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Model Comparison

Llama 4 Scout vs ParetoWhich Is Better in 2026?

Meta: Llama 4 Scout logoMeta•
Unbiased

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

Llama 4 Scout (by Meta) and Pareto (by Unbiased) 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.

Llama 4 Scout is about 25.0× cheaper on a blended 3:1 input/output basis ($0.15 vs $3.75 per 1M tokens).

Llama 4 Scout has the larger context window (1,310,720 tokens vs 262,144 tokens).

Choose Llama 4 Scout if…

  • • lower blended API cost matters for your workload
  • • you work with longer documents, transcripts, or codebases

Choose Pareto if…

  • • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage

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.

Chat with both models
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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 Pareto benchmark results
BenchmarkLlama 4 ScoutPareto
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

——
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

9.1%
SWE-bench
—
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

28
artificialanalysis.ai
—

Benchmark sources

Reviewed evidence last updated 2026-08-09.

Pricing, capabilities, and model facts

Llama 4 Scout and Pareto model facts
FeatureLlama 4 ScoutPareto
Context & model facts
DeveloperMetaUnbiased
API providerMetaPareto
Input context1,310,720 tokens262,144 tokens
Maximum output16,384 tokens131,072 tokens
Released——
Added to WritingmateApr 5, 2025Sep 17, 2026
LicenseNot availableNot available
Knowledge cutoff2024-08-31—
Capabilities
InputsText, ImageText, Image
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningNoNo
VisionYesYes
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$0.10$2.50
Output (per 1M tokens)$0.30$7.50
Blended 3:1 input/output$0.15$3.75
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 Sep 17, 2026.

Why Pay for Multiple Subscriptions?

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

Subscription-plan access for Llama 4 Scout and Pareto
PlanPriceLlama 4 ScoutParetoAI 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
View Plans & Pricing

Llama 4 Scout vs Pareto FAQ

Which is better, Llama 4 Scout or Pareto?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Llama 4 Scout is about 25.0× cheaper on a blended 3:1 input/output basis ($0.15 vs $3.75 per 1M tokens). Llama 4 Scout has the larger context window (1,310,720 tokens vs 262,144 tokens).

Which model is cheaper to use through an API?

Llama 4 Scout is about 25.0× cheaper on a blended 3:1 input/output basis ($0.15 vs $3.75 per 1M tokens).

Which model supports more context?

Llama 4 Scout has the larger context window (1,310,720 tokens vs 262,144 tokens).

Can I switch between Llama 4 Scout and Pareto?

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.