Gemini 3.8 Flash Cyber vs Llama 3.1 405B Instruct

At a Glance

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Gemini 3.8 Flash CyberGoogle DeepMind
Pricing and Limits
Context windowMaximum documented tokensNot reported131K
Model facts checkedSep 2, 2026View model evidence →Aug 28, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldGemini 3.8 Flash CyberLlama-3.1-405B-Instruct
DeveloperGoogle DeepMindMeta
FamilyGemini 3Llama 3 1 405b Instruct
ModelGemini 3.8 Flash CyberLlama-3.1-405B-Instruct
VersionGemini 3.8 Flash CyberLlama-3.1-405B-Instruct
Lifecycleactiveactive
Released2026-09-022024-07-23
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesTextText
Context windowUnknown131K
Total parametersUnknown405.9B
Active parametersUnknownUnknown
LicenseUnknownllama3.1
Open weightsNoYes
API availableNoYes
Self-hostableNoYes
Provider accessUnknownTogether Ai (Standard)
Capabilitiesautomated-patching, cybersecurity, reasoning, vulnerability-detectionchat, generation, tools

Gemini 3.8 Flash Cyber Capabilities

automated-patchingcybersecurityreasoningvulnerability-detection
Serving providers0
Canonical IDgoogle-deepmind/gemini-3.8-flash-cyber

Llama 3.1 405B Instruct Capabilities

chatgenerationtools
Serving providers1
Canonical IDmeta-llama/Llama-3.1-405B-Instruct

Primary Evidence

Sources and Freshness

Questions

Gemini 3.8 Flash Cyber vs Llama 3.1 405B Instruct FAQs

Is Gemini 3.8 Flash Cyber or Llama 3.1 405B Instruct better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.8 Flash Cyber and Llama 3.1 405B Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Gemini 3.8 Flash Cyber or Llama 3.1 405B Instruct?+

Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.

Which has a larger context window, Gemini 3.8 Flash Cyber or Llama 3.1 405B Instruct?+

Neither model has a larger sourced context window in this comparison. Gemini 3.8 Flash Cyber is — and Llama 3.1 405B Instruct is 131K.

Which performs better in benchmarks, Gemini 3.8 Flash Cyber or Llama 3.1 405B Instruct?+

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

Can Gemini 3.8 Flash Cyber or Llama 3.1 405B Instruct be self-hosted?+

Llama 3.1 405B Instruct is the only model in this pair currently marked as self-hostable. Gemini 3.8 Flash Cyber is not marked open weight; Llama 3.1 405B Instruct is open weight.

Can Gemini 3.8 Flash Cyber and Llama 3.1 405B Instruct understand images?+

Gemini 3.8 Flash Cyber is not documented with image input; Llama 3.1 405B Instruct is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemini 3.8 Flash Cyber or Llama 3.1 405B Instruct?+

Neither has a larger sourced maximum output. Gemini 3.8 Flash Cyber is — and Llama 3.1 405B Instruct is —.

Do Gemini 3.8 Flash Cyber and Llama 3.1 405B Instruct support reasoning and tool use?+

Gemini 3.8 Flash Cyber: reasoning. Llama 3.1 405B Instruct: tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, Gemini 3.8 Flash Cyber or Llama 3.1 405B Instruct?+

Gemini 3.8 Flash Cyber has 0 sourced provider routes; Llama 3.1 405B Instruct has 1, so Llama 3.1 405B Instruct has broader tracked availability.

Which offers better value, Gemini 3.8 Flash Cyber or Llama 3.1 405B 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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