Gemini 2.5 Pro vs Jev

At a Glance

Compare
Gemini 2.5 ProGoogle DeepMind
JevTypeSafe
Pricing and Limits
Input priceFrom · USD / 1M tokens$1.25Google AI · Aug 29, 2026$0.042TypeSafe · Sep 17, 2026
Output priceFrom · USD / 1M tokens$10.00Google AI · Aug 29, 2026$0.000TypeSafe · Sep 17, 2026
Context windowMaximum documented tokens1,049K64K
Model facts checkedAug 29, 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 2.5 ProJev
DeveloperGoogle DeepMindTypeSafe
FamilyGemini 2 5Jev
ModelGemini 2.5 ProJev
VersionGemini 2.5 ProJev
Lifecycleactiveactive
ReleasedUnknown2026-09-15
Knowledge cutoff2025-01-01Unknown
Input modalitiesText, Image, Video, Audio, DocumentText, Model-specific input
Output modalitiesTextModel-specific input
Context window1,049K64K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessGoogle AI (Standard), Google Gemini (Standard)TypeSafe (Standard)
Capabilitieschat, generation, reasoning, toolscalibrated-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 2.5 Pro Capabilities

chatgenerationreasoningtools
Serving providers2
Canonical IDgoogle-deepmind/gemini-2.5-pro

Jev Capabilities

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

Primary Evidence

Sources and Freshness

Questions

Gemini 2.5 Pro vs Jev FAQs

Is Gemini 2.5 Pro or Jev better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 2.5 Pro and Jev, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Gemini 2.5 Pro or Jev?+

Gemini 2.5 Pro is $1.25 and Jev is $0.042 per million tokens, so Jev is cheaper on this metric. Gemini 2.5 Pro is $10.00 and Jev is $0.000 per million tokens, so Jev is cheaper on this metric.

Which has a larger context window, Gemini 2.5 Pro or Jev?+

Gemini 2.5 Pro has the larger sourced context window. Gemini 2.5 Pro supports 1,049K and Jev supports 64K.

Which performs better in benchmarks, Gemini 2.5 Pro or Jev?+

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

Can Gemini 2.5 Pro or Jev be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. Gemini 2.5 Pro is not marked open weight; Jev is not marked open weight.

Can Gemini 2.5 Pro and Jev understand images?+

Gemini 2.5 Pro 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, Gemini 2.5 Pro or Jev?+

Neither has a larger sourced maximum output. Gemini 2.5 Pro is 66K and Jev is —.

Do Gemini 2.5 Pro and Jev support reasoning and tool use?+

Gemini 2.5 Pro: reasoning, tool calling, and image input. Jev: none of these features are definitively sourced. Feature support does not establish relative quality.

Which is available from more inference providers, Gemini 2.5 Pro or Jev?+

Gemini 2.5 Pro has 2 sourced provider routes; Jev has 1, so Gemini 2.5 Pro has broader tracked availability.

Which offers better value, Gemini 2.5 Pro 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