Meta, open weights

# Llama 3.2 1B

> Llama 3.2 1B by Meta, released September 2024. Ranked #354 of 354 with a Noometry Index of 20.1. API: $0.027 in / $0.20 out per M tokens. 60K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/llama-3-2-1b
- Last updated: 2026-10-10
- Title: Llama 3.2 1B Benchmarks, Price & Rank (October 2026)

Llama 3.2 1B by Meta ranks 354th of 354 ranked models on the Noometry Index as of October 2026, with a score of 20.1. Its strongest category is agentic & tool use, where it ranks 150th. API pricing starts at $0.027 per million input tokens and $0.20 per million output tokens, with a 60K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #354 of 354
- **Index score:** 20.1
- **Evidence:** Confirmed 22 results
- **Provider:** [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta)
- **Released:** September 24, 2024
- **Weights:** Open weights
- **Reasoning:** Unknown
- **Context window:** 60K
- **Max output:** 54K
- **Input price:** $0.027 / M
- **Output price:** $0.20 / M
- **Blended price:** $0.0705 / M
- **Output speed:** Not measured
- **Value:** #21 of 219
- **Knowledge cutoff:** Unknown
- **Input:** text
- **Hugging Face:** [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)

## Category scores

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

Llama 3.2 1B category scores

1.  Coding 21.1
2.  Agentic & Tool Use 14.6
3.  Reasoning 16.2
4.  Math 10.4
5.  Knowledge 7.2
6.  Multilingual 23.8
7.  Instruction Following 52.4
8.  Long Context 31.9
9.  Writing & Preference 21.3
10.  0204060

Llama 3.2 1B category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 21.1 | #338 | 3 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 14.6 | #150 | 2 |
| [Reasoning](https://noometry.com/best/reasoning) | 16.2 | #308 | 2 |
| [Math](https://noometry.com/best/math) | 10.4 | #313 | 2 |
| [Knowledge](https://noometry.com/best/knowledge) | 7.2 | #312 | 2 |
| [Multilingual](https://noometry.com/best/multilingual) | 23.8 | #292 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 52.4 | #290 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 31.9 | #274 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 21.3 | #310 | 4 |

## Strengths and weaknesses

Categories where Llama 3.2 1B places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Llama 3.2 1B: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Reasoning](https://noometry.com/best/reasoning) | 16.2 | −7.4 | #308 of 350, top 88% |
| [Long Context](https://noometry.com/best/long-context) | 31.9 | −9.0 | #274 of 296, top 93% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 52.4 | −18.9 | #290 of 305, top 96% |

### Weakest categories

Llama 3.2 1B: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 21.1 | −17.7 | #338 of 340, top 100% |
| [Knowledge](https://noometry.com/best/knowledge) | 7.2 | −30.1 | #312 of 314, top 100% |
| [Writing & Preference](https://noometry.com/best/writing) | 21.3 | −32.5 | #310 of 312, top 100% |

## Closest competitors

The models ranked just above and below Llama 3.2 1B. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Llama 3.2 1B
| 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-3-2-1b) |
| [DeepSeek LLM 67B](https://noometry.com/models/deepseek-llm-67b) | #347 | 24.9 | — | — | [Compare](https://noometry.com/compare/deepseek-llm-67b-vs-llama-3-2-1b) |
| [Llama 13b](https://noometry.com/models/llama-13b) | #348 | 24.4 | — | — | [Compare](https://noometry.com/compare/llama-13b-vs-llama-3-2-1b) |
| [Llama 2-70B](https://noometry.com/models/llama-2-70b) | #349 | 24.4 | — | — | [Compare](https://noometry.com/compare/llama-2-70b-vs-llama-3-2-1b) |
| [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-3-2-1b) |
| [Mistral 7B](https://noometry.com/models/mistral-7b) | #351 | 23.0 | $0.25 | — | [Compare](https://noometry.com/compare/llama-3-2-1b-vs-mistral-7b) |
| [Llama 3.1-8B](https://noometry.com/models/llama-3-1-8b) | #352 | 23.0 | $0.0575 | — | [Compare](https://noometry.com/compare/llama-3-1-8b-vs-llama-3-2-1b) |
| [Gemma 3 1B](https://noometry.com/models/gemma-3-1b) | #353 | 21.1 | — | — | [Compare](https://noometry.com/compare/gemma-3-1b-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 3.2 1B Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [BigCodeBench Instruct](https://noometry.com/benchmarks/bigcodebench-instruct) | 8.2% | #63 of 64, top 99% |  | [BigCodeBench](https://bigcode-bench.github.io/) | 2024-09-25 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1070 | #280 of 294, top 96% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [BigCodeBench Complete](https://noometry.com/benchmarks/bigcodebench-complete) | 11.3% | #65 of 66, top 99% |  | [BigCodeBench](https://bigcode-bench.github.io/) | 2024-09-25 |

