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Llama 4 Scout vs Seed-2.0-Mini

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-2.0-Mini.

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-2.0-Mini

Seed-2.0-mini targets latency-sensitive, high-concurrency, and cost-sensitive scenarios, emphasizing fast response and flexible inference deployment. It delivers performance comparable to ByteDance-Seed-1.6, supports 256k context, four reasoning effort modes (minimal/low/medium/high), multimodal understanding,...

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Technical Specifications

SpecificationLlama 4 ScoutSeed-2.0-Mini
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 Date2025-04-052026-02-26

API Pricing Comparison

Input Price per Million Tokens

Llama 4 Scout

$0.10

Seed-2.0-Mini

$0.10

Output Price per Million Tokens

Llama 4 Scout

$0.30

Seed-2.0-Mini

$0.40

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-2.0-Mini
HumanEvalPython coding & logic synthesis
8950.0%vsN/A
Llama 4 Scout
Seed-2.0-Mini
MATHComplex mathematical problem solving
8100.0%vsN/A
Llama 4 Scout
Seed-2.0-Mini
GPQAGraduate-level expert reasoning
6680.0%vsN/A
Llama 4 Scout
Seed-2.0-Mini
HellaSwagCommonsense reasoning and inference
9450.0%vsN/A
Llama 4 Scout
Seed-2.0-Mini
MT-BenchMulti-turn conversation flow quality
910.0%vsN/A
Llama 4 Scout
Seed-2.0-Mini

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-2.0-Mini Quirks & Gotchas

No developer gotchas reported.