SciCode
Scientist-curated research coding problems split into subproblems across natural sciences. Code checked against test cases.
Mistral cites 'SciCode-Verified' (pass@1, n=6). Kimi uses Artificial Analysis numbers. Test-set answers partly held out.
- Made by
- SciCode team
- Released
- July 2024
- Kind
- Scientific code
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Code
- Tasks
- All public
- Strongest model tested
- Kimi K3 #24 on the capability index
- Last in a launch post
- October 2026
- Last model added
- October 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
Since language models (LMs) now outperform average humans on many challenging tasks, it has become increasingly difficult to develop challenging, high-quality, and realistic evaluations. We address this issue by examining LMs' capabilities to generate code for solving real scientific research problems. Incorporating input from scientists and AI researchers in 16 diverse natural science sub-fields, including mathematics, physics, chemistry, biology, and materials science, we created a scientist-curated coding benchmark, SciCode. The problems in SciCode naturally factorize into multiple subproblems, each involving knowledge recall, reasoning, and code synthesis. In total, SciCode contains 338 subproblems decomposed from 80 challenging main problems. It offers optional descriptions specifying useful scientific background information and scientist-annotated gold-standard solutions and test cases for evaluation. Claude3.5-Sonnet, the best-performing model among those tested, can solve only 4.6% of the problems in the most realistic setting. We believe that SciCode demonstrates both contemporary LMs' progress towards becoming helpful scientific assistants and sheds light on the development and evaluation of scientific AI in the future.
Seed2.1 Pro leads with 59.8%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
o3-mini leads with 10.8% across all independent runs
Best score per model across the official leaderboard, Epoch AI’s own runs and any board Epoch AI copied. Each bar and row names its source.
Seed2.1 Pro leads with 59.8%, claims included
Best score per model from every source, including labs’ own numbers and claims Epoch AI collected from reports and papers. Labs pick their own settings, so a claim can beat an independent run of the same model.
- 59.8
Seed2.1 Pro
- 59.0
Gemini 3.1 Pro
- 58.9
Step 5 Preview
- 58.7 Kimi K3
- 58.5
Muse Spark Open
- 53.5
Qwen3.7-Max
- 52.2 Kimi K2.6
- 51.3
Qwen3.7-Plus
- 50.3
KAT-Coder-Pro V2.5
- 49.7 Beam 1.0
- 48.7 Kimi K2.5
- 48.7
Inkling-Small
- 48.5
Seed2.0 Pro
- 47.6
Intern-S2-397B
- 47.6
Solar Mini 4
- 47.1
Nemotron 3.5 Super VL
- 47.0
Qwen 3.6 Max (Preview)
- 47.0
MiniMax-M2.7
- 44.8 Kimi K2 Thinking
- 44.6
Nemotron 3 Ultra
- 44.4
MiniMax-M2.5
- 44.2
KAT-Coder-V2.5-Dev
- 43.6
Muse Glimmer
- 43.0
Gemma 4 31B IT
- 42.0
Nemotron 3 Super
