Gemini 2.5 Flash Lite vs Llama 4 Scout 17B 16E
At a Glance
| Compare |  Gemini 2.5 Flash LiteGoogle DeepMind | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.10Google AI ↗ · Aug 29, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $0.40Google AI ↗ · Aug 29, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,049K | 10,000K |
| Model facts checked | Aug 29, 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 2.5 Flash-Lite | Llama-4-Scout-17B-16E |
|---|---|---|
| Developer | Google DeepMind | Meta |
| Family | Gemini 2 5 | Llama 4 Scout 17b 16e |
| Model | Gemini 2.5 Flash-Lite | Llama-4-Scout-17B-16E |
| Version | Gemini 2.5 Flash-Lite | Llama-4-Scout-17B-16E |
| Lifecycle | active | active |
| Released | Unknown | 2025-04-05 |
| Knowledge cutoff | 2025-01-01 | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,049K | 10,000K |
| Total parameters | Unknown | 108.6B |
| Active parameters | Unknown | 17B |
| License | Unknown | other |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, tools |
Gemini 2.5 Flash Lite Capabilities
Llama 4 Scout 17B 16E Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini 2.5 Flash Lite vs Llama 4 Scout 17B 16E FAQs
Is Gemini 2.5 Flash Lite or Llama 4 Scout 17B 16E better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 2.5 Flash Lite and Llama 4 Scout 17B 16E, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 2.5 Flash Lite or Llama 4 Scout 17B 16E?+
Only Gemini 2.5 Flash Lite has a directly sourced input price: $0.10 per million tokens. Only Gemini 2.5 Flash Lite has a directly sourced output price: $0.40 per million tokens.
Which has a larger context window, Gemini 2.5 Flash Lite or Llama 4 Scout 17B 16E?+
Llama 4 Scout 17B 16E has the larger sourced context window. Gemini 2.5 Flash Lite supports 1,049K and Llama 4 Scout 17B 16E supports 10,000K.
Which performs better in benchmarks, Gemini 2.5 Flash Lite or Llama 4 Scout 17B 16E?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Gemini 2.5 Flash Lite or Llama 4 Scout 17B 16E be self-hosted?+
Llama 4 Scout 17B 16E is the only model in this pair currently marked as self-hostable. Gemini 2.5 Flash Lite is not marked open weight; Llama 4 Scout 17B 16E is open weight.
Can Gemini 2.5 Flash Lite and Llama 4 Scout 17B 16E understand images?+
Gemini 2.5 Flash Lite is documented with image input; Llama 4 Scout 17B 16E is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 2.5 Flash Lite or Llama 4 Scout 17B 16E?+
Neither has a larger sourced maximum output. Gemini 2.5 Flash Lite is 66K and Llama 4 Scout 17B 16E is —.
Do Gemini 2.5 Flash Lite and Llama 4 Scout 17B 16E support reasoning and tool use?+
Gemini 2.5 Flash Lite: reasoning, tool calling, and image input. Llama 4 Scout 17B 16E: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini 2.5 Flash Lite or Llama 4 Scout 17B 16E?+
Gemini 2.5 Flash Lite has 2 sourced provider routes; Llama 4 Scout 17B 16E has 1, so Gemini 2.5 Flash Lite has broader tracked availability.
Which offers better value, Gemini 2.5 Flash Lite or Llama 4 Scout 17B 16E?+
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.