Gemini 3.8 Flash Cyber vs Phi-4 Multimodal Instruct

At a Glance

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Gemini 3.8 Flash CyberGoogle DeepMind
Pricing and Limits
Context windowMaximum documented tokensNot reported131K
Model facts checkedSep 2, 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

FieldGemini 3.8 Flash CyberPhi-4-multimodal-instruct
DeveloperGoogle DeepMindMicrosoft
FamilyGemini 3Phi 4 Multimodal Instruct
ModelGemini 3.8 Flash CyberPhi-4-multimodal-instruct
VersionGemini 3.8 Flash CyberPhi-4-multimodal-instruct
Lifecycleactiveactive
Released2026-09-022025-02-26
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image, Audio
Output modalitiesTextText
Context windowUnknown131K
Total parametersUnknown5.6B
Active parametersUnknownUnknown
LicenseUnknownmit
Open weightsNoYes
API availableNoUnknown
Self-hostableNoYes
Provider accessUnknownUnknown
Capabilitiesautomated-patching, cybersecurity, reasoning, vulnerability-detectionchat, generation

Gemini 3.8 Flash Cyber Capabilities

automated-patchingcybersecurityreasoningvulnerability-detection
Serving providers0
Canonical IDgoogle-deepmind/gemini-3.8-flash-cyber

Phi-4 Multimodal Instruct Capabilities

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

Primary Evidence

Sources and Freshness

Questions

Gemini 3.8 Flash Cyber vs Phi-4 Multimodal Instruct FAQs

Is Gemini 3.8 Flash Cyber or Phi-4 Multimodal Instruct better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.8 Flash Cyber and Phi-4 Multimodal Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Gemini 3.8 Flash Cyber or Phi-4 Multimodal Instruct?+

Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.

Which has a larger context window, Gemini 3.8 Flash Cyber or Phi-4 Multimodal Instruct?+

Neither model has a larger sourced context window in this comparison. Gemini 3.8 Flash Cyber is — and Phi-4 Multimodal Instruct is 131K.

Which performs better in benchmarks, Gemini 3.8 Flash Cyber or Phi-4 Multimodal Instruct?+

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

Can Gemini 3.8 Flash Cyber or Phi-4 Multimodal Instruct be self-hosted?+

Phi-4 Multimodal Instruct is the only model in this pair currently marked as self-hostable. Gemini 3.8 Flash Cyber is not marked open weight; Phi-4 Multimodal Instruct is open weight.

Can Gemini 3.8 Flash Cyber and Phi-4 Multimodal Instruct understand images?+

Gemini 3.8 Flash Cyber is not documented with image input; Phi-4 Multimodal Instruct is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemini 3.8 Flash Cyber or Phi-4 Multimodal Instruct?+

Neither has a larger sourced maximum output. Gemini 3.8 Flash Cyber is — and Phi-4 Multimodal Instruct is —.

Do Gemini 3.8 Flash Cyber and Phi-4 Multimodal Instruct support reasoning and tool use?+

Gemini 3.8 Flash Cyber: reasoning. Phi-4 Multimodal Instruct: image input. Feature support does not establish relative quality.

Which is available from more inference providers, Gemini 3.8 Flash Cyber or Phi-4 Multimodal Instruct?+

Gemini 3.8 Flash Cyber has 0 sourced provider routes; Phi-4 Multimodal Instruct has 0, a tie.

Which offers better value, Gemini 3.8 Flash Cyber or Phi-4 Multimodal Instruct?+

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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