- 41.7 GLM-4.5
- 41.0
Nemotron 3.5 Bolt 74B-A9B
- 41.0
MiniMax-M2.1
- 40.1
K-EXAONE 2.0
- 40.0
Gemma 4 26B A4B
- 38.8 Granite 4.2 30B
- 38.2
North Mini Code 1.0
- 38.0 Mercury 2
- 38.0
Command A+
- 38.0 Mercury 2.5
- 37.3 GLM-4.5-Air
- 36.8
Spark-X2.6-4B
- 36.4
Nemotron-Cascade 2 30B-A3B
- 36.0
MiniMax-M2
- 33.3
Nemotron 3 Nano 30B-A3B
- 32.7
Nemotron 3.5 Lightning 30B-A3B
- 32.7 LFM2.5-24B-A2B
- 32.0
Nemotron 3 Nano Omni 30B-A3B
- 28.5 LFM3-1.7B
- 28.3
Falcon H1R 7B
- 24.0
Gemma 4 E4B
- 21.0
Gemma 4 E2B
- 18.0 Mercury
- 17.6
Nemotron Nano 12B v2 VL
- 1
Seed2.1 Pro 59.8%
- 2
Gemini 3.1 Pro 59%
- 3
Step 5 Preview 58.9%
- 4 Kimi K3 58.7%
- 5
Muse Spark Open 58.5%
- 6
Qwen3.7-Max 53.5%
- 7 Kimi K2.6 52.2%
- 8
Qwen3.7-Plus 51.3%
- 9
KAT-Coder-Pro V2.5 50.3%
- 10 Beam 1.0 49.7%
- 11 Kimi K2.5 48.7%
- 12
Inkling-Small 48.7%
- 13
Seed2.0 Pro 48.5%
- 14
Intern-S2-397B 47.6%
- 15
Solar Mini 4 47.6%
- 16
Nemotron 3.5 Super VL 47.1%
- 17
Qwen 3.6 Max (Preview) 47%
- 18
MiniMax-M2.7 47%
- 19 Kimi K2 Thinking 44.8%
- 20
Nemotron 3 Ultra 44.6%
- 21
MiniMax-M2.5 44.4%
- 22
KAT-Coder-V2.5-Dev 44.2%
- 23
Muse Glimmer 43.6%
- 24
Gemma 4 31B IT 43%
- 25
Nemotron 3 Super 42%
- 26 GLM-4.5 41.7%
- 27
Nemotron 3.5 Bolt 74B-A9B 41%
- 28
MiniMax-M2.1 41%
- 29
K-EXAONE 2.0 40.1%
- 30
Gemma 4 26B A4B 40%
- 31 Granite 4.2 30B 38.8%
- 32
North Mini Code 1.0 38.2%
- 33 Mercury 2 38%
- 34
Command A+ 38%
- 35 Mercury 2.5 38%
- 36 GLM-4.5-Air 37.3%
- 37
Spark-X2.6-4B 36.8%
- 38
Nemotron-Cascade 2 30B-A3B 36.4%
- 39
MiniMax-M2 36%
- 40
Nemotron 3 Nano 30B-A3B 33.3%
- 41
Nemotron 3.5 Lightning 30B-A3B 32.7%
- 42 LFM2.5-24B-A2B 32.7%
- 43
Nemotron 3 Nano Omni 30B-A3B 32%
- 44 LFM3-1.7B 28.5%
- 45
Falcon H1R 7B 28.3%
- 46
Gemma 4 E4B 24%
- 47
Gemma 4 E2B 21%
- 48 Mercury 18%
- 49
Nemotron Nano 12B v2 VL 17.6%
Source: Labs’ model cards
- 10.8 o3-mini
- 7.70 o1-preview
- 4.60 Claude 3.5 Sonnet
- 4.60 Claude 3.5 Sonnet (October 2024)
- 4.60
DeepSeek-R1
- 3.10
DeepSeek-Coder-V2 236B
- 3.10
DeepSeek-V3
- 1.50 Claude 3 Opus
- 1.50 Claude 3 Sonnet
- 1.50 GPT-4 Turbo
- 1.50 GPT-4o
- 1.50
Gemini 1.5 Pro
- 1.50
Qwen2-72B
- 1.50
Llama 3.1-405B
- 1.50 o1-mini
- 0.0
Mixtral 8x22B
- 0.0
Llama 3-70B
- 0.0
Llama 3.1-70B
- 1 o3-mini low 10.8%
- 2 o1-preview 7.70%
- 3 Claude 3.5 Sonnet 4.60%
- 4 Claude 3.5 Sonnet (October 2024) 4.60%
- 5
DeepSeek-R1 4.60%
- 6
DeepSeek-Coder-V2 236B 3.10%
- 7
DeepSeek-V3 3.10%
- 8 Claude 3 Opus 1.50%
- 9 Claude 3 Sonnet 1.50%
- 10 GPT-4 Turbo Apr 2024 1.50%
- 11 GPT-4o May 2024 1.50%
- 12
Gemini 1.5 Pro May 2024 1.50%
- 13
Qwen2-72B 1.50%
- 14
Llama 3.1-405B 1.50%
- 15 o1-mini 1.50%
- 16
Mixtral 8x22B 0%
- 17
Llama 3-70B 0%
- 18
Llama 3.1-70B 0%
Source: Official SciCode leaderboard
- 59.8
Seed2.1 Pro
- 59.0
Gemini 3.1 Pro
- 58.9
Step 5 Preview
- 58.7 Kimi K3
- 58.5
Muse Spark Open
- 53.5
Qwen3.7-Max
- 52.2 Kimi K2.6
- 51.3
Qwen3.7-Plus
- 50.3
KAT-Coder-Pro V2.5
