Phi-4 Reasoning vs GPT-4.1 Mini
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | Phi-4-reasoning | GPT-4.1 Mini |
|---|---|---|
| Developer | Microsoft | OpenAI |
| Family | Phi 4 Reasoning | Gpt 4 1 |
| Model | Phi-4-reasoning | GPT-4.1 Mini |
| Version | Phi-4-reasoning | GPT-4.1 Mini |
| Lifecycle | active | active |
| Released | 2025-04-30 | Unknown |
| Knowledge cutoff | Unknown | 2024-06-01 |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 33K | 1,048K |
| Total parameters | 14.7B | Unknown |
| Active parameters | Unknown | Unknown |
| License | mit | Unknown |
| Open weights | Yes | No |
| API available | Unknown | Yes |
| Self-hostable | Yes | No |
| Provider access | Unknown | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning | chat, generation, tools |
11 comparable fields · 9 material differences · Pair passes the primary-source comparison gate
Phi-4 Reasoning Capabilities
GPT-4.1 Mini Capabilities
Internal Comparison Graph
Related Comparisons
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|---|---|---|---|
Phi-4-reasoningMicrosoft | vs | GPT-6 AstraOpenAI | cross-developer peerstext |
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 DeepSeek-V4-ProDeepSeek | vs | Phi-4-reasoningMicrosoft | cross-developer peerstext |
Phi-4-reasoningMicrosoft | vs | Grok 4.6xAI | cross-developer peerstext |
Phi-4-reasoningMicrosoft | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
Phi-4-reasoningMicrosoft | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | Phi-4-reasoningMicrosoft | cross-developer peerstext |
Phi-4-reasoningMicrosoft | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Phi-4-reasoningMicrosoft | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | Phi-4-reasoningMicrosoft | cross-developer peerstext |
Phi-4-reasoningMicrosoft | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Phi-4 Reasoning vs GPT-4.1 Mini FAQs
Is Phi-4 Reasoning or GPT-4.1 Mini better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Phi-4 Reasoning and GPT-4.1 Mini, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Phi-4 Reasoning or GPT-4.1 Mini?+
Only GPT-4.1 Mini has a directly sourced input price: $0.20 per million tokens. Only GPT-4.1 Mini has a directly sourced output price: $0.80 per million tokens.
Which has a larger context window, Phi-4 Reasoning or GPT-4.1 Mini?+
GPT-4.1 Mini has the larger sourced context window. Phi-4 Reasoning supports 33K and GPT-4.1 Mini supports 1,048K.
Which performs better in benchmarks, Phi-4 Reasoning or GPT-4.1 Mini?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Phi-4 Reasoning or GPT-4.1 Mini be self-hosted?+
Phi-4 Reasoning is the only model in this pair currently marked as self-hostable. Phi-4 Reasoning is open weight; GPT-4.1 Mini is not marked open weight.
Can Phi-4 Reasoning and GPT-4.1 Mini understand images?+
Phi-4 Reasoning is not documented with image input; GPT-4.1 Mini is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Phi-4 Reasoning or GPT-4.1 Mini?+
Neither has a larger sourced maximum output. Phi-4 Reasoning is — and GPT-4.1 Mini is 33K.
Do Phi-4 Reasoning and GPT-4.1 Mini support reasoning and tool use?+
Phi-4 Reasoning: reasoning. GPT-4.1 Mini: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Phi-4 Reasoning or GPT-4.1 Mini?+
Phi-4 Reasoning has 0 sourced provider routes; GPT-4.1 Mini has 2, so GPT-4.1 Mini has broader tracked availability.
Which offers better value, Phi-4 Reasoning or GPT-4.1 Mini?+
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.