reasoning benchmark
Natural2Code Leaderboard
NaturalCodeBench (NCB) is a challenging code benchmark designed to mirror the complexity and variety of real-world coding tasks. It comprises 402 high-quality problems in Python and Java, selected from natural user queries from online coding services, covering 6 different domains.
Updated Aug 17, 2026
Models8
Model coverage8
MetricScore
EvidenceB
Natural2Code Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelGO | Score92.9% | Percentile100.0% | Participants8 | EvidenceC | Evaluated |
| Rank02 | ModelGO | Score85.4% | Percentile85.7% | Participants8 | EvidenceC | Evaluated |
| Rank03 | ModelGO | Score84.5% | Percentile71.4% | Participants8 | EvidenceC | Evaluated |
| Rank04 | ModelGO | Score80.7% | Percentile57.1% | Participants8 | EvidenceC | Evaluated |
| Rank05 | ModelGO | Score79.8% | Percentile42.9% | Participants8 | EvidenceC | Evaluated |
| Rank06 | ModelGO | Score75.5% | Percentile28.6% | Participants8 | EvidenceC | Evaluated |
| Rank07 | ModelGO | Score70.3% | Percentile14.3% | Participants8 | EvidenceC | Evaluated |
| Rank08 | ModelGO | Score56.0% | Percentile0.0% | Participants8 | EvidenceC | Evaluated |
Natural2Code Highlights
The leading models and scores on this benchmark.
Natural2Code Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for Natural2Code
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis natural2code 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.
- 01GOGemini 2.0 FlashGoogle
- Score
- 92.9%
- Speed
- Up to 183 tok/s via Google
Strengths
- Ranks #1 of 8 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures Natural2Code, not total model capability
- 02GOGemini 1.5 ProGoogle
- Score
- 85.4%
- Speed
- Up to 85 tok/s via Google
Strengths
- Ranks #2 of 8 compared models
- 86th percentile on this benchmark
- C evidence result
Considerations
- This result measures Natural2Code, not total model capability
- 03GOGemma 3 27BGoogle
- Score
- 84.5%
- Speed
- Up to 33 tok/s via DeepInfra
Strengths
- Ranks #3 of 8 compared models
- 71th percentile on this benchmark
- C evidence result
Considerations
- This result measures Natural2Code, not total model capability
- 04GOGemma 3 12BGoogle
- Score
- 80.7%
- Speed
- Up to 33 tok/s via DeepInfra
Strengths
- Ranks #4 of 8 compared models
- 57th percentile on this benchmark
- C evidence result
Considerations
- This result measures Natural2Code, not total model capability
- 05GOGemini 1.5 FlashGoogle
- Score
- 79.8%
- Speed
- Up to 150 tok/s via Google
Strengths
- Ranks #5 of 8 compared models
- 43th percentile on this benchmark
- C evidence result
Considerations
- This result measures Natural2Code, not total model capability
Selection summary
Best AI Models for Natural2Code
Gemini 2.0 Flash currently leads Natural2Code with 92.9%. 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 Natural2Code?
What Natural2Code measures and how its scores work.
NaturalCodeBench (NCB) is a challenging code benchmark designed to mirror the complexity and variety of real-world coding tasks. It comprises 402 high-quality problems in Python and Java, selected from natural user queries from online coding services, covering 6 different domains.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- Natural2Code
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- natural2code|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about Natural2Code.
Which model scores highest on Natural2Code?
Gemini 2.0 Flash is currently ranked first with 92.9%.
What does Natural2Code measure?
NaturalCodeBench (NCB) is a challenging code benchmark designed to mirror the complexity and variety of real-world coding tasks. It comprises 402 high-quality problems in Python and Java, selected from natural user queries from online coding services, covering 6 different domains.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
8 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.