NVIDIA Nemotron 3 Nano 30B A3B Base BF16 vs Bonsai 8B

At a Glance

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Bonsai 8BPrismML
Pricing and Limits
Context windowMaximum documented tokens262K66K
Model facts checkedAug 28, 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

FieldNVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16Bonsai 8B
DeveloperNVIDIAPrismML
FamilyNvidia Nemotron 3 Nano 30b A3b Base Bf16Bonsai 8b
ModelNVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16Bonsai 8B
VersionNVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16Bonsai 8B
Lifecycleactiveactive
Released2025-12-152026-03-18
Knowledge cutoff2025-06-25Unknown
Input modalitiesTextText
Output modalitiesTextText
Context window262K66K
Total parameters31.6B8.2B
Active parametersUnknownUnknown
Licenseotherapache-2.0
Open weightsYesYes
API availableUnknownNo
Self-hostableYesYes
Provider accessUnknownUnknown
Capabilitiesgenerationchat, generation
Effective bit widthUnknown1 bit per weight
Weight sizeUnknown1.16 GB
Weight formatUnknownBinary Q1_0

NVIDIA Nemotron 3 Nano 30B A3B Base BF16 Capabilities

generation
Serving providers0
Canonical IDnvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16

Bonsai 8B Capabilities

chatgeneration
Serving providers0
Canonical IDprism-ml/Bonsai-8B

Primary Evidence

Sources and Freshness

Questions

NVIDIA Nemotron 3 Nano 30B A3B Base BF16 vs Bonsai 8B FAQs

Is NVIDIA Nemotron 3 Nano 30B A3B Base BF16 or Bonsai 8B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both NVIDIA Nemotron 3 Nano 30B A3B Base BF16 and Bonsai 8B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, NVIDIA Nemotron 3 Nano 30B A3B Base BF16 or Bonsai 8B?+

Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.

Which has a larger context window, NVIDIA Nemotron 3 Nano 30B A3B Base BF16 or Bonsai 8B?+

NVIDIA Nemotron 3 Nano 30B A3B Base BF16 has the larger sourced context window. NVIDIA Nemotron 3 Nano 30B A3B Base BF16 supports 262K and Bonsai 8B supports 66K.

Which performs better in benchmarks, NVIDIA Nemotron 3 Nano 30B A3B Base BF16 or Bonsai 8B?+

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

Can NVIDIA Nemotron 3 Nano 30B A3B Base BF16 or Bonsai 8B be self-hosted?+

Both models have the same recorded self-hosting status: supported. NVIDIA Nemotron 3 Nano 30B A3B Base BF16 is open weight; Bonsai 8B is open weight.

Can NVIDIA Nemotron 3 Nano 30B A3B Base BF16 and Bonsai 8B understand images?+

NVIDIA Nemotron 3 Nano 30B A3B Base BF16 is not documented with image input; Bonsai 8B is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, NVIDIA Nemotron 3 Nano 30B A3B Base BF16 or Bonsai 8B?+

Neither has a larger sourced maximum output. NVIDIA Nemotron 3 Nano 30B A3B Base BF16 is 131K and Bonsai 8B is —.

Do NVIDIA Nemotron 3 Nano 30B A3B Base BF16 and Bonsai 8B support reasoning and tool use?+

NVIDIA Nemotron 3 Nano 30B A3B Base BF16: none of these features are definitively sourced. Bonsai 8B: none of these features are definitively sourced. Feature support does not establish relative quality.

Which is available from more inference providers, NVIDIA Nemotron 3 Nano 30B A3B Base BF16 or Bonsai 8B?+

NVIDIA Nemotron 3 Nano 30B A3B Base BF16 has 0 sourced provider routes; Bonsai 8B has 0, a tie.

Which offers better value, NVIDIA Nemotron 3 Nano 30B A3B Base BF16 or Bonsai 8B?+

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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