Meta, open weights

# Llama 4 Scout

> Llama 4 Scout by Meta, released April 2025. Ranked #330 of 354 with a Noometry Index of 27.7. API: $0.10 in / $0.30 out per M tokens. 128K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/llama-4-scout
- Last updated: 2026-10-10
- Title: Llama 4 Scout Benchmarks, Price & Rank (October 2026)

Llama 4 Scout by Meta ranks 330th of 354 ranked models on the Noometry Index as of October 2026, with a score of 27.7. Its strongest category is multimodal, where it ranks 102nd. API pricing starts at $0.10 per million input tokens and $0.30 per million output tokens, with a 128K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #330 of 354
- **Index score:** 27.7
- **Evidence:** Confirmed 43 results
- **Provider:** [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta)
- **Released:** April 5, 2025
- **Weights:** Open weights
- **Reasoning:** No
- **Context window:** 128K
- **Max output:** 4K
- **Input price:** $0.10 / M
- **Output price:** $0.30 / M
- **Blended price:** $0.15 / M
- **Output speed:** 272 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #45 of 219
- **Knowledge cutoff:** August 2024
- **Input:** text, image
- **Hugging Face:** [meta-llama/Llama-4-Scout-17B-16E-Instruct](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct)

## Category scores

Each category score combines every public result we have in that category.

Llama 4 Scout category scores

1.  Coding 20.2
2.  Agentic & Tool Use 24.6
3.  Reasoning 9.1
4.  Math 19.6
5.  Knowledge 31.9
6.  Multimodal 32.2
7.  Multilingual 41.0
8.  Instruction Following 65.8
9.  Long Context 27.5
10.  Writing & Preference 37.0
11.  020406080

Llama 4 Scout category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 20.2 | #339 | 4 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 24.6 | #119 | 1 |
| [Reasoning](https://noometry.com/best/reasoning) | 9.1 | #345 | 7 |
| [Math](https://noometry.com/best/math) | 19.6 | #286 | 4 |
| [Knowledge](https://noometry.com/best/knowledge) | 31.9 | #217 | 5 |
| [Multimodal](https://noometry.com/best/multimodal) | 32.2 | #102 | 1 |
| [Multilingual](https://noometry.com/best/multilingual) | 41.0 | #212 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 65.8 | #217 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 27.5 | #294 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 37.0 | #261 | 5 |

## Strengths and weaknesses

Categories where Llama 4 Scout places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Llama 4 Scout: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Knowledge](https://noometry.com/best/knowledge) | 31.9 | −5.4 | #217 of 314, top 70% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 65.8 | −5.5 | #217 of 305, top 72% |
| [Multilingual](https://noometry.com/best/multilingual) | 41.0 | −6.4 | #212 of 297, top 72% |

### Weakest categories

Llama 4 Scout: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 20.2 | −18.5 | #339 of 340, top 100% |
| [Long Context](https://noometry.com/best/long-context) | 27.5 | −13.4 | #294 of 296, top 100% |
| [Reasoning](https://noometry.com/best/reasoning) | 9.1 | −14.5 | #345 of 350, top 99% |

