Gemini 3.8 Flash Cyber vs Jev

At a Glance

Compare
Gemini 3.8 Flash CyberGoogle DeepMind
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 tokensNot reported64K
Model facts checkedSep 2, 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

FieldGemini 3.8 Flash CyberJev
DeveloperGoogle DeepMindTypeSafe
FamilyGemini 3Jev
ModelGemini 3.8 Flash CyberJev
VersionGemini 3.8 Flash CyberJev
Lifecycleactiveactive
Released2026-09-022026-09-15
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Model-specific input
Output modalitiesTextModel-specific input
Context windowUnknown64K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableNoYes
Self-hostableNoNo
Provider accessUnknownTypeSafe (Standard)
Capabilitiesautomated-patching, cybersecurity, reasoning, vulnerability-detectioncalibrated-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

Gemini 3.8 Flash Cyber Capabilities

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

Jev Capabilities

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

Primary Evidence

Sources and Freshness

Questions

Gemini 3.8 Flash Cyber vs Jev FAQs

Is Gemini 3.8 Flash Cyber or Jev better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.8 Flash Cyber and Jev, 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 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, Gemini 3.8 Flash Cyber or Jev?+

Neither model has a larger sourced context window in this comparison. Gemini 3.8 Flash Cyber is — and Jev is 64K.

Which performs better in benchmarks, Gemini 3.8 Flash Cyber or Jev?+

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 Jev be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. Gemini 3.8 Flash Cyber is not marked open weight; Jev is not marked open weight.

Can Gemini 3.8 Flash Cyber and Jev understand images?+

Gemini 3.8 Flash Cyber is not documented with image input; Jev is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemini 3.8 Flash Cyber or Jev?+

Neither has a larger sourced maximum output. Gemini 3.8 Flash Cyber is — and Jev is —.

Do Gemini 3.8 Flash Cyber and Jev support reasoning and tool use?+

Gemini 3.8 Flash Cyber: reasoning. Jev: none of these features are definitively sourced. Feature support does not establish relative quality.

Which is available from more inference providers, Gemini 3.8 Flash Cyber or Jev?+

Gemini 3.8 Flash Cyber has 0 sourced provider routes; Jev has 1, so Jev has broader tracked availability.

Which offers better value, Gemini 3.8 Flash Cyber 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.

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