Gemini 3.5 Flash Lite vs Llama 4 Scout 17B 16E Instruct

At a Glance

Compare
Gemini 3.5 Flash LiteGoogle DeepMind
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5#39 of 4617.6 score · 3/3 sources · completeUnrankedNot in the 46-model eligible cohort
CostLower is better · Published-token output estimate#8 of 44$0.029 per LiveBench caseUnrankedNot in the 44-model eligible cohort
EfficiencyHigher is better · MM Efficiency v1.5#32 of 3846.2 score · 3/3 sources · completeUnrankedNot in the 38-model eligible cohort
Pricing and Limits
Input priceFrom · USD / 1M tokens$0.30Google AI · Aug 29, 2026$0.10Deepinfra · Sep 21, 2026
Output priceFrom · USD / 1M tokens$2.50Google AI · Aug 29, 2026$0.30Deepinfra · Sep 21, 2026
Context windowMaximum documented tokens1,049K10,000K
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 →
BenchmarkGemini 3.5 Flash-LiteLlama-4-Scout-17B-16E-Instruct
LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader1,435.54100% of row best · rating · gemini-3.5-flash-lite; 95% CI [1430.59505493, 1440.49024696]; votes 26165; rank 791,279.2989% of row best · rating · llama-4-scout-17b-16e-instruct; 95% CI [1274.57681953, 1284.00026285]; votes 29740; rank 259
LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader1,268.42100% of row best · rating · gemini-3.5-flash-lite; 95% CI [1258.88097193, 1277.96729164]; votes 5254; rank 371,117.8088% of row best · rating · llama-4-scout-17b-16e-instruct; 95% CI [1108.35603674, 1127.24229078]; votes 6466; rank 114
ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified73.42100% of row best · points · Gemini 3.5 Flash-Lite · 1,966 output tokens / case47.3264% of row best · points · Llama 4 Scout · 696 output tokens / case
Overall ResultCounted from the protocol-matched rows above2 benchmark winsNo overall winner0 benchmark winsNo overall winner

Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.

Side-by-Side Facts

FieldGemini 3.5 Flash-LiteLlama-4-Scout-17B-16E-Instruct
DeveloperGoogle DeepMindMeta
FamilyGemini 3Llama 4 Scout 17b 16e Instruct
ModelGemini 3.5 Flash-LiteLlama-4-Scout-17B-16E-Instruct
VersionGemini 3.5 Flash-LiteLlama-4-Scout-17B-16E-Instruct
Lifecycleactiveactive
ReleasedUnknown2025-04-05
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Video, Audio, DocumentText, Image
Output modalitiesTextText
Context window1,049K10,000K
Total parametersUnknown108.6B
Active parametersUnknown17B
LicenseUnknownother
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessGoogle AI (Standard), Google Gemini (Standard)Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitieschat, generation, reasoning, toolschat, generation, tools

Gemini 3.5 Flash Lite Capabilities

chatgenerationreasoningtools
Serving providers2
Canonical IDgoogle-deepmind/gemini-3.5-flash-lite

Llama 4 Scout 17B 16E Instruct Capabilities

chatgenerationtools
Serving providers4
Canonical IDmeta-llama/Llama-4-Scout-17B-16E-Instruct

Primary Evidence

Sources and Freshness

Questions

Gemini 3.5 Flash Lite vs Llama 4 Scout 17B 16E Instruct FAQs

Is Gemini 3.5 Flash Lite or Llama 4 Scout 17B 16E Instruct better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.5 Flash Lite and Llama 4 Scout 17B 16E Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Gemini 3.5 Flash Lite or Llama 4 Scout 17B 16E Instruct?+

Gemini 3.5 Flash Lite is $0.30 and Llama 4 Scout 17B 16E Instruct is $0.10 per million tokens, so Llama 4 Scout 17B 16E Instruct is cheaper on this metric. Gemini 3.5 Flash Lite is $2.50 and Llama 4 Scout 17B 16E Instruct is $0.30 per million tokens, so Llama 4 Scout 17B 16E Instruct is cheaper on this metric.

Which has a larger context window, Gemini 3.5 Flash Lite or Llama 4 Scout 17B 16E Instruct?+

Llama 4 Scout 17B 16E Instruct has the larger sourced context window. Gemini 3.5 Flash Lite supports 1,049K and Llama 4 Scout 17B 16E Instruct supports 10,000K.

Which performs better in benchmarks, Gemini 3.5 Flash Lite or Llama 4 Scout 17B 16E Instruct?+

There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.

Can Gemini 3.5 Flash Lite or Llama 4 Scout 17B 16E Instruct be self-hosted?+

Llama 4 Scout 17B 16E Instruct is the only model in this pair currently marked as self-hostable. Gemini 3.5 Flash Lite is not marked open weight; Llama 4 Scout 17B 16E Instruct is open weight.

Can Gemini 3.5 Flash Lite and Llama 4 Scout 17B 16E Instruct understand images?+

Gemini 3.5 Flash Lite is documented with image input; Llama 4 Scout 17B 16E Instruct is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemini 3.5 Flash Lite or Llama 4 Scout 17B 16E Instruct?+

Neither has a larger sourced maximum output. Gemini 3.5 Flash Lite is 66K and Llama 4 Scout 17B 16E Instruct is —.

Do Gemini 3.5 Flash Lite and Llama 4 Scout 17B 16E Instruct support reasoning and tool use?+

Gemini 3.5 Flash Lite: reasoning, tool calling, and image input. Llama 4 Scout 17B 16E Instruct: tool calling and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Gemini 3.5 Flash Lite or Llama 4 Scout 17B 16E Instruct?+

Gemini 3.5 Flash Lite has 2 sourced provider routes; Llama 4 Scout 17B 16E Instruct has 4, so Llama 4 Scout 17B 16E Instruct has broader tracked availability.

Which offers better value, Gemini 3.5 Flash Lite or Llama 4 Scout 17B 16E 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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