### Agentic & Tool Use

Llama 3.2 1B Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 10.8% | #47 of 49, top 96% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| [BALROG](https://noometry.com/benchmarks/balrog) | 6.6% | #35 of 35, top 100% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Llama 3.2 1B Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 0% | #121 of 129, top 94% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1044 | #283 of 297, top 96% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 101.99 | #201 of 213, top 95% |  | [Epoch AI](https://epoch.ai/eci) | 2024-09-24 |

### Math

Llama 3.2 1B Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 0.6% | #171 of 173, top 99% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1086 | #269 of 285, top 95% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

Llama 3.2 1B Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 23.9% | #182 of 186, top 98% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1007 | #269 of 273, top 99% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multilingual

Llama 3.2 1B Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 973 | #292 of 297, top 99% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 959 | #283 of 285, top 100% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1014 | #225 of 231, top 98% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 941 | #282 of 283, top 100% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Llama 3.2 1B Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1031 | #286 of 298, top 96% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Llama 3.2 1B Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1050 | #279 of 291, top 96% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Llama 3.2 1B Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1055 | #286 of 297, top 97% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1033 | #284 of 295, top 97% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 200 | #115 of 115, top 100% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1030 | #281 of 295, top 96% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Llama 3.2 1B API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [openrouter](https://openrouter.ai/meta-llama/llama-3.2-1b-instruct) | $0.027 | $0.20 | — | 2026-10-10 |

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

## Compare Llama 3.2 1B

-   [Llama 3.2 1B vs Llama 3.1-405B](https://noometry.com/compare/llama-3-1-405b-vs-llama-3-2-1b)
-   [Llama 3.2 1B vs Gemma 3 1B](https://noometry.com/compare/gemma-3-1b-vs-llama-3-2-1b)
-   [Llama 3.2 1B vs Llama 3.1-8B](https://noometry.com/compare/llama-3-1-8b-vs-llama-3-2-1b)
-   [Llama 3.2 1B vs Mistral 7B](https://noometry.com/compare/llama-3-2-1b-vs-mistral-7b)
-   [Llama 3.2 1B vs GPT-3.5-turbo](https://noometry.com/compare/gpt-3-5-turbo-vs-llama-3-2-1b)
-   [Llama 3.2 1B vs Llama 2-70B](https://noometry.com/compare/llama-2-70b-vs-llama-3-2-1b)
-   [Llama 3.2 1B vs Llama 13b](https://noometry.com/compare/llama-13b-vs-llama-3-2-1b)
-   [Llama 3.2 1B vs GPT-6 Astra](https://noometry.com/compare/gpt-6-astra-vs-llama-3-2-1b)
-   [Llama 3.2 1B vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-llama-3-2-1b)
-   [Llama 3.2 1B vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-llama-3-2-1b)
-   [Llama 3.2 1B vs Kimi K3](https://noometry.com/compare/kimi-k3-vs-llama-3-2-1b)
-   [Llama 3.2 1B vs Grok 4.6](https://noometry.com/compare/grok-4-6-vs-llama-3-2-1b)
-   [Llama 3.2 1B vs Qwen3.8 Max](https://noometry.com/compare/llama-3-2-1b-vs-qwen3-8-max)
-   [Llama 3.2 1B vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-llama-3-2-1b)

## 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 3.2 1B?

Llama 3.2 1B by Meta ranks 354th of 354 ranked models on the Noometry Index as of October 2026, with a score of 20.1. Its strongest category is agentic & tool use, where it ranks 150th. API pricing starts at $0.027 per million input tokens and $0.20 per million output tokens, with a 60K-token context window.

### How much does Llama 3.2 1B cost?

Llama 3.2 1B costs $0.027 per million input tokens and $0.20 per million output tokens on openrouter.

### What is Llama 3.2 1B's context window?

Llama 3.2 1B accepts up to 60K tokens of input and can write up to 54K tokens in one response.

### Is Llama 3.2 1B open source?

Yes. Llama 3.2 1B's weights are downloadable from Hugging Face (meta-llama/Llama-3.2-1B-Instruct); check the license for commercial terms.

### What are Llama 3.2 1B's strengths and weaknesses?

Relative to other ranked models, Llama 3.2 1B places best in reasoning, long context, instruction following and lowest in coding, knowledge, writing & preference.

### What is Llama 3.2 1B best at?

Its best category is agentic & tool use, where it ranks 150th on Noometry.

### Cite this page

Noometry. (2026). Llama 3.2 1B benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/llama-3-2-1b

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/llama-3-2-1b.md).