[whatmodel.sh](../../README.md) / Claude 1.3 # Claude 1.3 Anthropic · released 2023-05 · proprietary **score 48.7** ±1.3 · comparison only: no current run option verified · from 15 benchmark results across 7 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Claude 1.3 among every model on that task or test, worst to best. 
task    vs the others          rank  counts  tests
coding  ▁▁▁▃▄●█▇▅▄▃▁   #422 of 539*     11%      1
math    ▁▁▃▆█●▅█▃▁▁▁    #259 of 530     30%      1
coding · #422 of 539 · 11% of its score Beats [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [Qwen3-VL 2B Thinking](qwen3-vl-2b-thinking.md) here, though they rank higher overall. Trails [Code Tulu 2 13B](code-tulu-2-13b.md), [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md) here, though they rank lower overall. 
test               result  vs the others in its run    difficulty      counts  why
HumanEval (maker)   56.0%  ▁▁▁▁▂▂▆●█▅▂▁ beats 107/205  easy · avg 71%     11%  sharp · at its level
math · #259 of 530 · 30% of its score Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md) here, though they rank higher overall. Trails [Phi-2 (2.7B)](phi-2.md), [Granite 8B Code Base (4K)](granite-8b-code-base-4k.md), [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md) here, though they rank lower overall. 
test   result  vs the others in its run    difficulty           counts  why
GSM8K   81.8%  ▂▂▂▂▃▄▅▇█●▆▄ beats 154/206  very easy · avg 90%     30%  sharp · several runs
other tests (11) 
test                                result  vs the others in its run    difficulty           counts  why
ARC Challenge (maker)                90.0%  ▄▁▁▂▁▄█▇▃▂▃● beats 136/140  very easy · avg 90%     10%  sharp · small run
MMLU (maker)                         77.0%  ▁▁▁▁▁▂▂▅█●▆▄ beats 158/214  easy · avg 81%          10%  at its level · small run
BIG-Bench Hard                       67.3%  ▁▁▁▁▂▄▅▆●▃▃▁ beats 106/155  medium · avg 69%       9.4%  sharp · at its level · small run
clembench v1.0 clemscore (self-pl…   37.1%  ▄······▄·●·▄ beats 3/4      medium · avg 38%       7.7%  sharp · at its level
TriviaQA (maker)                     86.7%  ▁▁▂▂▄▄▃▆▅█▅● beats 79/80    easy · avg 85%         4.8%  small run
ZeroSCROLLS Qasper (zero-shot F1)    52.3%  ▂▂··▂·▂··▂●▄ beats 8/10     medium · avg 56%       4.2%  blunt · at its level
ZeroSCROLLS QuALITY (zero-shot ac…   84.8%  ▃····▃·▃▃█▅● beats 9/10     easy · avg 81%         3.7%  at its level
SummEdits factual-consistency det…   59.8%  ▄····▄·●···▄ beats 2/4      hard · avg 65%         3.6%  at its level · noisy
ZeroSCROLLS NarrativeQA (zero-sho…   32.6%  ▃··▃·█▃·█▃·● beats 10/10    hard · avg 29%         2.3%  blunt · at its level
ConvRe Re2Text, normal relation, …   89.7%  ▂▂▄··▂▂█●··▂ beats 10/12    easy · avg 90%         1.3%  noisy
ZeroSCROLLS MuSiQue (zero-shot F1)   36.1%  ▄·█··█●·▄▄▄▄ beats 6/10     medium · avg 43%       0.6%  blunt · at its level
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
Anthropic system cards and model announ…        5   49% ▇▇▇▇▇▇
Chain-of-Thought Hub: A Continuous Effo…        2   16% ▇▇
LLMLingua: Compressing Prompts for Acce…        1   11% ▇
ZeroSCROLLS: A Zero-Shot Benchmark for …        4   11% ▇
clembench: Using Game Play to Evaluate …        1    8% ▇
SummEdits: Measuring LLM Ability at Fac…        1    4% ▇
An Investigation of LLMs' Inefficacy in…        1    1% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 48.7
Licences: Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance ([CC BY 4.0](../../licenses/paper-2305.17306v1-CC-BY-4-0.txt)) · LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-825-CC-BY-4-0.txt)) · ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding ([CC BY 4.0](../../licenses/acl-2023-findings-emnlp-536-CC-BY-4-0.txt)) · clembench: Using Game Play to Evaluate Chat-Optimized Language Models as Conversational Agents ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-689-CC-BY-4-0.txt)) · SummEdits: Measuring LLM Ability at Factual Reasoning Through The Lens of Summarization ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-600-CC-BY-4-0.txt)) · An Investigation of LLMs' Inefficacy in Understanding Converse Relations ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-429-CC-BY-4-0.txt)) ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/claude-1-3) · [JSON](https://whatmodel.sh/api?format=json&all=1)