reasoning benchmark
DeepSearchQA Leaderboard
DeepSearchQA is a benchmark for evaluating deep search and question-answering capabilities, testing models' ability to perform multi-hop reasoning and information retrieval across complex knowledge domains.
Updated Aug 17, 2026
Models9
Model coverage9
MetricScore
EvidenceB
DeepSearchQA Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score95.0% | Percentile100.0% | Participants9 | EvidenceC | Evaluated |
| Rank02 | ModelAN | Score93.1% | Percentile87.5% | Participants9 | EvidenceC | Evaluated |
| Rank03 | ModelAN | Score91.3% | Percentile75.0% | Participants9 | EvidenceC | Evaluated |
| Rank04 | ModelTE | Score91.0% | Percentile62.5% | Participants9 | EvidenceC | Evaluated |
| Rank05 | ModelXI | Score86.7% | Percentile50.0% | Participants9 | EvidenceC | Evaluated |
| Rank06 | ModelMA | Score83.0% | Percentile37.5% | Participants9 | EvidenceC | Evaluated |
| Rank07 | ModelMA | Score77.1% | Percentile25.0% | Participants9 | EvidenceC | Evaluated |
| Rank08 | ModelME | Score74.8% | Percentile12.5% | Participants9 | EvidenceC | Evaluated |
| Rank09 | ModelME | Score74.6% | Percentile0.0% | Participants9 | EvidenceC | Evaluated |
DeepSearchQA Highlights
The leading models and scores on this benchmark.
DeepSearchQA Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for DeepSearchQA
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis deepsearchqa AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01MAKimi K3Moonshot AI
- Score
- 95.0%
- Price
- $3.0 input / $15 output per 1M tokens
- Speed
- Up to 26 tok/s via Fireworks
Strengths
- Ranks #1 of 9 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures DeepSearchQA, not total model capability
- 02ANClaude Opus 4.8Anthropic
- Score
- 93.1%
- Price
- $5.0 input / $25 output per 1M tokens
- Speed
- Up to 46 tok/s via Anthropic
Strengths
- Ranks #2 of 9 compared models
- 88th percentile on this benchmark
- C evidence result
Considerations
- This result measures DeepSearchQA, not total model capability
- 03ANClaude Opus 4.6Anthropic
- Score
- 91.3%
- Price
- $5.0 input / $25 output per 1M tokens
- Speed
- Up to 17 tok/s via Anthropic
Strengths
- Ranks #3 of 9 compared models
- 75th percentile on this benchmark
- C evidence result
Considerations
- This result measures DeepSearchQA, not total model capability
- 04TEHy3Tencent
- Score
- 91.0%
Strengths
- Ranks #4 of 9 compared models
- 63th percentile on this benchmark
- C evidence result
Considerations
- This result measures DeepSearchQA, not total model capability
- 05XIMiMo-V2-ProXiaomi
- Score
- 86.7%
- Price
- $0.43 input / $0.87 output per 1M tokens
Strengths
- Ranks #5 of 9 compared models
- 50th percentile on this benchmark
- C evidence result
Considerations
- This result measures DeepSearchQA, not total model capability
Selection summary
Best AI Models for DeepSearchQA
Kimi K3 currently leads DeepSearchQA with 95.0%. 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.
What is DeepSearchQA?
What DeepSearchQA measures and how its scores work.
DeepSearchQA is a benchmark for evaluating deep search and question-answering capabilities, testing models' ability to perform multi-hop reasoning and information retrieval across complex knowledge domains.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- DeepSearchQA
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- deepsearchqa|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about DeepSearchQA.
Which model scores highest on DeepSearchQA?
Kimi K3 is currently ranked first with 95.0%.
What does DeepSearchQA measure?
DeepSearchQA is a benchmark for evaluating deep search and question-answering capabilities, testing models' ability to perform multi-hop reasoning and information retrieval across complex knowledge domains.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
9 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.