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Meta: Meta: Llama 4 Scout

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...

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API record Report
InputT
OutputT
Input price$0.1/M
Output price$0.3/M
Context327.68K
Max output16.384K
Providers0
Inference availability

Providers

Provider-specific identifiers, limits, and listed prices per million tokens. Every row links back to the provider's own documentation.

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Specification

Capabilities

Recorded from the source catalog and provider listings.

? Reasoning Unknown
? Tool calling Unknown
? Structured output Unknown
? Attachments Unknown
✓ Vision input Yes
? Open weights Unknown
Creator
Meta
Model family
Not documented
Knowledge cutoff
Not documented
License
Not documented
Release date
Not documented
Model ID
meta-llama/llama-4-scout
Published evaluations

Benchmarks

Every result stays attached to its source, version, metric and harness. Scores from different versions are never merged, and the profile below plots each benchmark against its own population rather than on a shared scale.

Benchmark registry
Benchmark profile
Coding IndexCoding Index Agentic IndexAgentic Index IntelligenceIntelligence Index Design Arena: w…Design Arena: website Design Arena: c…Design Arena: codecategories Design Arena: g…Design Arena: gamedev Design Arena: d…Design Arena: dataviz Design Arena: u…Design Arena: uicomponent This model — Coding Index: 8.2 index (2.7th percentile) This model — Agentic Index: 0.5 index (2.6th percentile) This model — Intelligence Index: 6.5 index (4.4th percentile) This model — Design Arena: website: 762 elo (0.3th percentile) This model — Design Arena: codecategories: 804 elo (0.3th percentile) This model — Design Arena: gamedev: 796 elo (0.3th percentile) This model — Design Arena: dataviz: 909 elo (3.3th percentile) This model — Design Arena: uicomponent: 782 elo (0.3th percentile)

Each axis is this model's percentile among the 8 benchmarks it has published results for, measured against every other model with a score on that same benchmark. Percentiles are used because benchmarks do not share a scale — a 60 on one is not a 60 on another. Hover any point for the raw score.

Coding Index index Source ↗
Coding Index #200 of 205
Agentic Index index Source ↗
Agentic Index #188 of 193
Intelligence Index index Source ↗
Intelligence Index #184 of 192
Design Arena: website elo Source ↗
Design Arena: website #175 of 175
Design Arena: codecategories elo Source ↗
Design Arena: codecategories #167 of 167
Design Arena: gamedev elo Source ↗
Design Arena: gamedev #166 of 166
Design Arena: dataviz elo Source ↗
Design Arena: dataviz #160 of 165
Design Arena: uicomponent elo Source ↗
Design Arena: uicomponent #161 of 161

Each strip shows every published score for that benchmark, with this model marked. The lighter shape behind the ticks is the density of results, and the dashed line is the median.

Cost against capability

Price and performance

Listed input price plotted against Coding Index, the benchmark with the widest published coverage that this model appears in.

