Meta, open weights

# Llama 3.1-70B

> Llama 3.1-70B by Meta, released July 2024. Ranked #308 of 354 with a Noometry Index of 29.6. API: $0.40 in / $0.40 out per M tokens. 128K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/llama-3-1-70b
- Last updated: 2026-10-10
- Title: Llama 3.1-70B Benchmarks, Price & Rank (October 2026)

Llama 3.1-70B by Meta ranks 308th of 354 ranked models on the Noometry Index as of October 2026, with a score of 29.6. Its strongest category is agentic & tool use, where it ranks 112th. API pricing starts at $0.40 per million input tokens and $0.40 per million output tokens, with a 128K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #308 of 354
- **Index score:** 29.6
- **Evidence:** Confirmed 35 results
- **Provider:** [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta)
- **Released:** July 23, 2024
- **Weights:** Open weights
- **Reasoning:** No
- **Context window:** 128K
- **Max output:** 4K
- **Input price:** $0.40 / M
- **Output price:** $0.40 / M
- **Blended price:** $0.40 / M
- **Output speed:** Not measured
- **Value:** #79 of 219
- **Knowledge cutoff:** December 2023
- **Input:** text
- **Hugging Face:** [meta-llama/Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct)

## Category scores

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

Llama 3.1-70B category scores

1.  Coding 30.3
2.  Agentic & Tool Use 25.1
3.  Reasoning 21.6
4.  Math 13.5
5.  Knowledge 24.2
6.  Multilingual 38.8
7.  Instruction Following 65.3
8.  Long Context 37.6
9.  Writing & Preference 35.4
10.  020406080

Llama 3.1-70B category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 30.3 | #296 | 4 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 25.1 | #112 | 2 |
| [Reasoning](https://noometry.com/best/reasoning) | 21.6 | #220 | 3 |
| [Math](https://noometry.com/best/math) | 13.5 | #304 | 4 |
| [Knowledge](https://noometry.com/best/knowledge) | 24.2 | #269 | 4 |
| [Multilingual](https://noometry.com/best/multilingual) | 38.8 | #225 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 65.3 | #223 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 37.6 | #214 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 35.4 | #267 | 5 |

## Strengths and weaknesses

Categories where Llama 3.1-70B places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Llama 3.1-70B: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Reasoning](https://noometry.com/best/reasoning) | 21.6 | −2.0 | #220 of 350, top 63% |
| [Long Context](https://noometry.com/best/long-context) | 37.6 | −3.3 | #214 of 296, top 73% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 25.1 | −5.3 | #112 of 154, top 73% |

### Weakest categories

Llama 3.1-70B: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Math](https://noometry.com/best/math) | 13.5 | −23.1 | #304 of 327, top 93% |
| [Coding](https://noometry.com/best/coding) | 30.3 | −8.5 | #296 of 340, top 88% |
| [Knowledge](https://noometry.com/best/knowledge) | 24.2 | −13.1 | #269 of 314, top 86% |

## Closest competitors

The models ranked just above and below Llama 3.1-70B. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Llama 3.1-70B
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [OLMo 2 Furious 13B](https://noometry.com/models/olmo-2-furious-13b) | #304 | 29.7 | — | — | [Compare](https://noometry.com/compare/llama-3-1-70b-vs-olmo-2-furious-13b) |
| [Phi 3 Mini 128k Instruct](https://noometry.com/models/phi-3-mini-128k-instruct) | #305 | 29.7 | — | — | [Compare](https://noometry.com/compare/llama-3-1-70b-vs-phi-3-mini-128k-instruct) |
| [phi-3-medium 14B](https://noometry.com/models/phi-3-medium-14b) | #306 | 29.7 | — | — | [Compare](https://noometry.com/compare/llama-3-1-70b-vs-phi-3-medium-14b) |
| [Gemma 2B](https://noometry.com/models/gemma-2b) | #307 | 29.6 | — | — | [Compare](https://noometry.com/compare/gemma-2b-vs-llama-3-1-70b) |
| [Llama 2-13B](https://noometry.com/models/llama-2-13b) | #309 | 29.6 | — | — | [Compare](https://noometry.com/compare/llama-2-13b-vs-llama-3-1-70b) |
| [Claude 3 Opus](https://noometry.com/models/claude-3-opus) | #310 | 29.5 | — | — | [Compare](https://noometry.com/compare/claude-3-opus-vs-llama-3-1-70b) |
| [DBRX](https://noometry.com/models/dbrx) | #311 | 29.4 | — | — | [Compare](https://noometry.com/compare/dbrx-vs-llama-3-1-70b) |
| [Gemma 2 27B](https://noometry.com/models/gemma-2-27b) | #312 | 29.4 | $0.65 | — | [Compare](https://noometry.com/compare/gemma-2-27b-vs-llama-3-1-70b) |

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 3.1-70B Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 9% | #115 of 119, top 97% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BigCodeBench Instruct](https://noometry.com/benchmarks/bigcodebench-instruct) | 46.1% | #14 of 64, top 22% |  | [BigCodeBench](https://bigcode-bench.github.io/) | 2024-07-23 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1260 | #222 of 294, top 76% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [BigCodeBench Complete](https://noometry.com/benchmarks/bigcodebench-complete) | 54.8% | #20 of 66, top 31% |  | [BigCodeBench](https://bigcode-bench.github.io/) | 2024-07-23 |

