Meta, open weights

# Llama 3.1-8B

> Llama 3.1-8B by Meta, released July 2024. Ranked #352 of 354 with a Noometry Index of 23.0. API: $0.05 in / $0.08 out per M tokens. 128K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/llama-3-1-8b
- Last updated: 2026-10-10
- Title: Llama 3.1-8B Benchmarks, Price & Rank (October 2026)

Llama 3.1-8B by Meta ranks 352nd of 354 ranked models on the Noometry Index as of October 2026, with a score of 23.0. Its strongest category is agentic & tool use, where it ranks 131st. API pricing starts at $0.05 per million input tokens and $0.08 per million output tokens, with a 128K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #352 of 354
- **Index score:** 23.0
- **Evidence:** Confirmed 43 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:** $0.05 / M
- **Output price:** $0.08 / M
- **Blended price:** $0.0575 / M
- **Output speed:** Not measured
- **Value:** #13 of 219
- **Knowledge cutoff:** December 2023
- **Input:** text
- **Hugging Face:** [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)

## Category scores

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

Llama 3.1-8B category scores

1.  Coding 20.2
2.  Agentic & Tool Use 22.5
3.  Reasoning 14.9
4.  Math 10.2
5.  Knowledge 8.0
6.  Multilingual 34.0
7.  Instruction Following 58.9
8.  Long Context 35.8
9.  Writing & Preference 29.7
10.  0204060

Llama 3.1-8B category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 20.2 | #340 | 5 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 22.5 | #131 | 2 |
| [Reasoning](https://noometry.com/best/reasoning) | 14.9 | #321 | 5 |
| [Math](https://noometry.com/best/math) | 10.2 | #317 | 4 |
| [Knowledge](https://noometry.com/best/knowledge) | 8.0 | #307 | 4 |
| [Multilingual](https://noometry.com/best/multilingual) | 34.0 | #249 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 58.9 | #258 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 35.8 | #238 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 29.7 | #290 | 5 |

## Strengths and weaknesses

Categories where Llama 3.1-8B places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Llama 3.1-8B: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 35.8 | −5.1 | #238 of 296, top 81% |
| [Multilingual](https://noometry.com/best/multilingual) | 34.0 | −13.4 | #249 of 297, top 84% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 58.9 | −12.4 | #258 of 305, top 85% |

### Weakest categories

Llama 3.1-8B: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 20.2 | −18.5 | #340 of 340, top 100% |
| [Knowledge](https://noometry.com/best/knowledge) | 8.0 | −29.3 | #307 of 314, top 98% |
| [Math](https://noometry.com/best/math) | 10.2 | −26.4 | #317 of 327, top 97% |

## Closest competitors

The models ranked just above and below Llama 3.1-8B. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Llama 3.1-8B
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Claude 2](https://noometry.com/models/claude-2) | #346 | 25.0 | — | — | [Compare](https://noometry.com/compare/claude-2-vs-llama-3-1-8b) |
| [DeepSeek LLM 67B](https://noometry.com/models/deepseek-llm-67b) | #347 | 24.9 | — | — | [Compare](https://noometry.com/compare/deepseek-llm-67b-vs-llama-3-1-8b) |
| [Llama 13b](https://noometry.com/models/llama-13b) | #348 | 24.4 | — | — | [Compare](https://noometry.com/compare/llama-13b-vs-llama-3-1-8b) |
| [Llama 2-70B](https://noometry.com/models/llama-2-70b) | #349 | 24.4 | — | — | [Compare](https://noometry.com/compare/llama-2-70b-vs-llama-3-1-8b) |
| [GPT-3.5-turbo](https://noometry.com/models/gpt-3-5-turbo) | #350 | 23.2 | $0.75 | — | [Compare](https://noometry.com/compare/gpt-3-5-turbo-vs-llama-3-1-8b) |
| [Mistral 7B](https://noometry.com/models/mistral-7b) | #351 | 23.0 | $0.25 | — | [Compare](https://noometry.com/compare/llama-3-1-8b-vs-mistral-7b) |
| [Gemma 3 1B](https://noometry.com/models/gemma-3-1b) | #353 | 21.1 | — | — | [Compare](https://noometry.com/compare/gemma-3-1b-vs-llama-3-1-8b) |
| [Llama 3.2 1B](https://noometry.com/models/llama-3-2-1b) | #354 | 20.1 | $0.0705 | — | [Compare](https://noometry.com/compare/llama-3-1-8b-vs-llama-3-2-1b) |

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 3.1-8B Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SciCode](https://noometry.com/benchmarks/scicode) | 13.2% | #120 of 121, top 100% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 1.7% | #119 of 119, top 100% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BigCodeBench Instruct](https://noometry.com/benchmarks/bigcodebench-instruct) | 32.8% | #51 of 64, top 80% |  | [BigCodeBench](https://bigcode-bench.github.io/) | 2024-07-23 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1195 | #244 of 294, top 83% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [BigCodeBench Complete](https://noometry.com/benchmarks/bigcodebench-complete) | 40.5% | #52 of 66, top 79% |  | [BigCodeBench](https://bigcode-bench.github.io/) | 2024-07-23 |
| [HumanEval+](https://noometry.com/benchmarks/humaneval-plus) | 62.8% | #25 of 45, top 56% |  | [EvalPlus](https://evalplus.github.io/leaderboard.html) |  |
| [MBPP+](https://noometry.com/benchmarks/mbpp-plus) | 55.6% | #28 of 38, top 74% |  | [EvalPlus](https://evalplus.github.io/leaderboard.html) |  |

