Granite 4.2 8B vs Phi-4-mini-reasoning
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | Granite 4.2 8B | Phi-4-mini-reasoning |
|---|---|---|
| Developer | IBM | Microsoft |
| Family | Granite 4 2 | Phi 4 Mini Reasoning |
| Model | Granite 4.2 8B | Phi-4-mini-reasoning |
| Version | Granite 4.2 8B | Phi-4-mini-reasoning |
| Lifecycle | active | active |
| Released | 2026-08-25 | 2025-04-30 |
| 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 | Unknown |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Openrouter (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | chat, generation, reasoning |
14 comparable fields · 8 material differences · Pair passes the primary-source comparison gate
Granite 4.2 8B Capabilities
Phi-4-mini-reasoning Capabilities
Internal Comparison Graph
Related Comparisons
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|---|---|---|---|
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| vs | family variantstext | ||
| vs | family variantstext | ||
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Phi-4-mini-reasoningMicrosoft | vs | Phi-4-reasoningMicrosoft | family variantstext |
Phi-4-mini-instructMicrosoft | vs | Phi-4-mini-reasoningMicrosoft | family variantstext |
Phi-4-mini-reasoningMicrosoft | vs | Phi-4-multimodal-instructMicrosoft | family variantstext |
Gemma 4 E2BGoogle DeepMind | vs | cross-developer peerstext | |
Phi-4-mini-reasoningMicrosoft | vs | phi-4Microsoft | family variantstext |
| vs | family variantstext | ||
| vs |  gpt-oss-20bOpenAI | cross-developer peerstext | |
| vs | Phi-4-mini-reasoningMicrosoft | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Granite 4.2 8B vs Phi-4-mini-reasoning FAQs
Is Granite 4.2 8B or Phi-4-mini-reasoning better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Granite 4.2 8B and Phi-4-mini-reasoning, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Granite 4.2 8B or Phi-4-mini-reasoning?+
Only Granite 4.2 8B has a directly sourced input price: $0.060 per million tokens. Only Granite 4.2 8B has a directly sourced output price: $0.15 per million tokens.
Which has a larger context window, Granite 4.2 8B or Phi-4-mini-reasoning?+
Neither model has a larger sourced context window in this comparison. Granite 4.2 8B is 131,072 and Phi-4-mini-reasoning is 131,072.
Which performs better in benchmarks, Granite 4.2 8B or Phi-4-mini-reasoning?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Granite 4.2 8B or Phi-4-mini-reasoning be self-hosted?+
Both models have the same recorded self-hosting status: supported. Granite 4.2 8B is open weight; Phi-4-mini-reasoning is open weight.
Can Granite 4.2 8B and Phi-4-mini-reasoning understand images?+
Granite 4.2 8B is not documented with image input; Phi-4-mini-reasoning is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Granite 4.2 8B or Phi-4-mini-reasoning?+
Neither has a larger sourced maximum output. Granite 4.2 8B is — and Phi-4-mini-reasoning is —.
Do Granite 4.2 8B and Phi-4-mini-reasoning support reasoning and tool use?+
Granite 4.2 8B: reasoning and tool calling. Phi-4-mini-reasoning: reasoning. Feature support does not establish relative quality.
Which is available from more inference providers, Granite 4.2 8B or Phi-4-mini-reasoning?+
Granite 4.2 8B has 2 sourced provider routes; Phi-4-mini-reasoning has 0, so Granite 4.2 8B has broader tracked availability.
Which offers better value, Granite 4.2 8B or Phi-4-mini-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.