long context benchmark
MMLongBench-Doc Leaderboard
MMLongBench-Doc evaluates long document understanding capabilities in vision-language models.
Updated Aug 17, 2026
Models5
Model coverage5
MetricScore
EvidenceB
MMLongBench-Doc Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score0.62 points | Percentile100.0% | Participants5 | EvidenceC | Evaluated |
| Rank02 | ModelAC | Score0.602 points | Percentile75.0% | Participants5 | EvidenceC | Evaluated |
| Rank03 | ModelAC | Score0.595 points | Percentile50.0% | Participants5 | EvidenceC | Evaluated |
| Rank04 | ModelAC | Score0.59 points | Percentile25.0% | Participants5 | EvidenceC | Evaluated |
| Rank05 | ModelAC | Score0.562 points | Percentile0.0% | Participants5 | EvidenceC | Evaluated |
MMLongBench-Doc Highlights
The leading models and scores on this benchmark.
MMLongBench-Doc Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for MMLongBench-Doc
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis mmlongbench-doc 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.
- 01ACQwen3.6 PlusAlibaba Cloud / Qwen Team
- Score
- 0.62 points
- Price
- $0.50 input / $3.0 output per 1M tokens
- Speed
- Up to 16 tok/s via Together
Strengths
- Ranks #1 of 5 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures MMLongBench-Doc, not total model capability
- 02ACQwen3.5-27BAlibaba Cloud / Qwen Team
- Score
- 0.602 points
- Price
- $0.30 input / $2.4 output per 1M tokens
- Speed
- Up to 6.7 tok/s via Novita
Strengths
- Ranks #2 of 5 compared models
- 75th percentile on this benchmark
- C evidence result
Considerations
- This result measures MMLongBench-Doc, not total model capability
- 03ACQwen3.5-35B-A3BAlibaba Cloud / Qwen Team
- Score
- 0.595 points
- Price
- $0.25 input / $2.0 output per 1M tokens
Strengths
- Ranks #3 of 5 compared models
- 50th percentile on this benchmark
- C evidence result
Considerations
- This result measures MMLongBench-Doc, not total model capability
- 04ACQwen3.5-122B-A10BAlibaba Cloud / Qwen Team
- Score
- 0.59 points
- Price
- $0.40 input / $3.2 output per 1M tokens
Strengths
- Ranks #4 of 5 compared models
- 25th percentile on this benchmark
- C evidence result
Considerations
- This result measures MMLongBench-Doc, not total model capability
- 05ACQwen3 VL 235B A22B ThinkingAlibaba Cloud / Qwen Team
- Score
- 0.562 points
Strengths
- Ranks #5 of 5 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures MMLongBench-Doc, not total model capability
Selection summary
Best AI Models for MMLongBench-Doc
Qwen3.6 Plus currently leads MMLongBench-Doc with 0.62 points. 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 MMLongBench-Doc?
What MMLongBench-Doc measures and how its scores work.
MMLongBench-Doc evaluates long document understanding capabilities in vision-language models.
Scores are shown in points. This benchmark is not independently verified and has an evidence level of B.
- Family
- MMLongBench-Doc
- Modality
- image
- Primary category
- long context
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- mmlongbench-doc|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about MMLongBench-Doc.
Which model scores highest on MMLongBench-Doc?
Qwen3.6 Plus is currently ranked first with 0.62 points.
What does MMLongBench-Doc measure?
MMLongBench-Doc evaluates long document understanding capabilities in vision-language models.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
5 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.