Claude Sonnet 4.6 vs Llama 4 Scout 17B 16E Instruct

At a Glance

Compare
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5#24 of 4660.3 score · 3/3 sources · completeUnrankedNot in the 46-model eligible cohort
CostLower is better · Published-token output estimate#31 of 44$0.198 per LiveBench caseUnrankedNot in the 44-model eligible cohort
EfficiencyHigher is better · MM Efficiency v1.5#31 of 3847.3 score · 3/3 sources · completeUnrankedNot in the 38-model eligible cohort
Pricing and Limits
Input priceFrom · USD / 1M tokens$3.00Anthropic · Sep 3, 2026$0.10Deepinfra · Sep 21, 2026
Output priceFrom · USD / 1M tokens$15.00Anthropic · Sep 3, 2026$0.30Deepinfra · Sep 21, 2026
Context windowMaximum documented tokens1,000K10,000K
Model facts checkedSep 3, 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 →
BenchmarkClaude Sonnet 4.6Llama-4-Scout-17B-16E-Instruct
LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader1,458.26100% of row best · rating · claude-sonnet-4-6; 95% CI [1454.67519650, 1461.85096774]; votes 66208; rank 351,279.2988% 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 · leader1,282.72100% of row best · rating · claude-sonnet-4-6; 95% CI [1276.36460283, 1289.07266013]; votes 25552; rank 271,117.8087% 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 · unverified83.63100% of row best · points · Claude Sonnet 4.6 · 18,675 output tokens / case47.3257% 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

FieldClaude Sonnet 4.6Llama-4-Scout-17B-16E-Instruct
DeveloperAnthropicMeta
FamilyClaude 4Llama 4 Scout 17b 16e Instruct
ModelClaude Sonnet 4.6Llama-4-Scout-17B-16E-Instruct
VersionClaude Sonnet 4.6Llama-4-Scout-17B-16E-Instruct
Lifecycleactiveactive
Released2026-02-172025-04-05
Knowledge cutoff2025-08-01Unknown
Input modalitiesText, ImageText, Image
Output modalitiesTextText
Context window1,000K10,000K
Total parametersUnknown108.6B
Active parametersUnknown17B
LicenseUnknownother
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessAnthropic (Standard), Deepinfra (Standard)Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitieschat, generation, reasoning, structured_outputs, toolschat, generation, tools

Claude Sonnet 4.6 Capabilities

chatgenerationreasoningstructured outputstools
Serving providers2
Canonical IDanthropic/claude-sonnet-4-6

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

Claude Sonnet 4.6 vs Llama 4 Scout 17B 16E Instruct FAQs

Is Claude Sonnet 4.6 or Llama 4 Scout 17B 16E Instruct better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Claude Sonnet 4.6 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, Claude Sonnet 4.6 or Llama 4 Scout 17B 16E Instruct?+

Claude Sonnet 4.6 is $3.00 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. Claude Sonnet 4.6 is $15.00 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, Claude Sonnet 4.6 or Llama 4 Scout 17B 16E Instruct?+

Llama 4 Scout 17B 16E Instruct has the larger sourced context window. Claude Sonnet 4.6 supports 1,000K and Llama 4 Scout 17B 16E Instruct supports 10,000K.

Which performs better in benchmarks, Claude Sonnet 4.6 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 Claude Sonnet 4.6 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. Claude Sonnet 4.6 is not marked open weight; Llama 4 Scout 17B 16E Instruct is open weight.

Can Claude Sonnet 4.6 and Llama 4 Scout 17B 16E Instruct understand images?+

Claude Sonnet 4.6 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, Claude Sonnet 4.6 or Llama 4 Scout 17B 16E Instruct?+

Neither has a larger sourced maximum output. Claude Sonnet 4.6 is 128K and Llama 4 Scout 17B 16E Instruct is —.

Do Claude Sonnet 4.6 and Llama 4 Scout 17B 16E Instruct support reasoning and tool use?+

Claude Sonnet 4.6: 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, Claude Sonnet 4.6 or Llama 4 Scout 17B 16E Instruct?+

Claude Sonnet 4.6 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, Claude Sonnet 4.6 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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