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Terminal-Bench 2.1

Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol.

Mean task success

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AutoBench

A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench.

Weighted peer-assessment score

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GPQA Diamond

The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry.

Accuracy

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Humanity's Last Exam

A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged.

Accuracy

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Arena preference scores
Arena (Text)

Human preference score

1287 ±11
—

Benchmark sources

Reviewed evidence last updated 2026-08-09. Arena data last refreshed Sep 14, 2026.

Pricing, capabilities, and model facts

Granite 4.2 8B and Ternary Bonsai 2 27B model facts
FeatureGranite 4.2 8BTernary Bonsai 2 27B
Context & model facts
DeveloperIBM GranitePrism ml
API providerIBMPrismML
Input context131,072 tokens262,144 tokens
Maximum output117,964 tokens32,768 tokens
Released——
Added to WritingmateAug 31, 2026Sep 18, 2026
LicenseNot availableNot available
Knowledge cutoff——
Capabilities
InputsTextText, Image
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesYes
VisionNoYes
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$0.06$0.075
Output (per 1M tokens)$0.25$0.50
Blended 3:1 input/output$0.1075$0.18125
API performance
p95 latencyNot measuredNot measured
Output throughputNot measuredNot measured
Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table.

Data sources

  • Writingmate model catalog (pricing, limits, and availability)

Catalog data last updated Sep 18, 2026.

Why Pay for Multiple Subscriptions?

Comparing Granite 4.2 8B from IBM Granite with Ternary Bonsai 2 27B from Prism ml? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Granite 4.2 8B and Ternary Bonsai 2 27B
PlanPriceGranite 4.2 8BTernary Bonsai 2 27BAI ImagesAI Video
Writingmate Pro
Most popular
$20/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1
Writingmate Ultimate
Power users
$60/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1
View Plans & Pricing

Granite 4.2 8B vs Ternary Bonsai 2 27B FAQ

Which is better, Granite 4.2 8B or Ternary Bonsai 2 27B?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Granite 4.2 8B is about 1.7× cheaper on a blended 3:1 input/output basis ($0.1075 vs $0.18125 per 1M tokens). Ternary Bonsai 2 27B has the larger context window (262,144 tokens vs 131,072 tokens).

Which model is cheaper to use through an API?

Granite 4.2 8B is about 1.7× cheaper on a blended 3:1 input/output basis ($0.1075 vs $0.18125 per 1M tokens).

Which model supports more context?

Ternary Bonsai 2 27B has the larger context window (262,144 tokens vs 131,072 tokens).

Can I switch between Granite 4.2 8B and Ternary Bonsai 2 27B?

Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.