Phi-4 Reasoning vs GPT-5.6 Terra
At a Glance
| Compare |  Phi-4 ReasoningMicrosoft |  GPT-5.6 TerraOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #13 of 4674.9 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #35 of 44$0.266 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #23 of 3851.5 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $2.00Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $12.00Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 33K | 1,050K |
| 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-reasoning | GPT-5.6 Terra |
|---|---|---|
| Developer | Microsoft | OpenAI |
| Family | Phi 4 Reasoning | Gpt 5 6 |
| Model | Phi-4-reasoning | GPT-5.6 Terra |
| Version | Phi-4-reasoning | GPT-5.6 Terra |
| Lifecycle | active | active |
| Released | 2025-04-30 | Unknown |
| Knowledge cutoff | Unknown | 2026-02-16 |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 33K | 1,050K |
| 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, reasoning, tools |
Phi-4 Reasoning Capabilities
GPT-5.6 Terra Capabilities
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Primary Evidence
Sources and Freshness
Questions
Phi-4 Reasoning vs GPT-5.6 Terra FAQs
Is Phi-4 Reasoning or GPT-5.6 Terra better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Phi-4 Reasoning and GPT-5.6 Terra, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Phi-4 Reasoning or GPT-5.6 Terra?+
Only GPT-5.6 Terra has a directly sourced input price: $2.00 per million tokens. Only GPT-5.6 Terra has a directly sourced output price: $12.00 per million tokens.
Which has a larger context window, Phi-4 Reasoning or GPT-5.6 Terra?+
GPT-5.6 Terra has the larger sourced context window. Phi-4 Reasoning supports 33K and GPT-5.6 Terra supports 1,050K.
Which performs better in benchmarks, Phi-4 Reasoning or GPT-5.6 Terra?+
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-5.6 Terra 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-5.6 Terra is not marked open weight.
Can Phi-4 Reasoning and GPT-5.6 Terra understand images?+
Phi-4 Reasoning is not documented with image input; GPT-5.6 Terra is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Phi-4 Reasoning or GPT-5.6 Terra?+
Neither has a larger sourced maximum output. Phi-4 Reasoning is — and GPT-5.6 Terra is 128K.
Do Phi-4 Reasoning and GPT-5.6 Terra support reasoning and tool use?+
Phi-4 Reasoning: reasoning. GPT-5.6 Terra: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Phi-4 Reasoning or GPT-5.6 Terra?+
Phi-4 Reasoning has 0 sourced provider routes; GPT-5.6 Terra has 2, so GPT-5.6 Terra has broader tracked availability.
Which offers better value, Phi-4 Reasoning or GPT-5.6 Terra?+
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.