Phi-4 Mini Instruct vs gpt-oss-20b
At a Glance
| Compare |  Phi-4 Mini InstructMicrosoft |  gpt-oss-20bOpenAI |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.018Openrouter ↗ · Sep 23, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.090Openrouter ↗ · Sep 23, 2026 |
| Context windowMaximum documented tokens | 131K | 131K |
| 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 | Phi-4-mini-instruct | gpt-oss-20b |
|---|---|---|
| Developer | Microsoft | OpenAI |
| Family | Phi 4 Mini Instruct | Gpt Oss |
| Model | Phi-4-mini-instruct | gpt-oss-20b |
| Version | Phi-4-mini-instruct | gpt-oss-20b |
| Lifecycle | active | active |
| Released | 2025-02-26 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 131K | 131K |
| Total parameters | 3.8B | 20.9B |
| Active parameters | Unknown | 3.6B |
| License | mit | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Hugging Face (Standard) | Deepinfra (Standard), Groq (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Serverless, Standard) |
| Capabilities | chat, generation | chat, generation, reasoning, tools |
Phi-4 Mini Instruct Capabilities
gpt-oss-20b Capabilities
Related Comparisons
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Phi-4-mini-instructMicrosoft | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Phi-4-mini-instructMicrosoft | vs |  Hy4 previewTencent | cross-developer peerstext |
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Phi-4-mini-instructMicrosoft | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Phi-4 Mini Instruct vs gpt-oss-20b FAQs
Is Phi-4 Mini Instruct or gpt-oss-20b better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Phi-4 Mini Instruct and gpt-oss-20b, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Phi-4 Mini Instruct or gpt-oss-20b?+
Only gpt-oss-20b has a directly sourced input price: $0.018 per million tokens. Only gpt-oss-20b has a directly sourced output price: $0.090 per million tokens.
Which has a larger context window, Phi-4 Mini Instruct or gpt-oss-20b?+
Neither model has a larger sourced context window in this comparison. Phi-4 Mini Instruct is 131K and gpt-oss-20b is 131K.
Which performs better in benchmarks, Phi-4 Mini Instruct or gpt-oss-20b?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Phi-4 Mini Instruct or gpt-oss-20b be self-hosted?+
Both models have the same recorded self-hosting status: supported. Phi-4 Mini Instruct is open weight; gpt-oss-20b is open weight.
Can Phi-4 Mini Instruct and gpt-oss-20b understand images?+
Phi-4 Mini Instruct is not documented with image input; gpt-oss-20b is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Phi-4 Mini Instruct or gpt-oss-20b?+
Neither has a larger sourced maximum output. Phi-4 Mini Instruct is — and gpt-oss-20b is —.
Do Phi-4 Mini Instruct and gpt-oss-20b support reasoning and tool use?+
Phi-4 Mini Instruct: none of these features are definitively sourced. gpt-oss-20b: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Phi-4 Mini Instruct or gpt-oss-20b?+
Phi-4 Mini Instruct has 1 sourced provider route; gpt-oss-20b has 5, so gpt-oss-20b has broader tracked availability.
Which offers better value, Phi-4 Mini Instruct or gpt-oss-20b?+
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.