Meta, open weights

# Llama 3.1-405B

> Llama 3.1-405B by Meta, released July 2024. Ranked #288 of 354 with a Noometry Index of 30.7. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/llama-3-1-405b
- Last updated: 2026-10-10
- Title: Llama 3.1-405B Benchmarks, Price & Rank (October 2026)

Llama 3.1-405B by Meta ranks 288th of 354 ranked models on the Noometry Index as of October 2026, with a score of 30.7. Its strongest category is agentic & tool use, where it ranks 140th.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #288 of 354
- **Index score:** 30.7
- **Evidence:** Confirmed 42 results
- **Provider:** [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta)
- **Released:** July 23, 2024
- **Weights:** Open weights
- **Reasoning:** Unknown
- **Context window:** —
- **Max output:** —
- **Input price:** Not listed
- **Output price:** Not listed
- **Blended price:** Not listed
- **Output speed:** 78 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** Not ranked
- **Knowledge cutoff:** Unknown

## Category scores

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

Llama 3.1-405B category scores

1.  Coding 33.1
2.  Agentic & Tool Use 21.0
3.  Reasoning 16.8
4.  Math 18.4
5.  Knowledge 30.4
6.  Multilingual 40.7
7.  Instruction Following 65.9
8.  Long Context 38.4
9.  Writing & Preference 38.9
10.  020406080

Llama 3.1-405B category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 33.1 | #262 | 2 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 21.0 | #140 | 2 |
| [Reasoning](https://noometry.com/best/reasoning) | 16.8 | #300 | 4 |
| [Math](https://noometry.com/best/math) | 18.4 | #290 | 4 |
| [Knowledge](https://noometry.com/best/knowledge) | 30.4 | #227 | 5 |
| [Multilingual](https://noometry.com/best/multilingual) | 40.7 | #214 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 65.9 | #214 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 38.4 | #197 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 38.9 | #251 | 5 |

## Strengths and weaknesses

Categories where Llama 3.1-405B places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Llama 3.1-405B: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 38.4 | −2.5 | #197 of 296, top 67% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 65.9 | −5.4 | #214 of 305, top 71% |
| [Multilingual](https://noometry.com/best/multilingual) | 40.7 | −6.7 | #214 of 297, top 73% |

### Weakest categories

Llama 3.1-405B: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 21.0 | −9.3 | #140 of 154, top 91% |
| [Math](https://noometry.com/best/math) | 18.4 | −18.2 | #290 of 327, top 89% |
| [Reasoning](https://noometry.com/best/reasoning) | 16.8 | −6.8 | #300 of 350, top 86% |

## Closest competitors

The models ranked just above and below Llama 3.1-405B. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Llama 3.1-405B
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Gemma 3 27B](https://noometry.com/models/gemma-3-27b) | #284 | 30.8 | $0.10 | 62 | [Compare](https://noometry.com/compare/gemma-3-27b-vs-llama-3-1-405b) |
| [Qwen1.5-72B](https://noometry.com/models/qwen1-5-72b) | #285 | 30.8 | — | — | [Compare](https://noometry.com/compare/llama-3-1-405b-vs-qwen1-5-72b) |
| [Granite 3.0 2b Instruct](https://noometry.com/models/granite-3-0-2b-instruct) | #286 | 30.8 | — | — | [Compare](https://noometry.com/compare/granite-3-0-2b-instruct-vs-llama-3-1-405b) |
| [Codellama 34b Instruct](https://noometry.com/models/codellama-34b-instruct) | #287 | 30.8 | — | — | [Compare](https://noometry.com/compare/codellama-34b-instruct-vs-llama-3-1-405b) |
| [Yi-1.5-34B](https://noometry.com/models/yi-1-5-34b) | #289 | 30.6 | — | — | [Compare](https://noometry.com/compare/llama-3-1-405b-vs-yi-1-5-34b) |
| [Codestral](https://noometry.com/models/codestral) | #290 | 30.6 | $0.45 | 271 | [Compare](https://noometry.com/compare/codestral-vs-llama-3-1-405b) |
| [Llama-3.3-70B-Instruct](https://noometry.com/models/llama-3-3-70b-instruct) | #291 | 30.6 | $0.16 | — | [Compare](https://noometry.com/compare/llama-3-1-405b-vs-llama-3-3-70b-instruct) |
| [GPT-4 Turbo](https://noometry.com/models/gpt-4-turbo) | #292 | 30.5 | $15 | — | [Compare](https://noometry.com/compare/gpt-4-turbo-vs-llama-3-1-405b) |

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 3.1-405B Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 21.4% | #104 of 119, top 88% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1283 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1291 | #204 of 294, top 70% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Agentic & Tool Use

Llama 3.1-405B Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [TheAgentCompany](https://noometry.com/benchmarks/the-agent-company) | 7.4% | #9 of 14, top 65% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Cybench](https://noometry.com/benchmarks/cybench) | 7.5% | #19 of 21, top 91% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Llama 3.1-405B Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 23% | #68 of 77, top 89% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 45% | #70 of 99, top 71% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1263 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1269 | #207 of 297, top 70% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 61.4% | #113 of 151, top 75% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BIG-Bench Hard](https://noometry.com/benchmarks/bbh) | 82.9% | #3 of 27, top 12% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 128.75 | #144 of 213, top 68% |  | [Epoch AI](https://epoch.ai/eci) | 2024-07-23 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 59.9 | #35 of 72, top 49% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [HellaSwag](https://noometry.com/benchmarks/hellaswag) | 89.2% | #3 of 29, top 11% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [PIQA](https://noometry.com/benchmarks/piqa) | 85.9% | #4 of 27, top 15% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WinoGrande](https://noometry.com/benchmarks/winogrande) | 89.2% | Best of 43 |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WinoGrande](https://noometry.com/benchmarks/winogrande) | 82.2% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