### Agentic & Tool Use

Llama 3.1-70B Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [TheAgentCompany](https://noometry.com/benchmarks/the-agent-company) | 6.9% | #10 of 14, top 72% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BALROG](https://noometry.com/benchmarks/balrog) | 27.9% | #20 of 35, top 58% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Llama 3.1-70B Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1241 | #226 of 297, top 77% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 60% | #116 of 151, top 77% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 14.8% | #105 of 125, top 84% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 125.92 | #154 of 213, top 73% |  | [Epoch AI](https://epoch.ai/eci) | 2024-07-23 |

### Math

Llama 3.1-70B Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 3.6% | #154 of 173, top 90% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-02-25 |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 21% | #50 of 57, top 88% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1252 | #210 of 285, top 74% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 36.7% | #55 of 79, top 70% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |

### Knowledge

Llama 3.1-70B Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 44.2% | #143 of 186, top 77% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 65.3% | #38 of 58, top 66% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 42.6% | #40 of 57, top 71% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1209 | #218 of 273, top 80% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MMLU](https://noometry.com/benchmarks/mmlu) | 80.1% | #16 of 81, top 20% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

Llama 3.1-70B Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1219 | #225 of 297, top 76% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1215 | #225 of 285, top 79% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1261 | #179 of 223, top 81% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1222 | #186 of 231, top 81% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1132 | #179 of 211, top 85% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1140 | #182 of 213, top 86% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1234 | #221 of 283, top 79% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1253 | #182 of 226, top 81% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Llama 3.1-70B Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 82.1% | #33 of 57, top 58% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1231 | #227 of 298, top 77% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Llama 3.1-70B Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1241 | #226 of 291, top 78% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Llama 3.1-70B Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1261 | #222 of 297, top 75% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1232 | #220 of 295, top 75% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 784 | #104 of 115, top 91% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 75.8% | #44 of 57, top 78% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1256 | #219 of 295, top 75% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Llama 3.1-70B API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $0.72 | $0.72 | — | 2026-10-10 |
| [openrouter](https://openrouter.ai/meta-llama/llama-3.1-70b-instruct) | $0.40 | $0.40 | — | 2026-10-10 |

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

## Compare Llama 3.1-70B

-   [Llama 3.1-70B vs Llama 3-70B](https://noometry.com/compare/llama-3-1-70b-vs-llama-3-70b)
-   [Llama 3.1-70B vs Gemma 2B](https://noometry.com/compare/gemma-2b-vs-llama-3-1-70b)
-   [Llama 3.1-70B vs Llama 2-13B](https://noometry.com/compare/llama-2-13b-vs-llama-3-1-70b)
-   [Llama 3.1-70B vs phi-3-medium 14B](https://noometry.com/compare/llama-3-1-70b-vs-phi-3-medium-14b)
-   [Llama 3.1-70B vs Claude 3 Opus](https://noometry.com/compare/claude-3-opus-vs-llama-3-1-70b)
-   [Llama 3.1-70B vs Phi 3 Mini 128k Instruct](https://noometry.com/compare/llama-3-1-70b-vs-phi-3-mini-128k-instruct)
-   [Llama 3.1-70B vs DBRX](https://noometry.com/compare/dbrx-vs-llama-3-1-70b)
-   [Llama 3.1-70B vs GPT-6 Astra](https://noometry.com/compare/gpt-6-astra-vs-llama-3-1-70b)
-   [Llama 3.1-70B vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-llama-3-1-70b)
-   [Llama 3.1-70B vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-llama-3-1-70b)
-   [Llama 3.1-70B vs Kimi K3](https://noometry.com/compare/kimi-k3-vs-llama-3-1-70b)
-   [Llama 3.1-70B vs Grok 4.6](https://noometry.com/compare/grok-4-6-vs-llama-3-1-70b)
-   [Llama 3.1-70B vs Qwen3.8 Max](https://noometry.com/compare/llama-3-1-70b-vs-qwen3-8-max)
-   [Llama 3.1-70B vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-llama-3-1-70b)

## 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 3.1-70B?

Llama 3.1-70B by Meta ranks 308th of 354 ranked models on the Noometry Index as of October 2026, with a score of 29.6. Its strongest category is agentic & tool use, where it ranks 112th. API pricing starts at $0.40 per million input tokens and $0.40 per million output tokens, with a 128K-token context window.

### How much does Llama 3.1-70B cost?

Llama 3.1-70B costs $0.40 per million input tokens and $0.40 per million output tokens on openrouter.

### What is Llama 3.1-70B's context window?

Llama 3.1-70B accepts up to 128K tokens of input and can write up to 4K tokens in one response.

### Is Llama 3.1-70B open source?

Yes. Llama 3.1-70B's weights are downloadable from Hugging Face (meta-llama/Meta-Llama-3.1-70B-Instruct); check the license for commercial terms.

### What are Llama 3.1-70B's strengths and weaknesses?

Relative to other ranked models, Llama 3.1-70B places best in reasoning, long context, agentic & tool use and lowest in math, coding, knowledge.

### What is Llama 3.1-70B best at?

Its best category is agentic & tool use, where it ranks 112th on Noometry.

### Cite this page

Noometry. (2026). Llama 3.1-70B benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/llama-3-1-70b

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/llama-3-1-70b.md).