- 49.7 Beam 1.0
- 48.7 Kimi K2.5
- 48.7
Inkling-Small
- 48.5
Seed2.0 Pro
- 47.6
Intern-S2-397B
- 47.6
Solar Mini 4
- 47.1
Nemotron 3.5 Super VL
- 47.0
Qwen 3.6 Max (Preview)
- 47.0
MiniMax-M2.7
- 44.8 Kimi K2 Thinking
- 44.6
Nemotron 3 Ultra
- 44.4
MiniMax-M2.5
- 44.2
KAT-Coder-V2.5-Dev
- 43.6
Muse Glimmer
- 43.0
Gemma 4 31B IT
- 42.0
Nemotron 3 Super
- 41.7 GLM-4.5
- 41.0
Nemotron 3.5 Bolt 74B-A9B
- 41.0
MiniMax-M2.1
- 40.1
K-EXAONE 2.0
- 40.0
Gemma 4 26B A4B
- 38.8 Granite 4.2 30B
- 38.2
North Mini Code 1.0
- 38.0 Mercury 2
- 38.0
Command A+
- 38.0 Mercury 2.5
- 37.3 GLM-4.5-Air
- 36.8
Spark-X2.6-4B
- 36.4
Nemotron-Cascade 2 30B-A3B
- 36.0
MiniMax-M2
- 33.3
Nemotron 3 Nano 30B-A3B
- 32.7
Nemotron 3.5 Lightning 30B-A3B
- 32.7 LFM2.5-24B-A2B
- 32.0
Nemotron 3 Nano Omni 30B-A3B
- 28.5 LFM3-1.7B
- 28.3
Falcon H1R 7B
- 24.0
Gemma 4 E4B
- 21.0
Gemma 4 E2B
- 18.0 Mercury
- 17.6
Nemotron Nano 12B v2 VL
- 10.8 o3-mini
- 7.70 o1-preview
- 4.60 Claude 3.5 Sonnet
- 4.60 Claude 3.5 Sonnet (October 2024)
- 4.60
DeepSeek-R1
- 3.10
DeepSeek-Coder-V2 236B
- 3.10
DeepSeek-V3
- 1.50 Claude 3 Opus
- 1.50 Claude 3 Sonnet
- 1.50 GPT-4 Turbo
- 1.50 GPT-4o
- 1.50
Gemini 1.5 Pro
- 1.50
Qwen2-72B
- 1.50
Llama 3.1-405B
- 1.50 o1-mini
- 0.0
Mixtral 8x22B
- 0.0
Llama 3-70B
- 0.0
Llama 3.1-70B
- 1
Seed2.1 Pro 59.8%
- 2
Gemini 3.1 Pro 59%
- 3
Step 5 Preview 58.9%
- 4 Kimi K3 58.7%
- 5
Muse Spark Open 58.5%
- 6
Qwen3.7-Max 53.5%
- 7 Kimi K2.6 52.2%
- 8
Qwen3.7-Plus 51.3%
- 9
KAT-Coder-Pro V2.5 50.3%
- 10 Beam 1.0 49.7%
- 11 Kimi K2.5 48.7%
- 12
Inkling-Small 48.7%
- 13
Seed2.0 Pro 48.5%
- 14
Intern-S2-397B 47.6%
- 15
Solar Mini 4 47.6%
- 16
Nemotron 3.5 Super VL 47.1%
- 17
Qwen 3.6 Max (Preview) 47%
- 18
MiniMax-M2.7 47%
- 19 Kimi K2 Thinking 44.8%
- 20
Nemotron 3 Ultra 44.6%
- 21
MiniMax-M2.5 44.4%
- 22
KAT-Coder-V2.5-Dev 44.2%
- 23
Muse Glimmer 43.6%
- 24
Gemma 4 31B IT 43%
- 25
Nemotron 3 Super 42%
- 26 GLM-4.5 41.7%
- 27
Nemotron 3.5 Bolt 74B-A9B 41%
- 28
MiniMax-M2.1 41%
- 29
K-EXAONE 2.0 40.1%
- 30
Gemma 4 26B A4B 40%
- 31 Granite 4.2 30B 38.8%
- 32
North Mini Code 1.0 38.2%
- 33 Mercury 2 38%
- 34
Command A+ 38%
- 35 Mercury 2.5 38%
- 36 GLM-4.5-Air 37.3%
- 37
Spark-X2.6-4B 36.8%
- 38
Nemotron-Cascade 2 30B-A3B 36.4%
- 39
MiniMax-M2 36%
- 40
Nemotron 3 Nano 30B-A3B 33.3%
- 41
Nemotron 3.5 Lightning 30B-A3B 32.7%
- 42 LFM2.5-24B-A2B 32.7%
- 43
Nemotron 3 Nano Omni 30B-A3B 32%
- 44 LFM3-1.7B 28.5%
- 45
Falcon H1R 7B 28.3%
- 46
Gemma 4 E4B 24%
- 47
Gemma 4 E2B 21%
- 48 Mercury 18%
- 49
Nemotron Nano 12B v2 VL 17.6%
- 50 o3-mini low 10.8%
- 51 o1-preview 7.70%
- 52 Claude 3.5 Sonnet 4.60%
- 53 Claude 3.5 Sonnet (October 2024) 4.60%
- 54
DeepSeek-R1 4.60%
- 55
DeepSeek-Coder-V2 236B 3.10%
- 56
DeepSeek-V3 3.10%
- 57 Claude 3 Opus 1.50%
- 58 Claude 3 Sonnet 1.50%
- 59 GPT-4 Turbo Apr 2024 1.50%
- 60 GPT-4o May 2024 1.50%
- 61
Gemini 1.5 Pro May 2024 1.50%
- 62
Qwen2-72B 1.50%
- 63
