Llama 4 Scout 17B 16E Instruct vs Phi-4 Reasoning
At a Glance
| Compare |  Phi-4 ReasoningMicrosoft | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.10Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $0.30Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Context windowMaximum documented tokens | 10,000K | 33K |
| 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-4-Scout-17B-16E-Instruct | Phi-4-reasoning |
|---|---|---|
| Developer | Meta | Microsoft |
| Family | Llama 4 Scout 17b 16e Instruct | Phi 4 Reasoning |
| Model | Llama-4-Scout-17B-16E-Instruct | Phi-4-reasoning |
| Version | Llama-4-Scout-17B-16E-Instruct | Phi-4-reasoning |
| Lifecycle | active | active |
| Released | 2025-04-05 | 2025-04-30 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 10,000K | 33K |
| Total parameters | 108.6B | 14.7B |
| Active parameters | 17B | Unknown |
| License | other | mit |
| Open weights | Yes | Yes |
| API available | Yes | Unknown |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Unknown |
| Capabilities | chat, generation, tools | chat, generation, reasoning |
Llama 4 Scout 17B 16E Instruct Capabilities
Phi-4 Reasoning Capabilities
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Primary Evidence
Sources and Freshness
Questions
Llama 4 Scout 17B 16E Instruct vs Phi-4 Reasoning FAQs
Is Llama 4 Scout 17B 16E Instruct or Phi-4 Reasoning better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Scout 17B 16E Instruct and Phi-4 Reasoning, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 4 Scout 17B 16E Instruct or Phi-4 Reasoning?+
Only Llama 4 Scout 17B 16E Instruct has a directly sourced input price: $0.10 per million tokens. Only Llama 4 Scout 17B 16E Instruct has a directly sourced output price: $0.30 per million tokens.
Which has a larger context window, Llama 4 Scout 17B 16E Instruct or Phi-4 Reasoning?+
Llama 4 Scout 17B 16E Instruct has the larger sourced context window. Llama 4 Scout 17B 16E Instruct supports 10,000K and Phi-4 Reasoning supports 33K.
Which performs better in benchmarks, Llama 4 Scout 17B 16E Instruct or Phi-4 Reasoning?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Llama 4 Scout 17B 16E Instruct or Phi-4 Reasoning be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 4 Scout 17B 16E Instruct is open weight; Phi-4 Reasoning is open weight.
Can Llama 4 Scout 17B 16E Instruct and Phi-4 Reasoning understand images?+
Llama 4 Scout 17B 16E Instruct is documented with image input; Phi-4 Reasoning is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 4 Scout 17B 16E Instruct or Phi-4 Reasoning?+
Neither has a larger sourced maximum output. Llama 4 Scout 17B 16E Instruct is — and Phi-4 Reasoning is —.
Do Llama 4 Scout 17B 16E Instruct and Phi-4 Reasoning support reasoning and tool use?+
Llama 4 Scout 17B 16E Instruct: tool calling and image input. Phi-4 Reasoning: reasoning. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 4 Scout 17B 16E Instruct or Phi-4 Reasoning?+
Llama 4 Scout 17B 16E Instruct has 4 sourced provider routes; Phi-4 Reasoning has 0, so Llama 4 Scout 17B 16E Instruct has broader tracked availability.
Which offers better value, Llama 4 Scout 17B 16E Instruct or Phi-4 Reasoning?+
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.