0 43 86 $0.01 $0.1 $1 $10 Qwen: Qwen3.7 Plus — $0.32/M, score 55.9 OpenAI: GPT-5.4 (batch) — $1.25/M, score 71.1 Qwen: Qwen3.6 Plus — $0.325/M, score 54.5 Qwen: Qwen3 Next 80B A3B Thinking — $0.15/M, score 17.4 OpenAI: GPT-5.1 (batch) — $0.625/M, score 49.4 Anthropic: Claude Haiku 4.5 (batch) — $0.5/M, score 43.9 Z.ai: GLM 4.6 — $0.43/M, score 45.8 OpenAI: GPT-4 Turbo (batch) — $5/M, score 21.5 Anthropic: Claude Sonnet 4.5 (batch) — $1.5/M, score 52.1 GPT-5 Mini — $0.25/M, score 15.6 Mistral: Ministral 3 8B 2512 (batch) — $0.075/M, score 9.7 Anthropic: Claude Sonnet 4 — $3/M, score 37.6 MiMo-V2.5-Pro — $0.435/M, score 60.2 Gemini 3.1 Pro Preview — $2/M, score 68.8 OpenAI: o3 Mini High (batch) — $0.55/M, score 16.3 DeepSeek V4 Flash 0731 — $0.05/M, score 69.1 GPT-4o (2024-05-13) — $5/M, score 24.2 GPT OSS 20B — $0.02/M, score 20.7 GPT-4 Turbo — $10/M, score 21.5 o1 — $15/M, score 39.7 Qwen: Qwen3.8 Max (0902) — $2/M, score 71.8 Gemini 3.5 Flash — $1.5/M, score 70.1 Z.ai: GLM 5.3 Flash (batch) — $0.075/M, score 71.5 Gemma 3 12B IT — $0.05/M, score 5.8 GPT-4 — $30/M, score 13.1 GPT-4.1 mini — $0.4/M, score 20.2 DeepSeek V4 Pro — $0.435/M, score 59.4 Claude Sonnet 5 — $2/M, score 71.5 Mistral: Ministral 3 8B 2512 — $0.15/M, score 9.7 Gemini 3.1 Flash Lite Preview — $0.25/M, score 34.7 OpenAI: o1 (batch) — $7.5/M, score 39.7 Anthropic: Claude Fable 5.1 (batch) — $5/M, score 81.6 Gemini 3.7 Flash — $0.75/M, score 76.1 LongCat-2.0 — $0.3/M, score 45.3 Z.ai: GLM 5.1 — $0.966/M, score 55.8 Anthropic: Claude Opus 4.8 (batch) — $2.5/M, score 74.3 Mistral: Mistral Large 3 2512 — $0.5/M, score 20.1 DeepSeek V3.2 — $0.18/M, score 44.2 Anthropic: Claude Opus 4.7 — $5/M, score 73.6 Google: Gemini 3.5 Flash (batch) — $0.75/M, score 70.1 OpenAI: GPT-5.4 Nano (batch) — $0.1/M, score 56.1 Inception: Mercury 2 — $0.25/M, score 31.1 OpenAI: GPT-4o-mini (batch) — $0.075/M, score 11.4 Qwen: Qwen3.5-9B (batch) — $0.17/M, score 28.7 Gemini 3.6 Flash — $0.75/M, score 69.2 Gemma 4 26B A4B IT — $0.042/M, score 39.3 Claude Fable 5 — $10/M, score 76.5 Gemma 4 31B IT — $0.09/M, score 43.4 Z.ai: GLM 5.3 Flash — $0.15/M, score 71.5 OpenAI: GPT-3.5 Turbo (batch) — $0.25/M, score 10.7 Gemma 3 27B IT — $0.08/M, score 10.1 MiniMax: MiniMax M3 — $0.3/M, score 58.6 DeepSeek: DeepSeek V4 Pro 0813 (batch) — $0.66/M, score 68.8 Thinking Machines: Inkling Small (batch) — $0.5/M, score 52.9 Anthropic: Claude Fable 5.1 — $10/M, score 81.6 Google: Gemma 4 31B (batch) — $0.39/M, score 43.4 Qwen: Qwen3.8 2.4T A95B (batch) — $2/M, score 71.9 Mistral: Devstral 2 2512 — $0.4/M, score 31.3 MoonshotAI: Kimi K2.7 Code (batch) — $0.95/M, score 60.8 Inkling — $1.87/M, score 52.1 OpenAI: gpt-oss-20b (batch) — $0.05/M, score 20.7 Muse Spark 1.1 — $1.25/M, score 71.3 MiMo-V2.5 — $0.14/M, score 56.8 Google: Gemini 3.5 Flash Lite (batch) — $0.15/M, score 49.3 Kimi K2 Thinking — $0.4/M, score 21.0 Mistral: Mistral Medium 3.5 (batch) — $0.75/M, score 46.9 GPT-5.6 Luna — $0.2/M, score 71.4 Qwen: Qwen3.8 Max (0803) — $2/M, score 68.9 Mistral: Mistral Medium 3.1 (batch) — $0.2/M, score 20.5 GPT-5.1 — $1.25/M, score 49.4 Qwen: Qwen3.8 27B — $0.42/M, score 68.1 Google: Gemini 3.8 Flash (batch) — $0.375/M, score 76.3 Google: Gemini 3.7 Flash (batch) — $0.375/M, score 76.1 Mistral: Mistral Small 4 (batch) — $0.075/M, score 26.6 inclusionAI: Ling 3.0 Flash VL — $0.06/M, score 57.0 Z.ai: GLM 5.3 (batch) — $0.7/M, score 74.8 DeepSeek: DeepSeek V4 Flash 0731 (batch) — $0.11/M, score 69.1 OpenAI: GPT-5.5 (batch) — $2.5/M, score 74.9 MoonshotAI: Kimi K3 (batch) — $3/M, score 76.2 Inkling Small — $0.45/M, score 52.9 Anthropic: Claude Sonnet 5 (batch) — $1/M, score 71.5 Anthropic: Claude Fable 5 (batch) — $5/M, score 76.5 Nemotron 3 Super 120B A12B — $0.2/M, score 37.7 IBM: Granite 4.2 8B — $0.06/M, score 22.4 OpenAI: GPT-5 (batch) — $0.625/M, score 37.8 Google: Gemini 2.5 Pro (batch) — $0.625/M, score 33.3 GPT OSS 120B — $0.03/M, score 30.4 OpenAI: gpt-oss-120b (batch) — $0.15/M, score 30.4 OpenAI: GPT-4.1 Mini (batch) — $0.2/M, score 20.2 OpenAI: GPT-4.1 Nano (batch) — $0.05/M, score 11.1 SpaceXAI: Grok 4.6 — $2/M, score 76.8 DeepSeek-R1 — $0.7/M, score 24.6 DeepSeek: DeepSeek V3.1 Terminus — $0.27/M, score 43.5 OpenAI: GPT-6 Astra (batch) — $5/M, score 76.9 OpenAI: GPT-5.6 Terra (batch) — $1/M, score 76.7 Gemma 3 4B IT — $0.04/M, score 2.7 DeepSeek V4 Pro 0813 — $0.442/M, score 68.8 Z.ai: GLM 5.2 (batch) — $0.7/M, score 68.8 Google: Gemini 3.6 Flash (batch) — $0.375/M, score 69.2 GPT-6 Astra — $10/M, score 76.9 Nemotron 3 Nano 30B A3B — $0.05/M, score 14.4 Z.ai: GLM 5.3 — $1.4/M, score 74.8 Anthropic: Claude Opus 4.8 — $5/M, score 74.3 SpaceXAI: Grok 4.3 — $1.25/M, score 42.2 GPT-5.6 Terra — $2/M, score 76.7 MiniMax: MiniMax M3 (batch) — $0.3/M, score 58.6 Gemini 3.8 Flash — $0.75/M, score 76.3 NVIDIA: Nemotron 3 Ultra (batch) — $0.6/M, score 49.3 MiniMax: MiniMax M2.7 — $0.3/M, score 52.6 Kimi K2.6 — $0.95/M, score 61.8 Kimi K3 — $3/M, score 76.2 Claude Opus 5 — $5/M, score 78.0 Claude Opus 5 (batch) — $2.5/M, score 78.0 SpaceXAI: Grok 4.5 — $2/M, score 72.4 Mistral: Mistral Large 3 2512 (batch) — $0.25/M, score 20.1 Anthropic: Claude Opus 4.7 (batch) — $2.5/M, score 73.6 Anthropic: Claude Sonnet 4.5 — $3/M, score 52.1 Google: Gemini 3.1 Pro Preview (batch) — $1/M, score 68.8 Mistral: Mistral Medium 3.1 — $0.4/M, score 20.5 Thinking Machines: Inkling (batch) — $1/M, score 52.1 GPT-5.5 — $5/M, score 74.9 Anthropic: Claude Sonnet 4.6 — $3/M, score 63.0 Anthropic: Claude Sonnet 4.6 (batch) — $1.5/M, score 63.0 GPT-5 — $1.25/M, score 37.8 OpenAI: GPT-5 Mini (batch) — $0.125/M, score 15.6 Gemini 2.5 Pro — $1.25/M, score 33.3 inclusionAI: Ling 3.0 Flash — $0.021/M, score 50.6 SpaceXAI: Grok 4.3 (batch) — $1/M, score 42.2 Nemotron 3 Ultra 550B A55B — $0.5/M, score 49.3 OpenAI: GPT-5.6 Luna (batch) — $0.1/M, score 71.4 OpenAI: GPT-5.4 Mini (batch) — $0.375/M, score 56.1 Qwen: Qwen3.8 2.4T A95B — $2/M, score 71.9 OpenAI: GPT-5.6 Sol (batch) — $1/M, score 77.4 GPT-5.4 mini — $0.75/M, score 56.1 Kimi K2.7 Code — $0.95/M, score 60.8 Gemini 3.5 Flash Lite — $0.3/M, score 49.3 Hy3 preview — $0.066/M, score 58.8 Muse Spark 1.2 — $1.25/M, score 72.2 DeepSeek V4 Flash — $0.15/M, score 52.0 Nemotron 3.5 Lightning 30B A3B — $0.05/M, score 26.8 GPT-5.6 Sol — $4/M, score 77.4 GPT-4.1 nano — $0.1/M, score 11.1 GPT-5.4 — $2.5/M, score 71.1 GPT-4o mini — $0.15/M, score 11.4 GPT-5.4 nano — $0.2/M, score 56.1 GPT-3.5-turbo — $0.5/M, score 10.7 Solar Pro 4 — $0.3/M, score 52.7 Kimi K2.5 — $0.3/M, score 46.8 Trinity Large Thinking — $0.25/M, score 25.8 Qwen: Qwen3 30B A3B Thinking 2507 — $0.2/M, score 12.1 Z.ai: GLM 5.2 — $0.966/M, score 68.8 Qwen: Qwen3.7 Max — $1.475/M, score 66.0 SpaceXAI: Grok Build 0.1 — $1/M, score 51.5 Qwen: Qwen3.6 35B A3B — $0.1/M, score 41.9 Qwen: Qwen3.6 27B — $0.3/M, score 53.7 inclusionAI: Ling-2.6-flash — $0.01/M, score 25.3 Mistral: Mistral Small 4 — $0.15/M, score 26.6 Kwaipilot: KAT-Coder-Pro V2 — $0.3/M, score 59.5 Qwen: Qwen3.5-9B — $0.1/M, score 28.7 Qwen: Qwen3.5-35B-A3B — $0.312/M, score 37.0 Qwen: Qwen3.5-122B-A10B — $0.26/M, score 45.7 Upstage: Solar Pro 3 — $0.15/M, score 16.2 Z.ai: GLM 4.7 — $0.4/M, score 45.3 Amazon: Nova 2 Lite — $0.3/M, score 23.0 Mistral: Ministral 3 3B 2512 — $0.1/M, score 4.8 Google: Gemma 3n 4B — $0.06/M, score 3.2 Meta: Llama 4 Maverick — $0.2/M, score 16.3 OpenAI: o3 Mini High — $1.1/M, score 16.3 Meta: Llama 3.3 70B Instruct — $0.1/M, score 11.9 Meta: Llama 3.1 8B Instruct — $0.05/M, score 5.4 Nex AGI: Nex-N2-Pro — $0.25/M, score 59.1 Mistral: Mistral Medium 3.5 — $1.5/M, score 46.9 inclusionAI: Ring-2.6-1T — $0.075/M, score 42.8 IBM: Granite 4.1 8B — $0.05/M, score 9.5 Qwen: Qwen3.5 397B A17B — $0.55/M, score 48.2 Qwen: Qwen3 Coder Next — $0.12/M, score 36.2 Mistral: Ministral 3 14B 2512 — $0.2/M, score 14.4 Anthropic: Claude Haiku 4.5 — $1/M, score 43.9 Qwen: Qwen3 235B A22B Thinking 2507 — $0.23/M, score 22.1 Qwen: Qwen3 8B — $0.117/M, score 9.0 Qwen: Qwen3 14B — $0.227/M, score 13.8 Qwen: Qwen3 32B — $0.08/M, score 15.3 Meta: Llama 4 Scout — $0.1/M, score 8.2 DeepSeek: DeepSeek V3 0324 — $0.25/M, score 21.2 Cohere: Command A — $2.5/M, score 27.8 Step 3.7 Flash — $0.185/M, score 39.6 Meta: Llama 4 Scout Input price per million tokens (log scale) Index

