Seed 1.8 vs Llama-4-Scout-17B-16E-Instruct

Benchmark Performance

Available Benchmarks

No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.
FieldAt a Glance
ByteDance Seed · activeSeed 1.8Verified Sep 3, 2026
Meta · activeLlama-4-Scout-17B-16E-InstructVerified Aug 28, 2026

Technical Differences

Side-by-Side Facts

Indexable
FieldSeed 1.8Llama-4-Scout-17B-16E-Instruct
DeveloperByteDance SeedMeta
FamilySeed 1 8Llama 4 Scout 17b 16e Instruct
ModelSeed 1.8Llama-4-Scout-17B-16E-Instruct
VersionSeed 1.8Llama-4-Scout-17B-16E-Instruct
Lifecycleactiveactive
ReleasedUnknown2025-04-05
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText, Image
Output modalitiesTextText
Context windowUnknown10,000,000
Total parametersUnknown108,641,793,536
Active parametersUnknown17,000,000,000
LicenseUnknownother
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessUnknownDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitieschat, generation, reasoning, toolschat, generation, tools

11 comparable fields · 7 material differences · Pair passes the primary-source comparison gate

Seed 1.8 Capabilities

chatgenerationreasoningtools
Input price
Output price
Serving providers0
Canonical IDbytedance-seed/seed-1.8

Llama-4-Scout-17B-16E-Instruct Capabilities

chatgenerationtools
Input price$0.10
Output price$0.30
Serving providers4
Canonical IDmeta-llama/Llama-4-Scout-17B-16E-Instruct

Internal Comparison Graph

Related Comparisons

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Primary Evidence

Sources and Freshness

Questions

Seed 1.8 vs Llama-4-Scout-17B-16E-Instruct FAQs

Is Seed 1.8 or Llama-4-Scout-17B-16E-Instruct better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Seed 1.8 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, Seed 1.8 or Llama-4-Scout-17B-16E-Instruct?+

Only Llama-4-Scout-17B-16E-Instruct has a directly sourced input price: $0.10 per million tokens. Only Llama-4-Scout-17B-16E-Instruct has a directly sourced output price: $0.30 per million tokens.

Which has a larger context window, Seed 1.8 or Llama-4-Scout-17B-16E-Instruct?+

Neither model has a larger sourced context window in this comparison. Seed 1.8 is — and Llama-4-Scout-17B-16E-Instruct is 10,000,000.

Which performs better in benchmarks, Seed 1.8 or Llama-4-Scout-17B-16E-Instruct?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Seed 1.8 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. Seed 1.8 is not marked open weight; Llama-4-Scout-17B-16E-Instruct is open weight.

Can Seed 1.8 and Llama-4-Scout-17B-16E-Instruct understand images?+

Seed 1.8 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, Seed 1.8 or Llama-4-Scout-17B-16E-Instruct?+

Neither has a larger sourced maximum output. Seed 1.8 is — and Llama-4-Scout-17B-16E-Instruct is —.

Do Seed 1.8 and Llama-4-Scout-17B-16E-Instruct support reasoning and tool use?+

Seed 1.8: 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, Seed 1.8 or Llama-4-Scout-17B-16E-Instruct?+

Seed 1.8 has 0 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, Seed 1.8 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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