Llama 3.1-405B 1.50%
- 64 o1-mini 1.50%
- 65
Mixtral 8x22B 0%
- 66
Llama 3-70B 0%
- 67
Llama 3.1-70B 0%
Sources: Labs’ model cards and official SciCode leaderboard
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 59.8% | Jun 2026 | Lab model card | |
| 2 | | 59% | Feb 2026 | Lab model card | |
| 3 | | 58.9% | Sep 2026 | Lab model card | |
| 4 | Kimi K3 | 58.7% | Jul 2026 | Lab model card | |
| 5 | | 58.5% | Oct 2026 | Lab model card | |
| 6 | | 53.5% | May 2026 | Lab model card | |
| 7 | Kimi K2.6 | 52.2% | Apr 2026 | Lab model card | |
| 8 | | 51.3% | Jun 2026 | Lab model card | |
| 9 | | 50.3% | Jul 2026 | Lab model card | |
| 10 | Beam 1.0 | 49.7% | Oct 2026 | Lab model card | |
| 11 | Kimi K2.5 | 48.7% | Jan 2026 | Lab model card | |
| 12 | | 48.7% | Jul 2026 | Lab model card | |
| 13 | | 48.5% | Feb 2026 | Lab model card | |
| 14 | | 47.6% | Sep 2026 | Lab model card | |
| 15 | | 47.6% | Oct 2026 | Lab model card | |
| 16 | | 47.1% | Oct 2026 | Lab model card | |
| 17 | | 47% | Apr 2026 | Lab model card | |
| 18 | | 47% | May 2026 | Lab model card | |
| 19 | Kimi K2 Thinking | 44.8% | Nov 2025 | Lab model card | |
| 20 | | 44.6% | Jun 2026 | Lab model card | |
| 21 | | 44.4% | Feb 2026 | Lab model card | |
| 22 | | 44.2% | Jul 2026 | Lab model card | |
| 23 | | 43.6% | Aug 2026 | Lab model card | |
| 24 | | 43% | Jul 2026 | Lab model card | |
| 25 | | 42% | Mar 2026 | Lab model card | |
| 26 | GLM-4.5 | 41.7% | Jul 2025 | Lab model card | |
| 27 | | 41% | Oct 2026 | Lab model card | |
| 28 | | 41% | Dec 2025 | Lab model card | |
| 29 | | 40.1% | Aug 2026 | Lab model card | |
| 30 | | 40% | Jul 2026 | Lab model card | |
| 31 | Granite 4.2 30B | 38.8% | Aug 2026 | Lab model card | |
| 32 | | 38.2% | Jun 2026 | Lab model card | |
| 33 | Mercury 2 | 38% | Feb 2026 | Lab model card | |
| 34 | | 38% | May 2026 | Lab model card | |
| 35 | Mercury 2.5 | 38% | Sep 2026 | Lab model card | |
| 36 | GLM-4.5-Air | 37.3% | Aug 2025 | Lab model card | |
| 37 | | 36.8% | Oct 2026 | Lab model card | |
| 38 | | 36.4% | Mar 2026 | Lab model card | |
| 39 | | 36% | Oct 2025 | Lab model card | |
| 40 | | 33.3% | Dec 2025 | Lab model card | |
| 41 | | 32.7% | Oct 2026 | Lab model card | |
| 42 | LFM2.5-24B-A2B | 32.7% | Oct 2026 | Lab model card | |
| 43 | | 32% | Apr 2026 | Lab model card | |
| 44 | LFM3-1.7B | 28.5% | Oct 2026 | Lab model card | |
| 45 | | 28.3% | Jan 2026 | Lab model card | |
| 46 | | 24% | Jul 2026 | Lab model card | |
| 47 | | 21% | Jul 2026 | Lab model card | |
| 48 | Mercury | 18% | Jun 2025 | Lab model card | |
| 49 | | 17.6% | Nov 2025 | Lab model card |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | o3-mini | 10.8% | low | Jan 2025 | Official leaderboard |
| 2 | o1-preview | 7.70% | Sep 2024 | Official leaderboard | |
| 3 | Claude 3.5 Sonnet | 4.60% | Jun 2024 | Official leaderboard | |
| 4 | Claude 3.5 Sonnet (October 2024) | 4.60% | Oct 2024 | Official leaderboard | |
