Granite 4.2 8B vs Phi-4 Mini Reasoning
At a Glance
| Compare | Phi-4 Mini ReasoningMicrosoft | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v4.4 | #43 of 570.0 score · 1/3 sources · single-source provisional · missing LiveBench, ARC-AGI-2 · full-core range 0.0–66.7 | UnrankedNot in the 57-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.060Deepinfra ↗ · Oct 19, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $0.25Deepinfra ↗ · Oct 19, 2026 | Not reported |
| Context windowMaximum documented tokens | 131K | 131K |
| Model facts checked | Sep 2, 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 | 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 | 131K | 131K |
| Total parameters | 8.8B | 3.8B |
| 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 |
Granite 4.2 8B Capabilities
Phi-4 Mini Reasoning Capabilities
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: $0.25 per million tokens.
Which has a larger context window, Granite 4.2 8B or Phi-4 Mini Reasoning?+
Both models have the same sourced context window: 131,072 tokens each.
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 sourced 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 model has a sourced maximum output in this comparison, so a larger limit cannot be determined.
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.