Llama 3.1 405B vs Ternary Bonsai 4B
At a Glance
| Compare |  Llama 3.1 405BMeta |  Ternary Bonsai 4BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 131K | 33K |
| Model facts checked | Aug 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
Side-by-Side Facts
| Field | Llama-3.1-405B | Ternary Bonsai 4B |
|---|---|---|
| Developer | Meta | PrismML |
| Family | Llama 3 1 405b | Bonsai 4b |
| Model | Llama-3.1-405B | Ternary Bonsai 4B |
| Version | Llama-3.1-405B | Ternary Bonsai 4B |
| Lifecycle | active | active |
| Released | 2024-07-23 | 2026-04-18 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 131K | 33K |
| Total parameters | 405.9B | 4B |
| Active parameters | Unknown | Unknown |
| License | llama3.1 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | No |
| Self-hostable | Yes | Yes |
| Provider access | Together Ai (Standard) | Unknown |
| Capabilities | generation | chat, generation |
| Effective bit width | Unknown | 1.58 bits per weight |
| Weight size | Unknown | 1.07 GB |
| Weight format | Unknown | Ternary Q2_0 |
Llama 3.1 405B Capabilities
Ternary Bonsai 4B Capabilities
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Llama-3.1-405BMeta | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
Llama-3.1-405BMeta | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | Llama-3.1-405BMeta | cross-developer peerstext |
Llama-3.1-405BMeta | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Llama-3.1-405BMeta | vs |  Hy4 previewTencent | cross-developer peerstext |
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Llama-3.1-405BMeta | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Llama 3.1 405B vs Ternary Bonsai 4B FAQs
Is Llama 3.1 405B or Ternary Bonsai 4B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 405B and Ternary Bonsai 4B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 3.1 405B or Ternary Bonsai 4B?+
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 or Ternary Bonsai 4B?+
Llama 3.1 405B has the larger sourced context window. Llama 3.1 405B supports 131K and Ternary Bonsai 4B supports 33K.
Which performs better in benchmarks, Llama 3.1 405B or Ternary Bonsai 4B?+
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 or Ternary Bonsai 4B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 3.1 405B is open weight; Ternary Bonsai 4B is open weight.
Can Llama 3.1 405B and Ternary Bonsai 4B understand images?+
Llama 3.1 405B is not documented with image input; Ternary Bonsai 4B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 3.1 405B or Ternary Bonsai 4B?+
Neither has a larger sourced maximum output. Llama 3.1 405B is — and Ternary Bonsai 4B is —.
Do Llama 3.1 405B and Ternary Bonsai 4B support reasoning and tool use?+
Llama 3.1 405B: none of these features are definitively sourced. Ternary Bonsai 4B: 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 or Ternary Bonsai 4B?+
Llama 3.1 405B has 1 sourced provider route; Ternary Bonsai 4B has 0, so Llama 3.1 405B has broader tracked availability.
Which offers better value, Llama 3.1 405B or Ternary Bonsai 4B?+
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.