reasoning benchmark
FullStackBench en Leaderboard
English subset of FullStackBench for evaluating end-to-end software engineering and full-stack development capability.
Updated Aug 17, 2026
Models3
Model coverage3
MetricScore
EvidenceB
FullStackBench en Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score62.6% | Percentile100.0% | Participants3 | EvidenceC | Evaluated |
| Rank02 | ModelAC | Score60.1% | Percentile50.0% | Participants3 | EvidenceC | Evaluated |
| Rank03 | ModelAC | Score58.1% | Percentile0.0% | Participants3 | EvidenceC | Evaluated |
FullStackBench en Highlights
The leading models and scores on this benchmark.
FullStackBench en Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for FullStackBench en
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis fullstackbench en 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.
- 01ACQwen3.5-122B-A10BAlibaba Cloud / Qwen Team
- Score
- 62.6%
- Price
- $0.40 input / $3.2 output per 1M tokens
Strengths
- Ranks #1 of 3 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures FullStackBench en, not total model capability
- 02ACQwen3.5-27BAlibaba Cloud / Qwen Team
- Score
- 60.1%
- Price
- $0.30 input / $2.4 output per 1M tokens
- Speed
- Up to 6.7 tok/s via Novita
Strengths
- Ranks #2 of 3 compared models
- 50th percentile on this benchmark
- C evidence result
Considerations
- This result measures FullStackBench en, not total model capability
- 03ACQwen3.5-35B-A3BAlibaba Cloud / Qwen Team
- Score
- 58.1%
- Price
- $0.25 input / $2.0 output per 1M tokens
Strengths
- Ranks #3 of 3 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures FullStackBench en, not total model capability
Selection summary
Best AI Models for FullStackBench en
Qwen3.5-122B-A10B currently leads FullStackBench en with 62.6%. 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 FullStackBench en?
What FullStackBench en measures and how its scores work.
English subset of FullStackBench for evaluating end-to-end software engineering and full-stack development capability.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- FullStackBench en
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- fullstackbench-en|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about FullStackBench en.
Which model scores highest on FullStackBench en?
Qwen3.5-122B-A10B is currently ranked first with 62.6%.
What does FullStackBench en measure?
English subset of FullStackBench for evaluating end-to-end software engineering and full-stack development capability.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
3 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.