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LLaMA 13B
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LLaMA 13B is an open-weights language model from Meta, released February 2023. Its published size is 13B parameters. Licence: llama-1-noncommercial-research.
Catalogue identity notes
Original LLaMA (v1) base model (Touvron et al. 2023), released by Meta to approved researchers under a non-commercial licence; no official public download exists, so no run command is listed (community re-uploads such as huggyllama/* are unofficial).
$ how it compares
The LLMs ranked either side of LLaMA 13B, out of 828 with a score, then more from Meta.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 755 |  Falcon 40B Instruct | 28±2 | local | ||
| 756 |  GPT-NeoX-20B | 28±1 | local | ||
| 757 | LLaMA 13B | 28±1 | comparison only | ||
| 758 |  Koala 13B | 28±1 | comparison only | ||
| 759 |  StarCoderBase 3B | 28±1 | local | ||
| more from Meta | |||||
| 24 | Muse Spark 1.3 | 77±1 | api $2.00/M | ||
| 40 | Muse Spark 1.2 | 75±1 | api $2.00/M | ||
| 43 | Muse Spark 1.1 | 75±1 | api $2.00/M | ||
$ what it's good at
coding▁▁▃▃▅▇█▇▅▄▃▁ #505 of 524 · 18% of its score · 2 tests
Beats Molmo 7B-D (0924), DeepSeek R1 Distill Qwen 1.5B, Tulu 2 7B here, though they rank higher overall. Trails Falcon3 1B Instruct, Granite 3.0 1B-A400M Instruct, StarCoderBase 3B here, though they rank lower overall.
chat & writing▃▁▁▃▅▄▆█▇▇▇▃ #462 of 463 · 24% of its score · 3 tests
Trails Gemma 3 270M IT, FastChat-T5 3B, SmolLM2 360M Instruct here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024) | 853.0 ✎ | ▃▁▂▁▃▇█▆▇▄▂▂ beats 1/95 | 8.3% |
| LMArena (text) | 975.0 ✎ | ▃▁▁▂▃▃▃▆▆▇█▃ beats 1/379 | 8.2% |
| MT-Bench (GPT-4 single-answer grading, LMSYS runs) | 2.6100 MT-Bench score (1-10) ✎ | ▆▂▂▆▆▄██▂▄▄▆ beats 0/28 | 7.2% |
math▁▃▃▅▇▅▅█▄▁▁▁ #454 of 462 * · 8.4% of its score · 2 tests
Beats Llama 2 13B Chat, DeepSeek-Coder 1.3B Base, StarCoderBase 7B here, though they rank higher overall. Trails Qwen2 0.5B Instruct, Granite 3.0 1B-A400M Instruct here, though they rank lower overall.
other tests12 not in a task · 50% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| BIG-Bench Hard | 37.1% ✎ | ███··█·█·█·█ beats 4/6 | 8.3% |
| MMLU +1 | 46.2% ✎ | ▄▄·▄···▄▄·██ beats 6/8 | 6.7% |
| BoolQ lab | 78.1% ✎ | ▁▄▅▆▂▆▄▄█▃▄▄ beats 36/58 | 4.8% |
| ARC Challenge lab | 52.7% ✎ | ▆▁▁▂▂▅▇█▄▃▃▂ beats 61/97 | 4.5% |
| Factual knowledge recall, Premium2k exact match (zero-shot) | 21.3% ✎ | ▅····▃▅·██·█ beats 6/13 | 4.3% |
| WinoGrande lab | 73.0% ✎ | ▁···▂▁▁▅█▇▃▁ beats 71/87 | 4.2% |
| HellaSwag lab | 79.2% ✎ | ▁▁▁▁▂▂▃▇█▆▅▃ beats 64/85 | 4.0% |
| Factual knowledge recall, Premium2k exact match (10-shot) | 41.2% ✎ | ▃▅▃··▅▃·█▃▃▅ beats 8/13 | 3.8% |
4 more tests
| test | result | vs the others in its run | counts |
|---|---|---|---|
| OpenBookQA lab | 56.4% ✎ | █▁▁▁▄▃▃▇▃▁▄▂ beats 51/65 | 3.1% |
| ARC Easy lab | 74.8% ✎ | ▁▂▃▂▃▅▇█▇▆▄▆ beats 51/65 | 2.7% |
| Social IQA lab | 50.4% ✎ | ▂·▂██▅▅▁▂▃▆▁ beats 33/51 | 1.6% |
| PIQA lab | 80.1% ✎ | ▁▁▁▃▃▄▇█▄▃▁▁ beats 68/83 | 1.5% |
$ where to run it
No verified API or download recorded yet · add one
$ how the score was worked out · LLaMA: Open and Efficient Fou… 47%, InstructEval (DeCLaRe Lab) RE… 21%, Polyrating: A Cost-Effective… 8% and 3 more
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 LLaMA: Open and Efficient Foundation Language Models — authors' own runs · lab-reported | 13 | 47% |
 InstructEval (DeCLaRe Lab) README results table | 3 | 21% |
| Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3) | 1 | 8% |
 LMArena leaderboard dataset | 1 | 8% |
| Towards a Holistic Evaluation of LLMs on Factual Knowledge Recall (arXiv:2404.16164v1) | 2 | 8% |
 LMSYS Chatbot Arena leaderboard Space: MT-Bench column of the published leaderboard table | 1 | 7% |
| prior · pull toward the middle | 0% | |
| weighted average | = 27.8 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json