### Agentic & Tool Use

Llama 3.1-8B Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 25.8% | #41 of 49, top 84% | prompt | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| [BALROG](https://noometry.com/benchmarks/balrog) | 15.1% | #30 of 35, top 86% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Llama 3.1-8B Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [CritPt](https://noometry.com/benchmarks/critpt) | 0% | #116 of 134, top 87% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 0% | #120 of 129, top 94% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1175 | #245 of 297, top 83% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 50.9% | #135 of 151, top 90% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 5.4% | #122 of 125, top 98% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 116.57 | #182 of 213, top 86% |  | [Epoch AI](https://epoch.ai/eci) | 2024-07-23 |
| [PIQA](https://noometry.com/benchmarks/piqa) | 81.2% | #18 of 27, top 67% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

Llama 3.1-8B Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 1.7% | #164 of 173, top 95% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 13.7% | #55 of 57, top 97% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1179 | #242 of 285, top 85% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 22.9% | #61 of 79, top 78% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [GSM8K](https://noometry.com/benchmarks/gsm8k) | 82.4% | #12 of 38, top 32% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Knowledge

Llama 3.1-8B Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 27% | #177 of 186, top 96% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 40.6% | #54 of 58, top 94% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 24.7% | #57 of 57, top 100% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1144 | #239 of 273, top 88% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [BoolQ](https://noometry.com/benchmarks/boolq) | 82.8% | #13 of 23, top 57% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [MMLU](https://noometry.com/benchmarks/mmlu) | 56.1% | #67 of 81, top 83% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

Llama 3.1-8B Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1148 | #249 of 297, top 84% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1151 | #246 of 285, top 87% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1177 | #198 of 223, top 89% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1144 | #203 of 231, top 88% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1061 | #190 of 211, top 91% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1053 | #193 of 213, top 91% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1158 | #248 of 283, top 88% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1169 | #200 of 226, top 89% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Llama 3.1-8B Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 74.3% | #51 of 57, top 90% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1159 | #250 of 298, top 84% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Llama 3.1-8B Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1182 | #247 of 291, top 85% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Llama 3.1-8B Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1187 | #249 of 297, top 84% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1154 | #249 of 295, top 85% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 713 | #109 of 115, top 95% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 68.7% | #53 of 57, top 93% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1172 | #245 of 295, top 84% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Llama 3.1-8B API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $0.22 | $0.22 | — | 2026-10-10 |
| [groq](https://console.groq.com/docs/models) | $0.05 | $0.08 | — | 2026-10-10 |
| [openrouter](https://openrouter.ai/meta-llama/llama-3.1-8b-instruct) | $0.05 | $0.08 | $0.025 | 2026-10-10 |

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

## Compare Llama 3.1-8B

-   [Llama 3.1-8B vs Llama 3-70B](https://noometry.com/compare/llama-3-1-8b-vs-llama-3-70b)
-   [Llama 3.1-8B vs Mistral 7B](https://noometry.com/compare/llama-3-1-8b-vs-mistral-7b)
-   [Llama 3.1-8B vs Gemma 3 1B](https://noometry.com/compare/gemma-3-1b-vs-llama-3-1-8b)
-   [Llama 3.1-8B vs GPT-3.5-turbo](https://noometry.com/compare/gpt-3-5-turbo-vs-llama-3-1-8b)
-   [Llama 3.1-8B vs Llama 3.2 1B](https://noometry.com/compare/llama-3-1-8b-vs-llama-3-2-1b)
-   [Llama 3.1-8B vs Llama 2-70B](https://noometry.com/compare/llama-2-70b-vs-llama-3-1-8b)
-   [Llama 3.1-8B vs Llama 13b](https://noometry.com/compare/llama-13b-vs-llama-3-1-8b)
-   [Llama 3.1-8B vs GPT-6 Astra](https://noometry.com/compare/gpt-6-astra-vs-llama-3-1-8b)
-   [Llama 3.1-8B vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-llama-3-1-8b)
-   [Llama 3.1-8B vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-llama-3-1-8b)
-   [Llama 3.1-8B vs Kimi K3](https://noometry.com/compare/kimi-k3-vs-llama-3-1-8b)
-   [Llama 3.1-8B vs Grok 4.6](https://noometry.com/compare/grok-4-6-vs-llama-3-1-8b)
-   [Llama 3.1-8B vs Qwen3.8 Max](https://noometry.com/compare/llama-3-1-8b-vs-qwen3-8-max)
-   [Llama 3.1-8B vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-llama-3-1-8b)

## 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 3.1-8B?

Llama 3.1-8B by Meta ranks 352nd of 354 ranked models on the Noometry Index as of October 2026, with a score of 23.0. Its strongest category is agentic & tool use, where it ranks 131st. API pricing starts at $0.05 per million input tokens and $0.08 per million output tokens, with a 128K-token context window.

### How much does Llama 3.1-8B cost?

Llama 3.1-8B costs $0.05 per million input tokens and $0.08 per million output tokens on groq.

### What is Llama 3.1-8B's context window?

Llama 3.1-8B accepts up to 128K tokens of input and can write up to 4K tokens in one response.

### Is Llama 3.1-8B open source?

Yes. Llama 3.1-8B's weights are downloadable from Hugging Face (meta-llama/Meta-Llama-3.1-8B-Instruct); check the license for commercial terms.

### What are Llama 3.1-8B's strengths and weaknesses?

Relative to other ranked models, Llama 3.1-8B places best in long context, multilingual, instruction following and lowest in coding, knowledge, math.

### What is Llama 3.1-8B best at?

Its best category is agentic & tool use, where it ranks 131st on Noometry.

### Cite this page

Noometry. (2026). Llama 3.1-8B benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/llama-3-1-8b

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/llama-3-1-8b.md).