Phi-4 Multimodal Instruct vs Jev

At a Glance

Compare
JevTypeSafe
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$0.042TypeSafe · Sep 17, 2026
Output priceFrom · USD / 1M tokensNot reported$0.000TypeSafe · Sep 17, 2026
Context windowMaximum documented tokens131K64K
Model facts checkedAug 28, 2026View model evidence →Sep 17, 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-instructJev
DeveloperMicrosoftTypeSafe
FamilyPhi 4 Multimodal InstructJev
ModelPhi-4-multimodal-instructJev
VersionPhi-4-multimodal-instructJev
Lifecycleactiveactive
Released2025-02-262026-09-15
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, AudioText, Model-specific input
Output modalitiesTextModel-specific input
Context window131K64K
Total parameters5.6BUnknown
Active parametersUnknownUnknown
LicensemitUnknown
Open weightsYesNo
API availableUnknownYes
Self-hostableYesNo
Provider accessUnknownTypeSafe (Standard)
Capabilitieschat, generationcalibrated-confidence, parallel-evaluation, structured_outputs, typed-decisions
Maximum Choice cardinalityUnknown255 options
Default request rate limitUnknown1200 requests per minute
State plus longest question limitUnknown32000 tokens
Combined state and questions limitUnknown64000 tokens
Default token rate limitUnknown250000 tokens per second

Phi-4 Multimodal Instruct Capabilities

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

Jev Capabilities

calibrated-confidenceparallel-evaluationstructured outputstyped-decisions
Serving providers1
Canonical IDtypesafe/jev

Primary Evidence

Sources and Freshness

Questions

Phi-4 Multimodal Instruct vs Jev FAQs

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

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

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

Only Jev has a directly sourced input price: $0.042 per million tokens. Only Jev has a directly sourced output price: $0.000 per million tokens.

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

Phi-4 Multimodal Instruct has the larger sourced context window. Phi-4 Multimodal Instruct supports 131K and Jev supports 64K.

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

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 Jev 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; Jev is not marked open weight.

Can Phi-4 Multimodal Instruct and Jev understand images?+

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

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

Neither has a larger sourced maximum output. Phi-4 Multimodal Instruct is — and Jev is —.

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

Phi-4 Multimodal Instruct: image input. Jev: none of these features are definitively sourced. Feature support does not establish relative quality.

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

Phi-4 Multimodal Instruct has 0 sourced provider routes; Jev has 1, so Jev has broader tracked availability.

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

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.

Send Feedback