Gemini 3.1 Pro vs Llama 4 Scout 17B 16E Instruct
At a Glance
| Compare |  Gemini 3.1 ProGoogle DeepMind | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #9 of 4679.7 score · 3/3 sources · complete | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #27 of 44$0.161 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #9 of 3859.2 score · 3/3 sources · complete | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $2.00Google AI ↗ · Aug 29, 2026 | $0.10Deepinfra ↗ · Sep 21, 2026 |
| Output priceFrom · USD / 1M tokens | $12.00Google AI ↗ · Aug 29, 2026 | $0.30Deepinfra ↗ · Sep 21, 2026 |
| 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
| Benchmark | Gemini 3.1 Pro | Llama-4-Scout-17B-16E-Instruct |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,480.08100% of row best · rating · gemini-3.1-pro-preview; 95% CI [1476.92899018, 1483.22190450]; votes 106951; rank 16 | 1,279.2986% of row best · rating · llama-4-scout-17b-16e-instruct; 95% CI [1274.57681953, 1284.00026285]; votes 29740; rank 259 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,295.61100% of row best · rating · gemini-3.1-pro-preview; 95% CI [1290.13797622, 1301.07924121]; votes 40691; rank 19 | 1,117.8086% of row best · rating · llama-4-scout-17b-16e-instruct; 95% CI [1108.35603674, 1127.24229078]; votes 6466; rank 114 |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 79.89100% of row best · points · Gemini 3.1 Pro (high thinking) · 10,009 output tokens / case | 47.3259% of row best · points · Llama 4 Scout · 696 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 2 benchmark winsNo overall winner | 0 benchmark winsNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | Gemini 3.1 Pro | Llama-4-Scout-17B-16E-Instruct |
|---|---|---|
| Developer | Google DeepMind | Meta |
| Family | Gemini 3 | Llama 4 Scout 17b 16e Instruct |
| Model | Gemini 3.1 Pro | Llama-4-Scout-17B-16E-Instruct |
| Version | Gemini 3.1 Pro | Llama-4-Scout-17B-16E-Instruct |
| Lifecycle | preview | active |
| Released | Unknown | 2025-04-05 |
| Knowledge cutoff | Unknown | 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) | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, tools |
Gemini 3.1 Pro Capabilities
Llama 4 Scout 17B 16E Instruct Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini 3.1 Pro vs Llama 4 Scout 17B 16E Instruct FAQs
Is Gemini 3.1 Pro or Llama 4 Scout 17B 16E Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.1 Pro 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, Gemini 3.1 Pro or Llama 4 Scout 17B 16E Instruct?+
Gemini 3.1 Pro is $2.00 and Llama 4 Scout 17B 16E Instruct is $0.10 per million tokens, so Llama 4 Scout 17B 16E Instruct is cheaper on this metric. Gemini 3.1 Pro is $12.00 and Llama 4 Scout 17B 16E Instruct is $0.30 per million tokens, so Llama 4 Scout 17B 16E Instruct is cheaper on this metric.
Which has a larger context window, Gemini 3.1 Pro or Llama 4 Scout 17B 16E Instruct?+
Llama 4 Scout 17B 16E Instruct has the larger sourced context window. Gemini 3.1 Pro supports 1,049K and Llama 4 Scout 17B 16E Instruct supports 10,000K.
Which performs better in benchmarks, Gemini 3.1 Pro or Llama 4 Scout 17B 16E Instruct?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can Gemini 3.1 Pro 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. Gemini 3.1 Pro is not marked open weight; Llama 4 Scout 17B 16E Instruct is open weight.
Can Gemini 3.1 Pro and Llama 4 Scout 17B 16E Instruct understand images?+
Gemini 3.1 Pro 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, Gemini 3.1 Pro or Llama 4 Scout 17B 16E Instruct?+
Neither has a larger sourced maximum output. Gemini 3.1 Pro is 66K and Llama 4 Scout 17B 16E Instruct is —.
Do Gemini 3.1 Pro and Llama 4 Scout 17B 16E Instruct support reasoning and tool use?+
Gemini 3.1 Pro: 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, Gemini 3.1 Pro or Llama 4 Scout 17B 16E Instruct?+
Gemini 3.1 Pro has 2 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, Gemini 3.1 Pro 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.