## Closest competitors

The models ranked just above and below Llama 4 Scout. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Llama 4 Scout
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Gemma 3 4B](https://noometry.com/models/gemma-3-4b) | #326 | 28.1 | $0.05 | 72 | [Compare](https://noometry.com/compare/gemma-3-4b-vs-llama-4-scout) |
| [GPT-4.1 nano](https://noometry.com/models/gpt-4-1-nano) | #327 | 27.9 | $0.18 | 135 | [Compare](https://noometry.com/compare/gpt-4-1-nano-vs-llama-4-scout) |
| [Phi 3 Mini 4k Instruct](https://noometry.com/models/phi-3-mini-4k-instruct) | #328 | 27.9 | — | — | [Compare](https://noometry.com/compare/llama-4-scout-vs-phi-3-mini-4k-instruct) |
| [Yi-34B](https://noometry.com/models/yi-34b) | #329 | 27.8 | — | — | [Compare](https://noometry.com/compare/llama-4-scout-vs-yi-34b) |
| [Llama 3.2 90B](https://noometry.com/models/llama-3-2-90b) | #331 | 27.5 | — | — | [Compare](https://noometry.com/compare/llama-3-2-90b-vs-llama-4-scout) |
| [Gemini 1.0 Pro](https://noometry.com/models/gemini-1-0-pro) | #332 | 27.3 | — | — | [Compare](https://noometry.com/compare/gemini-1-0-pro-vs-llama-4-scout) |
| [Mixtral 8x22B](https://noometry.com/models/mixtral-8x22b) | #333 | 27.1 | $3 | — | [Compare](https://noometry.com/compare/llama-4-scout-vs-mixtral-8x22b) |
| [Mixtral 8x7B](https://noometry.com/models/mixtral-8x7b) | #334 | 27.1 | $0.70 | — | [Compare](https://noometry.com/compare/llama-4-scout-vs-mixtral-8x7b) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

Llama 4 Scout Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) | 9.1% | #38 of 39, top 98% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-20 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 17% | #119 of 121, top 99% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1286 | #209 of 294, top 72% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [BigCodeBench Complete](https://noometry.com/benchmarks/bigcodebench-complete) | 43.1% | #47 of 66, top 72% |  | [BigCodeBench](https://bigcode-bench.github.io/) | 2025-04-05 |

### Agentic & Tool Use

Llama 4 Scout Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 28.1% | #37 of 49, top 76% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |

### Reasoning

Llama 4 Scout Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 0% | #81 of 83, top 98% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 36.9% | #86 of 99, top 87% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 0.5% | #82 of 83, top 99% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 0% | #120 of 134, top 90% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1266 | #211 of 297, top 72% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 57.9% | #120 of 151, top 80% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 12% | #109 of 125, top 88% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 129.64 | #139 of 213, top 66% |  | [Epoch AI](https://epoch.ai/eci) | 2025-04-05 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 57.5 | #57 of 72, top 80% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

Llama 4 Scout Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 7.8% | #138 of 173, top 80% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-08 |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 37.3% | #30 of 57, top 53% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1287 | #188 of 285, top 66% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 62.3% | #38 of 79, top 49% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-08 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 0% | #68 of 68, top 100% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-08 |

### Knowledge

Llama 4 Scout Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 51.8% | #125 of 186, top 68% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-08 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 74.2% | #27 of 58, top 47% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 7.7% | #34 of 96, top 36% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 50.7% | #34 of 57, top 60% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1235 | #205 of 273, top 76% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Llama 4 Scout Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1118 | #105 of 122, top 87% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [SpatialViz-Bench](https://noometry.com/benchmarks/spatialviz-bench) | 34.2% | #4 of 8, top 50% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

Llama 4 Scout Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1252 | #212 of 297, top 72% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1255 | #204 of 285, top 72% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1282 | #169 of 223, top 76% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1272 | #165 of 231, top 72% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1206 | #157 of 211, top 75% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1207 | #156 of 213, top 74% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1263 | #202 of 283, top 72% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1278 | #170 of 226, top 76% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Llama 4 Scout Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 81.8% | #34 of 57, top 60% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1248 | #215 of 298, top 73% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Llama 4 Scout Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 36% | #44 of 47, top 94% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1265 | #213 of 291, top 74% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Llama 4 Scout Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1279 | #210 of 297, top 71% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1249 | #206 of 295, top 70% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 783 | #105 of 115, top 92% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 78% | #41 of 57, top 72% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1280 | #200 of 295, top 68% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Llama 4 Scout API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $0.20 | $0.78 | — | 2026-10-10 |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $0.17 | $0.66 | — | 2026-10-10 |
| [deepinfra](https://deepinfra.com/models) | $0.10 | $0.30 | — | 2026-10-10 |
| [openrouter](https://openrouter.ai/meta-llama/llama-4-scout) | $0.10 | $0.30 | — | 2026-10-10 |

