Llama 3.1 405B vs Phi-4 Mini Instruct
At a Glance
| Compare |  Llama 3.1 405BMeta |  Phi-4 Mini InstructMicrosoft |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 131K | 131K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 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 | Phi-4-mini-instruct |
|---|---|---|
| Developer | Meta | Microsoft |
| Family | Llama 3 1 405b | Phi 4 Mini Instruct |
| Model | Llama-3.1-405B | Phi-4-mini-instruct |
| Version | Llama-3.1-405B | Phi-4-mini-instruct |
| Lifecycle | active | active |
| Released | 2024-07-23 | 2025-02-26 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 131K | 131K |
| Total parameters | 405.9B | 3.8B |
| Active parameters | Unknown | Unknown |
| License | llama3.1 | mit |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Together Ai (Standard) | Hugging Face (Standard) |
| Capabilities | generation | chat, generation |
Llama 3.1 405B Capabilities
Phi-4 Mini Instruct Capabilities
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 DeepSeek-V4.1-FlashDeepSeek | vs | Llama-3.1-405BMeta | cross-developer peerstext |
Llama-3.1-405BMeta | vs | Grok 4.6xAI | cross-developer peerstext |
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 Phi-4 Mini Instruct FAQs
Is Llama 3.1 405B or Phi-4 Mini Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 405B and Phi-4 Mini Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 3.1 405B or Phi-4 Mini Instruct?+
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 Phi-4 Mini Instruct?+
Neither model has a larger sourced context window in this comparison. Llama 3.1 405B is 131K and Phi-4 Mini Instruct is 131K.
Which performs better in benchmarks, Llama 3.1 405B or Phi-4 Mini Instruct?+
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 Phi-4 Mini Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 3.1 405B is open weight; Phi-4 Mini Instruct is open weight.
Can Llama 3.1 405B and Phi-4 Mini Instruct understand images?+
Llama 3.1 405B is not documented with image input; Phi-4 Mini Instruct is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 3.1 405B or Phi-4 Mini Instruct?+
Neither has a larger sourced maximum output. Llama 3.1 405B is — and Phi-4 Mini Instruct is —.
Do Llama 3.1 405B and Phi-4 Mini Instruct support reasoning and tool use?+
Llama 3.1 405B: none of these features are definitively sourced. Phi-4 Mini Instruct: 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 Phi-4 Mini Instruct?+
Llama 3.1 405B has 1 sourced provider route; Phi-4 Mini Instruct has 1, a tie.
Which offers better value, Llama 3.1 405B or Phi-4 Mini Instruct?+
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.