granite-4.1-8b vs Phi-4-mini-instruct
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | granite-4.1-8b | Phi-4-mini-instruct |
|---|---|---|
| Developer | IBM | Microsoft |
| Family | Granite 4 1 8b | Phi 4 Mini Instruct |
| Model | granite-4.1-8b | Phi-4-mini-instruct |
| Version | granite-4.1-8b | Phi-4-mini-instruct |
| Lifecycle | active | active |
| Released | 2026-04-29 | 2025-02-26 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 131,072 | 131,072 |
| Total parameters | 8,791,592,960 | 3,836,021,760 |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | mit |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Openrouter (Standard) | Hugging Face (Standard) |
| Capabilities | chat, generation, tools | chat, generation |
16 comparable fields · 9 material differences · Pair passes the primary-source comparison gate
granite-4.1-8b Capabilities
Phi-4-mini-instruct Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
| vs | family variantstext | ||
| vs | family variantstext | ||
| vs | family variantstext | ||
| vs | family variantstext | ||
Phi-4-mini-instructMicrosoft | vs | Phi-4-mini-reasoningMicrosoft | family variantstext |
Phi-4-mini-instructMicrosoft | vs | Phi-4-multimodal-instructMicrosoft | family variantstext |
Phi-4-mini-instructMicrosoft | vs | Phi-4-reasoningMicrosoft | family variantstext |
Phi-4-mini-instructMicrosoft | vs | phi-4Microsoft | family variantstext |
| vs | cross-developer peerstext | ||
| vs |  Ministral-3-8B-Reasoning-2512Mistral AI | cross-developer peerstext | |
| vs | Ministral-3-8B-Instruct-2512Mistral AI | cross-developer peerstext | |
| vs | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
granite-4.1-8b vs Phi-4-mini-instruct FAQs
Is granite-4.1-8b or Phi-4-mini-instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both granite-4.1-8b and Phi-4-mini-instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, granite-4.1-8b or Phi-4-mini-instruct?+
Only granite-4.1-8b has a directly sourced input price: $0.050 per million tokens. Only granite-4.1-8b has a directly sourced output price: $0.10 per million tokens.
Which has a larger context window, granite-4.1-8b or Phi-4-mini-instruct?+
Neither model has a larger sourced context window in this comparison. granite-4.1-8b is 131,072 and Phi-4-mini-instruct is 131,072.
Which performs better in benchmarks, granite-4.1-8b 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 granite-4.1-8b or Phi-4-mini-instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. granite-4.1-8b is open weight; Phi-4-mini-instruct is open weight.
Can granite-4.1-8b and Phi-4-mini-instruct understand images?+
granite-4.1-8b 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, granite-4.1-8b or Phi-4-mini-instruct?+
Neither has a larger sourced maximum output. granite-4.1-8b is — and Phi-4-mini-instruct is —.
Do granite-4.1-8b and Phi-4-mini-instruct support reasoning and tool use?+
granite-4.1-8b: tool calling. 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, granite-4.1-8b or Phi-4-mini-instruct?+
granite-4.1-8b has 1 sourced provider route; Phi-4-mini-instruct has 1, a tie.
Which offers better value, granite-4.1-8b 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.