[All Meta API prices →](https://noometry.com/llm-pricing/meta) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare Llama 4 Scout

-   [Llama 4 Scout vs Yi-34B](https://noometry.com/compare/llama-4-scout-vs-yi-34b)
-   [Llama 4 Scout vs Llama 3.2 90B](https://noometry.com/compare/llama-3-2-90b-vs-llama-4-scout)
-   [Llama 4 Scout vs Phi 3 Mini 4k Instruct](https://noometry.com/compare/llama-4-scout-vs-phi-3-mini-4k-instruct)
-   [Llama 4 Scout vs Gemini 1.0 Pro](https://noometry.com/compare/gemini-1-0-pro-vs-llama-4-scout)
-   [Llama 4 Scout vs GPT-4.1 nano](https://noometry.com/compare/gpt-4-1-nano-vs-llama-4-scout)
-   [Llama 4 Scout vs Mixtral 8x22B](https://noometry.com/compare/llama-4-scout-vs-mixtral-8x22b)
-   [Llama 4 Scout vs GPT-6 Astra](https://noometry.com/compare/gpt-6-astra-vs-llama-4-scout)
-   [Llama 4 Scout vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-llama-4-scout)
-   [Llama 4 Scout vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-llama-4-scout)
-   [Llama 4 Scout vs Kimi K3](https://noometry.com/compare/kimi-k3-vs-llama-4-scout)
-   [Llama 4 Scout vs Grok 4.6](https://noometry.com/compare/grok-4-6-vs-llama-4-scout)
-   [Llama 4 Scout vs Qwen3.8 Max](https://noometry.com/compare/llama-4-scout-vs-qwen3-8-max)
-   [Llama 4 Scout vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-llama-4-scout)
-   [Llama 4 Scout vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-llama-4-scout)

## Other Meta models

-   [Muse Spark 1.3](https://noometry.com/models/muse-spark-1-3)54.8
-   [Muse Spark](https://noometry.com/models/muse-spark)50.6
-   [Muse Spark 1.2](https://noometry.com/models/muse-spark-1-2)50.3
-   [Muse Spark 1.1](https://noometry.com/models/muse-spark-1-1)49.9
-   [Muse Glimmer](https://noometry.com/models/muse-glimmer)41.7
-   [Codellama 70b Instruct](https://noometry.com/models/codellama-70b-instruct)33.7
-   [Llama 4 Maverick](https://noometry.com/models/llama-4-maverick)30.9
-   [Codellama 34b Instruct](https://noometry.com/models/codellama-34b-instruct)30.8

## Frequently asked questions

### How good is Llama 4 Scout?

Llama 4 Scout by Meta ranks 330th of 354 ranked models on the Noometry Index as of October 2026, with a score of 27.7. Its strongest category is multimodal, where it ranks 102nd. API pricing starts at $0.10 per million input tokens and $0.30 per million output tokens, with a 128K-token context window.

### How much does Llama 4 Scout cost?

Llama 4 Scout costs $0.10 per million input tokens and $0.30 per million output tokens on deepinfra.

### What is Llama 4 Scout's context window?

Llama 4 Scout accepts up to 128K tokens of input and can write up to 4K tokens in one response.

### Is Llama 4 Scout open source?

Yes. Llama 4 Scout's weights are downloadable from Hugging Face (meta-llama/Llama-4-Scout-17B-16E-Instruct); check the license for commercial terms.

### How fast is Llama 4 Scout?

Llama 4 Scout generated about 272 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are Llama 4 Scout's strengths and weaknesses?

Relative to other ranked models, Llama 4 Scout places best in knowledge, instruction following, multilingual and lowest in coding, long context, reasoning.

### What is Llama 4 Scout best at?

Its best category is multimodal, where it ranks 102nd on Noometry.

### Cite this page

Noometry. (2026). Llama 4 Scout benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/llama-4-scout

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/llama-4-scout.md).