reasoning benchmark
TriviaQA is a large-scale reading comprehension dataset with over 650K question-answer-evidence triples, including 95K question-answer pairs and independently gathered evidence documents.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score85.10% | Percentile100.00% | Participants18 | EvidenceC | Evaluated |
| Rank02 | ModelGO | Score83.70% | Percentile94.12% | Participants18 | EvidenceC | Evaluated |
| Rank03 | ModelXI | Score81.30% | Percentile88.24% | Participants18 | EvidenceC | Evaluated |
| Rank04 | ModelMA | Score80.50% | Percentile82.35% | Participants18 | EvidenceC | Evaluated |
| Rank05 | ModelMA | Score80.50% | Percentile76.47% | Participants18 | EvidenceC | Evaluated |
| Rank06 | ModelMA | Score80.32% | Percentile70.59% | Participants18 | EvidenceC | Evaluated |
| Rank07 | ModelIB | Score78.18% | Percentile64.71% | Participants18 | EvidenceC | Evaluated |
| Rank08 | ModelGO | Score76.60% | Percentile58.82% | Participants18 | EvidenceC | Evaluated |
| Rank09 | ModelMA | Score74.90% | Percentile52.94% | Participants18 | EvidenceC | Evaluated |
| Rank10 | ModelMA | Score74.90% | Percentile47.06% | Participants18 | EvidenceC | Evaluated |
| Rank11 | ModelMA | Score73.80% | Percentile41.18% | Participants18 | EvidenceC | Evaluated |
| Rank12 | ModelGO | Score70.20% | Percentile35.29% | Participants18 | EvidenceC | Evaluated |
| Rank13 | ModelGO | Score70.20% | Percentile29.41% | Participants18 | EvidenceC | Evaluated |
| Rank14 | ModelMA | Score68.10% | Percentile23.53% | Participants18 | EvidenceC | Evaluated |
| Rank15 | ModelMA | Score65.50% | Percentile17.65% | Participants18 | EvidenceC | Evaluated |
| Rank16 | ModelGO | Score60.80% | Percentile11.76% | Participants18 | EvidenceC | Evaluated |
| Rank17 | ModelGO | Score60.80% | Percentile5.88% | Participants18 | EvidenceC | Evaluated |
| Rank18 | ModelMA | Score59.20% | Percentile0.00% | Participants18 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
A closer view of the leading scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
What TriviaQA measures and how its scores work.
TriviaQA is a reasoning benchmark based on complex, compositional questions and evidence documents for answering them.
It reports a Score expressed as a ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about TriviaQA.
Kimi K2 Base is currently ranked first with 85.10%.
The current leaders are Kimi K2 Base (85.10%), Gemma 2 27B (83.70%), and MiMo-V2.5-Pro (81.30%).
Ministral 3 (14B Base 2512) has the lowest matched official input price at $0.10 input / $0.10 output per 1M tokens.
The fastest matched records are Mistral Small 3.1 24B Base (137.10 tok/s via Mistral AI), Mistral Small 3.1 24B Instruct (137.10 tok/s via Mistral AI), and Mistral NeMo Instruct (42.00 tok/s via Google).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It reports a Score expressed as a ratio.
Yes. Higher values rank better for this benchmark.
18 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis triviaqa AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Kimi K2 Base currently leads TriviaQA with 85.10%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.