Phi-4 Reasoning vs GPT-5.6 Luna

At a Glance

Compare
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5UnrankedNot in the 46-model eligible cohort#31 of 4652.7 score · 3/3 sources · complete
CostLower is better · Published-token output estimateUnrankedNot in the 44-model eligible cohort#6 of 44$0.026 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5UnrankedNot in the 38-model eligible cohort#4 of 3864.8 score · 3/3 sources · complete
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$0.20Openai · Sep 3, 2026
Output priceFrom · USD / 1M tokensNot reported$1.20Openai · Sep 3, 2026
Context windowMaximum documented tokens33K1,050K
Model facts checkedAug 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

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldPhi-4-reasoningGPT-5.6 Luna
DeveloperMicrosoftOpenAI
FamilyPhi 4 ReasoningGpt 5 6
ModelPhi-4-reasoningGPT-5.6 Luna
VersionPhi-4-reasoningGPT-5.6 Luna
Lifecycleactiveactive
Released2025-04-30Unknown
Knowledge cutoffUnknown2026-02-16
Input modalitiesTextText, Image
Output modalitiesTextText
Context window33K1,050K
Total parameters14.7BUnknown
Active parametersUnknownUnknown
LicensemitUnknown
Open weightsYesNo
API availableUnknownYes
Self-hostableYesNo
Provider accessUnknownOpenai (Standard), Openrouter (Standard)
Capabilitieschat, generation, reasoningchat, generation, reasoning, tools

Phi-4 Reasoning Capabilities

chatgenerationreasoning
Serving providers0
Canonical IDmicrosoft/Phi-4-reasoning

GPT-5.6 Luna Capabilities

chatgenerationreasoningtools
Serving providers2
Canonical IDopenai/gpt-5.6-luna

Primary Evidence

Sources and Freshness

Questions

Phi-4 Reasoning vs GPT-5.6 Luna FAQs

Is Phi-4 Reasoning or GPT-5.6 Luna better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Phi-4 Reasoning and GPT-5.6 Luna, 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 Luna?+

Only GPT-5.6 Luna has a directly sourced input price: $0.20 per million tokens. Only GPT-5.6 Luna has a directly sourced output price: $1.20 per million tokens.

Which has a larger context window, Phi-4 Reasoning or GPT-5.6 Luna?+

GPT-5.6 Luna has the larger sourced context window. Phi-4 Reasoning supports 33K and GPT-5.6 Luna supports 1,050K.

Which performs better in benchmarks, Phi-4 Reasoning or GPT-5.6 Luna?+

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 Luna 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 Luna is not marked open weight.

Can Phi-4 Reasoning and GPT-5.6 Luna understand images?+

Phi-4 Reasoning is not documented with image input; GPT-5.6 Luna 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 Luna?+

Neither has a larger sourced maximum output. Phi-4 Reasoning is — and GPT-5.6 Luna is 128K.

Do Phi-4 Reasoning and GPT-5.6 Luna support reasoning and tool use?+

Phi-4 Reasoning: reasoning. GPT-5.6 Luna: 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 Luna?+

Phi-4 Reasoning has 0 sourced provider routes; GPT-5.6 Luna has 2, so GPT-5.6 Luna has broader tracked availability.

Which offers better value, Phi-4 Reasoning or GPT-5.6 Luna?+

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.

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