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Model Comparison

Llama 4 Scout vs Claude Sonnet 4Which Is Better in 2026?

Llama 4 Scout vs Claude Sonnet 4: which should you choose in 2026?

Llama 4 Scout (by Meta) and Claude Sonnet 4 (by Anthropic) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.

There are not enough shared, protocol-compatible benchmark results to declare a performance leader.

At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.

Llama 4 Scout has the larger context window (1,310,720 tokens vs 1,000,000 tokens).

Choose Llama 4 Scout if…

  • you work with longer documents, transcripts, or codebases

Choose Claude Sonnet 4 if…

  • you need a model with explicit reasoning support

The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.

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Performance benchmarks

Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.

Llama 4 Scout and Claude Sonnet 4 benchmark results
BenchmarkLlama 4 ScoutClaude Sonnet 4
AutomationBench-AA

A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework.

Guardrail-aware score

BrowseComp

A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing.

Accuracy

OSWorld-Verified

A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state.

Mean task reward

41.4%
OSWorld
SWE-Bench Verified

A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests.

Resolved

Protocols differ or are incompletely disclosed; not counted as a controlled win

Terminal-Bench 2.1

Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol.

Mean task success

AutoBench

A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench.

Weighted peer-assessment score

GPQA Diamond

The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry.

Accuracy

Humanity's Last Exam

A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged.

Accuracy

Artificial Analysis Intelligence Index

Comprehensive comparison of AI models across intelligence, price, speed, and latency

Intelligence Index

Pricing, capabilities, and model facts

Llama 4 Scout and Claude Sonnet 4 model facts
FeatureLlama 4 ScoutClaude Sonnet 4
Context & model facts
DeveloperMetaAnthropic
API providerMetaAnthropic
Input context1,310,720 tokens1,000,000 tokens
Maximum output16,384 tokens64,000 tokens
Released
Added to WritingmateApr 5, 2025May 22, 2025
LicenseNot availableNot available
Knowledge cutoff2024-08-312025-01-31
Capabilities
InputsText, ImageImage, Text, File
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningNoYes
VisionYesYes
Image GenerationNoNo
Video GenerationNoNo
API pricing
Base input (per 1M tokens)$0.10$3.00
Base output (per 1M tokens)$0.30$15.00
Higher-context pricing tiersBase rate only
  • 200,000 prompt tokens$6.00 input · $22.50 output per 1M
Price-comparison caveatAt least one model changes token rates above a prompt-token threshold. Base rates are shown, but an unconditional blended price ratio would not be like-for-like.
API performance
p95 latencyNot measuredNot measured
Output throughputNot measuredNot measured
Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table.

Data sources

  • Writingmate model catalog (pricing, limits, and availability)

Catalog data last updated May 22, 2025.

Why Pay for Multiple Subscriptions?

Comparing Llama 4 Scout from Meta with Claude Sonnet 4 from Anthropic? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Llama 4 Scout and Claude Sonnet 4
PlanPriceLlama 4 ScoutClaude Sonnet 4AI ImagesAI Video
Writingmate Pro
Most popular
$20/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1
Writingmate Ultimate
Power users
$60/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1

Llama 4 Scout vs Claude Sonnet 4 FAQ

Which is better, Llama 4 Scout or Claude Sonnet 4?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison. Llama 4 Scout has the larger context window (1,310,720 tokens vs 1,000,000 tokens).

Which model is cheaper to use through an API?

At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.

Which model supports more context?

Llama 4 Scout has the larger context window (1,310,720 tokens vs 1,000,000 tokens).

Can I switch between Llama 4 Scout and Claude Sonnet 4?

Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.