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Llama 4 Scout vs Seed 1.6

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Llama 4 Scout and Seed 1.6.

Meta

Llama 4 Scout

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...

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ByteDance

Seed 1.6

Seed 1.6 is a general-purpose model released by the ByteDance Seed team. It incorporates multimodal capabilities and adaptive deep thinking with a 256K context window.

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Technical Specifications

SpecificationLlama 4 ScoutSeed 1.6
ProviderMetaByteDance
Context Window10,000,000 tokens262,144 tokens
Agent Suitability82/100N/A
Time to First Token (TTFT)350 msN/A
Deployment Modelself hostablemanaged api
Production Stabilitybetastable
API AvailableYesYes
Released Date2025-04-052025-12-23

API Pricing Comparison

Input Price per Million Tokens

Llama 4 Scout

$0.10

Seed 1.6

$0.25

Output Price per Million Tokens

Llama 4 Scout

$0.30

Seed 1.6

$2.00

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
8720.0%vsN/A
Llama 4 Scout
Seed 1.6
HumanEvalPython coding & logic synthesis
8950.0%vsN/A
Llama 4 Scout
Seed 1.6
MATHComplex mathematical problem solving
8100.0%vsN/A
Llama 4 Scout
Seed 1.6
GPQAGraduate-level expert reasoning
6680.0%vsN/A
Llama 4 Scout
Seed 1.6
HellaSwagCommonsense reasoning and inference
9450.0%vsN/A
Llama 4 Scout
Seed 1.6
MT-BenchMulti-turn conversation flow quality
910.0%vsN/A
Llama 4 Scout
Seed 1.6

Llama 4 Scout Quirks & Gotchas

  • โ–ธ10M context causes significant VRAM pressure โ€” recommend 4-bit quantization
  • โ–ธPrimarily designed for RAG, not agentic tool calling

Seed 1.6 Quirks & Gotchas

No developer gotchas reported.