Gemini 3.8 Flash Cyber vs Llama 3.1 70B Instruct
At a Glance
| Compare | Gemini 3.8 Flash CyberGoogle DeepMind | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.40Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.40Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | Not reported | 131K |
| Model facts checked | Sep 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
Side-by-Side Facts
| Field | Gemini 3.8 Flash Cyber | Llama-3.1-70B-Instruct |
|---|---|---|
| Developer | Google DeepMind | Meta |
| Family | Gemini 3 | Llama 3 1 70b Instruct |
| Model | Gemini 3.8 Flash Cyber | Llama-3.1-70B-Instruct |
| Version | Gemini 3.8 Flash Cyber | Llama-3.1-70B-Instruct |
| Lifecycle | active | active |
| Released | 2026-09-02 | 2024-07-23 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | Unknown | 131K |
| Total parameters | Unknown | 70.6B |
| Active parameters | Unknown | Unknown |
| License | Unknown | llama3.1 |
| Open weights | No | Yes |
| API available | No | Yes |
| Self-hostable | No | Yes |
| Provider access | Unknown | Openrouter (Standard) |
| Capabilities | automated-patching, cybersecurity, reasoning, vulnerability-detection | chat, generation, tools |
Gemini 3.8 Flash Cyber Capabilities
Llama 3.1 70B Instruct Capabilities
Primary Evidence
Sources and Freshness
Questions
Gemini 3.8 Flash Cyber vs Llama 3.1 70B Instruct FAQs
Is Gemini 3.8 Flash Cyber or Llama 3.1 70B 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 70B 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 70B Instruct?+
Only Llama 3.1 70B Instruct has a directly sourced input price: $0.40 per million tokens. Only Llama 3.1 70B Instruct has a directly sourced output price: $0.40 per million tokens.
Which has a larger context window, Gemini 3.8 Flash Cyber or Llama 3.1 70B Instruct?+
Neither model has a larger sourced context window in this comparison. Gemini 3.8 Flash Cyber is — and Llama 3.1 70B Instruct is 131K.
Which performs better in benchmarks, Gemini 3.8 Flash Cyber or Llama 3.1 70B 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 70B Instruct be self-hosted?+
Llama 3.1 70B 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 70B Instruct is open weight.
Can Gemini 3.8 Flash Cyber and Llama 3.1 70B Instruct understand images?+
Gemini 3.8 Flash Cyber is not documented with image input; Llama 3.1 70B 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 70B Instruct?+
Neither has a larger sourced maximum output. Gemini 3.8 Flash Cyber is — and Llama 3.1 70B Instruct is —.
Do Gemini 3.8 Flash Cyber and Llama 3.1 70B Instruct support reasoning and tool use?+
Gemini 3.8 Flash Cyber: reasoning. Llama 3.1 70B 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 70B Instruct?+
Gemini 3.8 Flash Cyber has 0 sourced provider routes; Llama 3.1 70B Instruct has 1, so Llama 3.1 70B Instruct has broader tracked availability.
Which offers better value, Gemini 3.8 Flash Cyber or Llama 3.1 70B 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.