GPT-4.1 vs Ternary Bonsai 27B

At a Glance

Compare
GPT-4.1OpenAI
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5#44 of 463.7 score · 2/3 sources · provisional · missing LiveBench · full-core range 2.5–35.8UnrankedNot in the 46-model eligible cohort
Pricing and Limits
Input priceFrom · USD / 1M tokens$1.00Openrouter · Sep 3, 2026Not reported
Output priceFrom · USD / 1M tokens$4.00Openrouter · Sep 3, 2026Not reported
Context windowMaximum documented tokens1,048K262K
Model facts checkedAug 29, 2026View model evidence →Sep 18, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldGPT-4.1Ternary Bonsai 27B
DeveloperOpenAIPrismML
FamilyGpt 4 1Bonsai 27b
ModelGPT-4.1Ternary Bonsai 27B
VersionGPT-4.1Ternary Bonsai 27B
Lifecycleactiveactive
ReleasedUnknown2026-07-04
Knowledge cutoff2024-06-01Unknown
Input modalitiesText, ImageText, Image
Output modalitiesTextText
Context window1,048K262K
Total parametersUnknown27B
Active parametersUnknownUnknown
LicenseUnknownapache-2.0
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessOpenai (Standard), Openrouter (Standard)Together Ai (Standard)
Capabilitieschat, generation, toolschat, generation, reasoning, tools, vision
Base modelUnknownQwen3.6 27B
Effective bit widthUnknown1.58 bits per weight
Language model sizeUnknown6.66 GiB
Weight formatUnknownTernary Q2_0

GPT-4.1 Capabilities

chatgenerationtools
Serving providers2
Canonical IDopenai/gpt-4.1

Ternary Bonsai 27B Capabilities

chatgenerationreasoningtoolsvision
Serving providers1
Canonical IDprism-ml/Ternary-Bonsai-27B

Primary Evidence

Sources and Freshness

Questions

GPT-4.1 vs Ternary Bonsai 27B FAQs

Is GPT-4.1 or Ternary Bonsai 27B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both GPT-4.1 and Ternary Bonsai 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, GPT-4.1 or Ternary Bonsai 27B?+

Only GPT-4.1 has a directly sourced input price: $1.00 per million tokens. Only GPT-4.1 has a directly sourced output price: $4.00 per million tokens.

Which has a larger context window, GPT-4.1 or Ternary Bonsai 27B?+

GPT-4.1 has the larger sourced context window. GPT-4.1 supports 1,048K and Ternary Bonsai 27B supports 262K.

Which performs better in benchmarks, GPT-4.1 or Ternary Bonsai 27B?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can GPT-4.1 or Ternary Bonsai 27B be self-hosted?+

Ternary Bonsai 27B is the only model in this pair currently marked as self-hostable. GPT-4.1 is not marked open weight; Ternary Bonsai 27B is open weight.

Can GPT-4.1 and Ternary Bonsai 27B understand images?+

GPT-4.1 is documented with image input; Ternary Bonsai 27B is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, GPT-4.1 or Ternary Bonsai 27B?+

Neither has a larger sourced maximum output. GPT-4.1 is 33K and Ternary Bonsai 27B is —.

Do GPT-4.1 and Ternary Bonsai 27B support reasoning and tool use?+

GPT-4.1: tool calling and image input. Ternary Bonsai 27B: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, GPT-4.1 or Ternary Bonsai 27B?+

GPT-4.1 has 2 sourced provider routes; Ternary Bonsai 27B has 1, so GPT-4.1 has broader tracked availability.

Which offers better value, GPT-4.1 or Ternary Bonsai 27B?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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