Llama 3.1 405B Instruct vs Bonsai 1.7B

At a Glance

Compare
Pricing and Limits
Context windowMaximum documented tokens131K33K
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

FieldLlama-3.1-405B-InstructBonsai 1.7B
DeveloperMetaPrismML
FamilyLlama 3 1 405b InstructBonsai 1 7b
ModelLlama-3.1-405B-InstructBonsai 1.7B
VersionLlama-3.1-405B-InstructBonsai 1.7B
Lifecycleactiveactive
Released2024-07-232026-03-29
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesTextText
Context window131K33K
Total parameters405.9B1.7B
Active parametersUnknownUnknown
Licensellama3.1apache-2.0
Open weightsYesYes
API availableYesNo
Self-hostableYesYes
Provider accessTogether Ai (Standard)Unknown
Capabilitieschat, generation, toolschat, generation
Effective bit widthUnknown1 bit per weight
Weight sizeUnknown0.25 GB
Weight formatUnknownBinary Q1_0

Llama 3.1 405B Instruct Capabilities

chatgenerationtools
Serving providers1
Canonical IDmeta-llama/Llama-3.1-405B-Instruct

Bonsai 1.7B Capabilities

chatgeneration
Serving providers0
Canonical IDprism-ml/Bonsai-1.7B

Primary Evidence

Sources and Freshness

Questions

Llama 3.1 405B Instruct vs Bonsai 1.7B FAQs

Is Llama 3.1 405B Instruct or Bonsai 1.7B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 405B Instruct and Bonsai 1.7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Llama 3.1 405B Instruct or Bonsai 1.7B?+

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, Llama 3.1 405B Instruct or Bonsai 1.7B?+

Llama 3.1 405B Instruct has the larger sourced context window. Llama 3.1 405B Instruct supports 131K and Bonsai 1.7B supports 33K.

Which performs better in benchmarks, Llama 3.1 405B Instruct or Bonsai 1.7B?+

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

Can Llama 3.1 405B Instruct or Bonsai 1.7B be self-hosted?+

Both models have the same recorded self-hosting status: supported. Llama 3.1 405B Instruct is open weight; Bonsai 1.7B is open weight.

Can Llama 3.1 405B Instruct and Bonsai 1.7B understand images?+

Llama 3.1 405B Instruct is not documented with image input; Bonsai 1.7B is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Llama 3.1 405B Instruct or Bonsai 1.7B?+

Neither has a larger sourced maximum output. Llama 3.1 405B Instruct is — and Bonsai 1.7B is —.

Do Llama 3.1 405B Instruct and Bonsai 1.7B support reasoning and tool use?+

Llama 3.1 405B Instruct: tool calling. Bonsai 1.7B: none of these features are definitively sourced. Feature support does not establish relative quality.

Which is available from more inference providers, Llama 3.1 405B Instruct or Bonsai 1.7B?+

Llama 3.1 405B Instruct has 1 sourced provider route; Bonsai 1.7B has 0, so Llama 3.1 405B Instruct has broader tracked availability.

Which offers better value, Llama 3.1 405B Instruct or Bonsai 1.7B?+

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.

Send Feedback