Llama 3.1 8B Instruct vs Llama 4 Scout 17B 16E

At a Glance

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Pricing and Limits
Input priceFrom · USD / 1M tokens$0.050Openrouter · Sep 22, 2026Not reported
Output priceFrom · USD / 1M tokens$0.080Openrouter · Sep 22, 2026Not reported
Context windowMaximum documented tokens131K10,000K
Model facts checkedAug 28, 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

FieldLlama-3.1-8B-InstructLlama-4-Scout-17B-16E
DeveloperMetaMeta
FamilyLlama 3 1 8b InstructLlama 4 Scout 17b 16e
ModelLlama-3.1-8B-InstructLlama-4-Scout-17B-16E
VersionLlama-3.1-8B-InstructLlama-4-Scout-17B-16E
Lifecycleactiveactive
Released2024-07-232025-04-05
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image
Output modalitiesTextText
Context window131K10,000K
Total parameters8B108.6B
Active parametersUnknown17B
Licensellama3.1other
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessHugging Face (Standard), Openrouter (Standard)Together Ai (Standard)
Capabilitieschat, generation, toolschat, generation, tools

Llama 3.1 8B Instruct Capabilities

chatgenerationtools
Serving providers2
Canonical IDmeta-llama/Llama-3.1-8B-Instruct

Llama 4 Scout 17B 16E Capabilities

chatgenerationtools
Serving providers1
Canonical IDmeta-llama/Llama-4-Scout-17B-16E

Primary Evidence

Sources and Freshness

Questions

Llama 3.1 8B Instruct vs Llama 4 Scout 17B 16E FAQs

Is Llama 3.1 8B Instruct or Llama 4 Scout 17B 16E better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 8B Instruct and Llama 4 Scout 17B 16E, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Llama 3.1 8B Instruct or Llama 4 Scout 17B 16E?+

Only Llama 3.1 8B Instruct has a directly sourced input price: $0.050 per million tokens. Only Llama 3.1 8B Instruct has a directly sourced output price: $0.080 per million tokens.

Which has a larger context window, Llama 3.1 8B Instruct or Llama 4 Scout 17B 16E?+

Llama 4 Scout 17B 16E has the larger sourced context window. Llama 3.1 8B Instruct supports 131K and Llama 4 Scout 17B 16E supports 10,000K.

Which performs better in benchmarks, Llama 3.1 8B Instruct or Llama 4 Scout 17B 16E?+

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

Can Llama 3.1 8B Instruct or Llama 4 Scout 17B 16E be self-hosted?+

Both models have the same recorded self-hosting status: supported. Llama 3.1 8B Instruct is open weight; Llama 4 Scout 17B 16E is open weight.

Can Llama 3.1 8B Instruct and Llama 4 Scout 17B 16E understand images?+

Llama 3.1 8B Instruct is not documented with image input; Llama 4 Scout 17B 16E is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Llama 3.1 8B Instruct or Llama 4 Scout 17B 16E?+

Neither has a larger sourced maximum output. Llama 3.1 8B Instruct is — and Llama 4 Scout 17B 16E is —.

Do Llama 3.1 8B Instruct and Llama 4 Scout 17B 16E support reasoning and tool use?+

Llama 3.1 8B Instruct: tool calling. Llama 4 Scout 17B 16E: tool calling and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Llama 3.1 8B Instruct or Llama 4 Scout 17B 16E?+

Llama 3.1 8B Instruct has 2 sourced provider routes; Llama 4 Scout 17B 16E has 1, so Llama 3.1 8B Instruct has broader tracked availability.

Which offers better value, Llama 3.1 8B Instruct or Llama 4 Scout 17B 16E?+

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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