ERNIE X1.1 vs Gemini 3.1 Flash Lite

At a Glance

Compare
Gemini 3.1 Flash LiteGoogle DeepMind
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$0.25Google AI · Aug 29, 2026
Output priceFrom · USD / 1M tokensNot reported$1.50Google AI · Aug 29, 2026
Context windowMaximum documented tokens66K1,049K
Model facts checkedAug 29, 2026View model evidence →Aug 29, 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

FieldERNIE X1.1Gemini 3.1 Flash-Lite
DeveloperBaiduGoogle DeepMind
FamilyErnie X1Gemini 3
ModelERNIE X1.1Gemini 3.1 Flash-Lite
VersionERNIE X1.1Gemini 3.1 Flash-Lite
Lifecycleactiveactive
Released2025-09-26Unknown
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image, Video, Audio, Document
Output modalitiesTextText
Context window66K1,049K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessBaidu Qianfan (Standard)Google AI (Standard), Google Gemini (Standard)
Capabilitiesagents, chat, reasoning, search, toolschat, generation, reasoning, tools

ERNIE X1.1 Capabilities

agentschatreasoningsearchtools
Serving providers1
Canonical IDbaidu/ernie-x1.1

Gemini 3.1 Flash Lite Capabilities

chatgenerationreasoningtools
Serving providers2
Canonical IDgoogle-deepmind/gemini-3.1-flash-lite

Primary Evidence

Sources and Freshness

Questions

ERNIE X1.1 vs Gemini 3.1 Flash Lite FAQs

Is ERNIE X1.1 or Gemini 3.1 Flash Lite better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both ERNIE X1.1 and Gemini 3.1 Flash Lite, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, ERNIE X1.1 or Gemini 3.1 Flash Lite?+

ERNIE X1.1 is $1.00 and Gemini 3.1 Flash Lite is $0.25 per million tokens, so Gemini 3.1 Flash Lite is cheaper on this metric. ERNIE X1.1 is $4.00 and Gemini 3.1 Flash Lite is $1.50 per million tokens, so Gemini 3.1 Flash Lite is cheaper on this metric.

Which has a larger context window, ERNIE X1.1 or Gemini 3.1 Flash Lite?+

Gemini 3.1 Flash Lite has the larger sourced context window. ERNIE X1.1 supports 66K and Gemini 3.1 Flash Lite supports 1,049K.

Which performs better in benchmarks, ERNIE X1.1 or Gemini 3.1 Flash Lite?+

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

Can ERNIE X1.1 or Gemini 3.1 Flash Lite be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. ERNIE X1.1 is not marked open weight; Gemini 3.1 Flash Lite is not marked open weight.

Can ERNIE X1.1 and Gemini 3.1 Flash Lite understand images?+

ERNIE X1.1 is not documented with image input; Gemini 3.1 Flash Lite is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, ERNIE X1.1 or Gemini 3.1 Flash Lite?+

Neither has a larger sourced maximum output. ERNIE X1.1 is 66K and Gemini 3.1 Flash Lite is 66K.

Do ERNIE X1.1 and Gemini 3.1 Flash Lite support reasoning and tool use?+

ERNIE X1.1: reasoning and tool calling. Gemini 3.1 Flash Lite: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, ERNIE X1.1 or Gemini 3.1 Flash Lite?+

ERNIE X1.1 has 1 sourced provider route; Gemini 3.1 Flash Lite has 2, so Gemini 3.1 Flash Lite has broader tracked availability.

Which offers better value, ERNIE X1.1 or Gemini 3.1 Flash Lite?+

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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