Llama 3.1-405B Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 9.7% | #133 of 173, top 77% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-02-25 |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 24.9% | #44 of 57, top 78% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1281 | #192 of 285, top 68% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1278 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 49.8% | #46 of 79, top 59% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |

### Knowledge

Llama 3.1-405B Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 50.9% | #127 of 186, top 69% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 72.3% | #33 of 58, top 57% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Confabulations](https://noometry.com/benchmarks/confabulations) (lower is better) | 17.6% | #27 of 51, top 53% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/confabulations) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 52.2% | #30 of 57, top 53% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1243 | #202 of 273, top 74% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1229 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ARC (AI2) Challenge](https://noometry.com/benchmarks/arc-challenge) | 95.3% | #2 of 39, top 6% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [MMLU](https://noometry.com/benchmarks/mmlu) | 84.5% | #10 of 81, top 13% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [MMLU](https://noometry.com/benchmarks/mmlu) | 84.4% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [TriviaQA](https://noometry.com/benchmarks/triviaqa) | 82.7% | #8 of 25, top 32% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

Llama 3.1-405B Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1248 | #214 of 297, top 73% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1247 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1242 | #213 of 285, top 75% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1234 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1271 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1279 | #172 of 223, top 78% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1252 | #176 of 231, top 77% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1251 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1171 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1208 | #153 of 211, top 73% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1172 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1184 | #171 of 213, top 81% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1265 | #199 of 283, top 71% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1256 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1260 | #179 of 226, top 80% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1253 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Llama 3.1-405B Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 81.1% | #38 of 57, top 67% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1259 | #202 of 298, top 68% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1259 | #202 of 298, top 68% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Llama 3.1-405B Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1260 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1266 | #211 of 291, top 73% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Llama 3.1-405B Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1284 | #205 of 297, top 70% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1282 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1260 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1262 | #197 of 295, top 67% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 870 | #101 of 115, top 88% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 78.3% | #40 of 57, top 71% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1297 | #189 of 295, top 65% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1287 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## Compare Llama 3.1-405B

-   [Llama 3.1-405B vs Llama 3-70B](https://noometry.com/compare/llama-3-1-405b-vs-llama-3-70b)
-   [Llama 3.1-405B vs Codellama 34b Instruct](https://noometry.com/compare/codellama-34b-instruct-vs-llama-3-1-405b)
-   [Llama 3.1-405B vs Yi-1.5-34B](https://noometry.com/compare/llama-3-1-405b-vs-yi-1-5-34b)
-   [Llama 3.1-405B vs Granite 3.0 2b Instruct](https://noometry.com/compare/granite-3-0-2b-instruct-vs-llama-3-1-405b)
-   [Llama 3.1-405B vs Codestral](https://noometry.com/compare/codestral-vs-llama-3-1-405b)
-   [Llama 3.1-405B vs Qwen1.5-72B](https://noometry.com/compare/llama-3-1-405b-vs-qwen1-5-72b)
-   [Llama 3.1-405B vs Llama-3.3-70B-Instruct](https://noometry.com/compare/llama-3-1-405b-vs-llama-3-3-70b-instruct)
-   [Llama 3.1-405B vs GPT-6 Astra](https://noometry.com/compare/gpt-6-astra-vs-llama-3-1-405b)
-   [Llama 3.1-405B vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-llama-3-1-405b)
-   [Llama 3.1-405B vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-llama-3-1-405b)
-   [Llama 3.1-405B vs Kimi K3](https://noometry.com/compare/kimi-k3-vs-llama-3-1-405b)
-   [Llama 3.1-405B vs Grok 4.6](https://noometry.com/compare/grok-4-6-vs-llama-3-1-405b)
-   [Llama 3.1-405B vs Qwen3.8 Max](https://noometry.com/compare/llama-3-1-405b-vs-qwen3-8-max)
-   [Llama 3.1-405B vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-llama-3-1-405b)

## 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 3.1-405B?

Llama 3.1-405B by Meta ranks 288th of 354 ranked models on the Noometry Index as of October 2026, with a score of 30.7. Its strongest category is agentic & tool use, where it ranks 140th.

### Is Llama 3.1-405B open source?

Yes. Llama 3.1-405B's weights are downloadable; check the license for commercial terms.

### How fast is Llama 3.1-405B?

Llama 3.1-405B generated about 78 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are Llama 3.1-405B's strengths and weaknesses?

Relative to other ranked models, Llama 3.1-405B places best in long context, instruction following, multilingual and lowest in agentic & tool use, math, reasoning.

### What is Llama 3.1-405B best at?

Its best category is agentic & tool use, where it ranks 140th on Noometry.

### Cite this page

Noometry. (2026). Llama 3.1-405B benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/llama-3-1-405b

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/llama-3-1-405b.md).