Llama 4 Scout 17B 16E vs Sonar Reasoning Pro

At a Glance

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Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$2.00Openrouter · Sep 22, 2026
Output priceFrom · USD / 1M tokensNot reported$8.00Openrouter · Sep 22, 2026
Context windowMaximum documented tokens10,000K128K
Model facts checkedAug 28, 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

FieldLlama-4-Scout-17B-16ESonar Reasoning Pro
DeveloperMetaPerplexity
FamilyLlama 4 Scout 17b 16eSonar
ModelLlama-4-Scout-17B-16ESonar Reasoning Pro
VersionLlama-4-Scout-17B-16ESonar Reasoning Pro
Lifecycleactiveactive
Released2025-04-05Unknown
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText
Output modalitiesTextText
Context window10,000K128K
Total parameters108.6BUnknown
Active parameters17BUnknown
LicenseotherUnknown
Open weightsYesNo
API availableYesYes
Self-hostableYesNo
Provider accessTogether Ai (Standard)Openrouter (Standard), Perplexity (Standard)
Capabilitieschat, generation, toolschat, citations, reasoning, search

Llama 4 Scout 17B 16E Capabilities

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

Sonar Reasoning Pro Capabilities

chatcitationsreasoningsearch
Serving providers2
Canonical IDperplexity/sonar-reasoning-pro

Primary Evidence

Sources and Freshness

Questions

Llama 4 Scout 17B 16E vs Sonar Reasoning Pro FAQs

Is Llama 4 Scout 17B 16E or Sonar Reasoning Pro better for coding?+

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

Which is cheaper, Llama 4 Scout 17B 16E or Sonar Reasoning Pro?+

Only Sonar Reasoning Pro has a directly sourced input price: $2.00 per million tokens. Only Sonar Reasoning Pro has a directly sourced output price: $8.00 per million tokens.

Which has a larger context window, Llama 4 Scout 17B 16E or Sonar Reasoning Pro?+

Llama 4 Scout 17B 16E has the larger sourced context window. Llama 4 Scout 17B 16E supports 10,000K and Sonar Reasoning Pro supports 128K.

Which performs better in benchmarks, Llama 4 Scout 17B 16E or Sonar Reasoning Pro?+

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

Can Llama 4 Scout 17B 16E or Sonar Reasoning Pro be self-hosted?+

Llama 4 Scout 17B 16E is the only model in this pair currently marked as self-hostable. Llama 4 Scout 17B 16E is open weight; Sonar Reasoning Pro is not marked open weight.

Can Llama 4 Scout 17B 16E and Sonar Reasoning Pro understand images?+

Llama 4 Scout 17B 16E is documented with image input; Sonar Reasoning Pro is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Llama 4 Scout 17B 16E or Sonar Reasoning Pro?+

Neither has a larger sourced maximum output. Llama 4 Scout 17B 16E is — and Sonar Reasoning Pro is —.

Do Llama 4 Scout 17B 16E and Sonar Reasoning Pro support reasoning and tool use?+

Llama 4 Scout 17B 16E: tool calling and image input. Sonar Reasoning Pro: reasoning. Feature support does not establish relative quality.

Which is available from more inference providers, Llama 4 Scout 17B 16E or Sonar Reasoning Pro?+

Llama 4 Scout 17B 16E has 1 sourced provider route; Sonar Reasoning Pro has 2, so Sonar Reasoning Pro has broader tracked availability.

Which offers better value, Llama 4 Scout 17B 16E or Sonar Reasoning Pro?+

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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