Gemini 3.1 Flash Lite vs Llama 3.1 70B

At a Glance

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Gemini 3.1 Flash LiteGoogle DeepMind
Pricing and Limits
Input priceFrom · USD / 1M tokens$0.25Google AI · Aug 29, 2026Not reported
Output priceFrom · USD / 1M tokens$1.50Google AI · Aug 29, 2026Not reported
Context windowMaximum documented tokens1,049K131K
Model facts checkedAug 29, 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 3.1 Flash-LiteLlama-3.1-70B
DeveloperGoogle DeepMindMeta
FamilyGemini 3Llama 3 1 70b
ModelGemini 3.1 Flash-LiteLlama-3.1-70B
VersionGemini 3.1 Flash-LiteLlama-3.1-70B
Lifecycleactiveactive
ReleasedUnknown2024-07-23
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Video, Audio, DocumentText
Output modalitiesTextText
Context window1,049K131K
Total parametersUnknown70.6B
Active parametersUnknownUnknown
LicenseUnknownllama3.1
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessGoogle AI (Standard), Google Gemini (Standard)Hugging Face (Standard)
Capabilitieschat, generation, reasoning, toolsgeneration

Gemini 3.1 Flash Lite Capabilities

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

Llama 3.1 70B Capabilities

generation
Serving providers1
Canonical IDmeta-llama/Llama-3.1-70B

Primary Evidence

Sources and Freshness

Questions

Gemini 3.1 Flash Lite vs Llama 3.1 70B FAQs

Is Gemini 3.1 Flash Lite or Llama 3.1 70B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.1 Flash Lite and Llama 3.1 70B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Gemini 3.1 Flash Lite or Llama 3.1 70B?+

Only Gemini 3.1 Flash Lite has a directly sourced input price: $0.25 per million tokens. Only Gemini 3.1 Flash Lite has a directly sourced output price: $1.50 per million tokens.

Which has a larger context window, Gemini 3.1 Flash Lite or Llama 3.1 70B?+

Gemini 3.1 Flash Lite has the larger sourced context window. Gemini 3.1 Flash Lite supports 1,049K and Llama 3.1 70B supports 131K.

Which performs better in benchmarks, Gemini 3.1 Flash Lite or Llama 3.1 70B?+

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

Can Gemini 3.1 Flash Lite or Llama 3.1 70B be self-hosted?+

Llama 3.1 70B is the only model in this pair currently marked as self-hostable. Gemini 3.1 Flash Lite is not marked open weight; Llama 3.1 70B is open weight.

Can Gemini 3.1 Flash Lite and Llama 3.1 70B understand images?+

Gemini 3.1 Flash Lite is documented with image input; Llama 3.1 70B is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemini 3.1 Flash Lite or Llama 3.1 70B?+

Neither has a larger sourced maximum output. Gemini 3.1 Flash Lite is 66K and Llama 3.1 70B is —.

Do Gemini 3.1 Flash Lite and Llama 3.1 70B support reasoning and tool use?+

Gemini 3.1 Flash Lite: reasoning, tool calling, and image input. Llama 3.1 70B: none of these features are definitively sourced. Feature support does not establish relative quality.

Which is available from more inference providers, Gemini 3.1 Flash Lite or Llama 3.1 70B?+

Gemini 3.1 Flash Lite has 2 sourced provider routes; Llama 3.1 70B has 1, so Gemini 3.1 Flash Lite has broader tracked availability.

Which offers better value, Gemini 3.1 Flash Lite or Llama 3.1 70B?+

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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