Llama 3.1 405B vs Bonsai 27B
At a Glance
| Compare |  Llama 3.1 405BMeta |  Bonsai 27BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 131K | 262K |
| 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 | Bonsai 27B |
|---|---|---|
| Developer | Meta | PrismML |
| Family | Llama 3 1 405b | Bonsai 27b |
| Model | Llama-3.1-405B | Bonsai 27B |
| Version | Llama-3.1-405B | Bonsai 27B |
| Lifecycle | active | active |
| Released | 2024-07-23 | 2026-07-04 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 131K | 262K |
| Total parameters | 405.9B | 27B |
| 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, reasoning, tools, vision |
| Base model | Unknown | Qwen3.6 27B |
| Effective bit width | Unknown | 1 bit per weight |
| Language model size | Unknown | 3.53 GiB |
| Weight format | Unknown | Binary Q1_0 |
Llama 3.1 405B Capabilities
Bonsai 27B 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 |
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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 Bonsai 27B FAQs
Is Llama 3.1 405B or Bonsai 27B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 405B and Bonsai 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 3.1 405B or Bonsai 27B?+
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 Bonsai 27B?+
Bonsai 27B has the larger sourced context window. Llama 3.1 405B supports 131K and Bonsai 27B supports 262K.
Which performs better in benchmarks, Llama 3.1 405B or Bonsai 27B?+
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 Bonsai 27B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 3.1 405B is open weight; Bonsai 27B is open weight.
Can Llama 3.1 405B and Bonsai 27B understand images?+
Llama 3.1 405B is not documented with image input; Bonsai 27B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 3.1 405B or Bonsai 27B?+
Neither has a larger sourced maximum output. Llama 3.1 405B is — and Bonsai 27B is —.
Do Llama 3.1 405B and Bonsai 27B support reasoning and tool use?+
Llama 3.1 405B: none of these features are definitively sourced. Bonsai 27B: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 3.1 405B or Bonsai 27B?+
Llama 3.1 405B has 1 sourced provider route; Bonsai 27B has 0, so Llama 3.1 405B has broader tracked availability.
Which offers better value, Llama 3.1 405B or 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.