Jamba 1.5 Large

Leaderboards

Where Jamba 1.5 Large sits among the top 20 in each category.

By task

Scored from votes on each kind of prompt alone, on the same 1,000-point scale.

Test results

Every test Jamba 1.5 Large has a published result on, out of 100%, with its rank among the models that took it.

• GPQA

  #232 / 256

  Graduate-level science questions written to be hard to look up.

  36.9%

  Self-reported

• MMLU-Pro

  #122 / 142

  Broad knowledge and reasoning across 14 subjects, ten answer choices each.

  53.5%

  Self-reported

• MMLU

  #55 / 103

  Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains

  81.2%

  Self-reported

• GSM8k

  #33 / 48

  Grade School Math 8K, a dataset of 8.5K high-quality linguistically diverse grade school math word problems requiring multi-step reasoning and elementary arithmetic operations.

  87.0%

  Self-reported

• ARC-C

  #7 / 35

  The AI2 Reasoning Challenge (ARC) Challenge Set is a multiple-choice question-answering benchmark containing grade-school level science questions that require advanced reasoning capabilities. ARC-C specifically contains questions that were answered incorrectly by both retrieval-based and word co-occurrence algorithms, making it a particularly challenging subset designed to test commonsense reasoning abilities in AI systems.

  93.0%

  Self-reported

• Arena Hard

  #13 / 27

  Arena-Hard-Auto is an automatic evaluation benchmark for instruction-tuned LLMs consisting of 500 challenging real-world prompts curated by BenchBuilder. It includes open-ended software engineering problems, mathematical questions, and creative writing tasks. The benchmark uses LLM-as-a-Judge methodology with GPT-4.1 and Gemini-2.5 as automatic judges to approximate human preference. Arena-Hard achieves 98.6% correlation with human preference rankings and provides 3x higher separation of model performances compared to MT-Bench, making it highly effective for distinguishing between models of similar quality.

  65.4%

  Self-reported

• TruthfulQA

  #9 / 18

  TruthfulQA is a benchmark to measure whether language models are truthful in generating answers to questions. It comprises 817 questions that span 38 categories, including health, law, finance and politics. The questions are crafted such that some humans would answer falsely due to a false belief or misconception, testing models' ability to avoid generating false answers learned from human texts.

  58.3%

  Self-reported

• Wild Bench

  #7 / 8

  WildBench is an automated evaluation framework that benchmarks large language models using 1,024 challenging, real-world tasks selected from over one million human-chatbot conversation logs. It introduces two evaluation metrics (WB-Reward and WB-Score) that achieve high correlation with human preferences and uses task-specific checklists for systematic evaluation.

  48.5%

  Self-reported