Phi-4 Multimodal Instruct vs GPT-5.1

At a Glance

Compare
GPT-5.1OpenAI
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5UnrankedNot in the 46-model eligible cohort#37 of 4625.7 score · 2/3 sources · provisional · missing LiveBench · full-core range 17.1–50.5
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$1.25Openai · Sep 3, 2026
Output priceFrom · USD / 1M tokensNot reported$10.00Openai · Sep 3, 2026
Context windowMaximum documented tokens131K400K
Model facts checkedAug 28, 2026View model evidence →Sep 3, 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-multimodal-instructGPT-5.1
DeveloperMicrosoftOpenAI
FamilyPhi 4 Multimodal InstructGpt 5 1
ModelPhi-4-multimodal-instructGPT-5.1
VersionPhi-4-multimodal-instructGPT-5.1
Lifecycleactiveactive
Released2025-02-262025-11-13
Knowledge cutoffUnknown2024-09-30
Input modalitiesText, Image, AudioText, Image
Output modalitiesTextText
Context window131K400K
Total parameters5.6BUnknown
Active parametersUnknownUnknown
LicensemitUnknown
Open weightsYesNo
API availableUnknownYes
Self-hostableYesNo
Provider accessUnknownOpenai (Standard), Openrouter (Standard)
Capabilitieschat, generationchat, generation, reasoning, structured_outputs, tools

Phi-4 Multimodal Instruct Capabilities

chatgeneration
Serving providers0
Canonical IDmicrosoft/Phi-4-multimodal-instruct

GPT-5.1 Capabilities

chatgenerationreasoningstructured outputstools
Serving providers2
Canonical IDopenai/gpt-5.1

Primary Evidence

Sources and Freshness

Questions

Phi-4 Multimodal Instruct vs GPT-5.1 FAQs

Is Phi-4 Multimodal Instruct or GPT-5.1 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Phi-4 Multimodal Instruct and GPT-5.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Phi-4 Multimodal Instruct or GPT-5.1?+

Only GPT-5.1 has a directly sourced input price: $1.25 per million tokens. Only GPT-5.1 has a directly sourced output price: $10.00 per million tokens.

Which has a larger context window, Phi-4 Multimodal Instruct or GPT-5.1?+

GPT-5.1 has the larger sourced context window. Phi-4 Multimodal Instruct supports 131K and GPT-5.1 supports 400K.

Which performs better in benchmarks, Phi-4 Multimodal Instruct or GPT-5.1?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Phi-4 Multimodal Instruct or GPT-5.1 be self-hosted?+

Phi-4 Multimodal Instruct is the only model in this pair currently marked as self-hostable. Phi-4 Multimodal Instruct is open weight; GPT-5.1 is not marked open weight.

Can Phi-4 Multimodal Instruct and GPT-5.1 understand images?+

Phi-4 Multimodal Instruct is documented with image input; GPT-5.1 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Phi-4 Multimodal Instruct or GPT-5.1?+

Neither has a larger sourced maximum output. Phi-4 Multimodal Instruct is — and GPT-5.1 is 128K.

Do Phi-4 Multimodal Instruct and GPT-5.1 support reasoning and tool use?+

Phi-4 Multimodal Instruct: image input. GPT-5.1: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Phi-4 Multimodal Instruct or GPT-5.1?+

Phi-4 Multimodal Instruct has 0 sourced provider routes; GPT-5.1 has 2, so GPT-5.1 has broader tracked availability.

Which offers better value, Phi-4 Multimodal Instruct or GPT-5.1?+

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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