The stepped line is the efficient frontier: at each price, the best score available for that money or less. A model sitting on it is not being beaten by anything cheaper. Price is log-scaled because listed rates span four orders of magnitude. Only models with both a listed price and a score on this benchmark can appear.

Catalog activity

Change log

Field-level changes detected between successful source imports.

Full change log
Max Output Tokens8192 → 16384
Context Length131072 → 327680
Price Completion0.33999999999999997 → 0.3
Price Prompt0.11 → 0.09999999999999999
Max Output Tokens16384 → 8192
Context Length327680 → 131072
Price Completion0.3 → 0.33999999999999997
Price Prompt0.09999999999999999 → 0.11
Public API

Use this record

Fetch the complete source-linked model record. No key, no account, no rate-limited tier.

API documentation
Endpoint
GET https://model.kyssta.lol/api/v1/models/meta-llama/llama-4-scout
curl
curl "https://model.kyssta.lol/api/v1/models/meta-llama/llama-4-scout"
Common questions

Frequently asked questions

Answered directly from the stored record — nothing here is generated beyond the catalog's own fields.

What is Meta: Llama 4 Scout?

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input. It is published by Meta and catalogued here from OpenRouter.

What is the context length of Meta: Llama 4 Scout?

Meta: Llama 4 Scout accepts up to 327.68K tokens of context and returns up to 16.384K output tokens.

Does Meta: Llama 4 Scout support tool calling and structured output?

Provider catalogs list support for image input.

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