| 5 | | 4.60% | Jan 2025 | Official leaderboard | |
| 6 | | 3.10% | Jun 2024 | Official leaderboard | |
| 7 | | 3.10% | Dec 2024 | Official leaderboard | |
| 8 | Claude 3 Opus | 1.50% | Feb 2024 | Official leaderboard | |
| 9 | Claude 3 Sonnet | 1.50% | Feb 2024 | Official leaderboard | |
| 10 | GPT-4 Turbo Apr 2024 | 1.50% | Apr 2024 | Official leaderboard | |
| 11 | GPT-4o May 2024 | 1.50% | May 2024 | Official leaderboard | |
| 12 | | 1.50% | May 2024 | Official leaderboard | |
| 13 | | 1.50% | Jun 2024 | Official leaderboard | |
| 14 | | 1.50% | Jul 2024 | Official leaderboard | |
| 15 | o1-mini | 1.50% | Sep 2024 | Official leaderboard | |
| 16 | | 0% | Apr 2024 | Official leaderboard | |
| 17 | | 0% | Apr 2024 | Official leaderboard | |
| 18 | | 0% | Jul 2024 | Official leaderboard |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 59.8% | Jun 2026 | Lab model card | |
| 2 | | 59% | Feb 2026 | Lab model card | |
| 3 | | 58.9% | Sep 2026 | Lab model card | |
| 4 | Kimi K3 | 58.7% | Jul 2026 | Lab model card | |
| 5 | | 58.5% | Oct 2026 | Lab model card | |
| 6 | | 53.5% | May 2026 | Lab model card | |
| 7 | Kimi K2.6 | 52.2% | Apr 2026 | Lab model card | |
| 8 | | 51.3% | Jun 2026 | Lab model card | |
| 9 | | 50.3% | Jul 2026 | Lab model card | |
| 10 | Beam 1.0 | 49.7% | Oct 2026 | Lab model card | |
| 11 | Kimi K2.5 | 48.7% | Jan 2026 | Lab model card | |
| 12 | | 48.7% | Jul 2026 | Lab model card | |
| 13 | | 48.5% | Feb 2026 | Lab model card | |
| 14 | | 47.6% | Sep 2026 | Lab model card | |
| 15 | | 47.6% | Oct 2026 | Lab model card | |
| 16 | | 47.1% | Oct 2026 | Lab model card | |
| 17 | | 47% | Apr 2026 | Lab model card | |
| 18 | | 47% | May 2026 | Lab model card | |
| 19 | Kimi K2 Thinking | 44.8% | Nov 2025 | Lab model card | |
| 20 | | 44.6% | Jun 2026 | Lab model card | |
| 21 | | 44.4% | Feb 2026 | Lab model card | |
| 22 | | 44.2% | Jul 2026 | Lab model card | |
| 23 | | 43.6% | Aug 2026 | Lab model card | |
| 24 | | 43% | Jul 2026 | Lab model card | |
| 25 | | 42% | Mar 2026 | Lab model card | |
| 26 | GLM-4.5 | 41.7% | Jul 2025 | Lab model card | |
| 27 | | 41% | Oct 2026 | Lab model card | |
| 28 | | 41% | Dec 2025 | Lab model card | |
| 29 | | 40.1% | Aug 2026 | Lab model card | |
| 30 | | 40% | Jul 2026 | Lab model card | |
| 31 | Granite 4.2 30B | 38.8% | Aug 2026 | Lab model card | |
| 32 | | 38.2% | Jun 2026 | Lab model card | |
| 33 | Mercury 2 | 38% | Feb 2026 | Lab model card | |
| 34 | | 38% | May 2026 | Lab model card | |
| 35 | Mercury 2.5 | 38% | Sep 2026 | Lab model card | |
| 36 | GLM-4.5-Air | 37.3% | Aug 2025 | Lab model card | |
| 37 | | 36.8% | Oct 2026 | Lab model card | |
| 38 | | 36.4% | Mar 2026 | Lab model card | |
| 39 | | 36% | Oct 2025 | Lab model card | |
| 40 | | 33.3% | Dec 2025 | Lab model card | |
| 41 | | 32.7% | Oct 2026 | Lab model card | |
| 42 | LFM2.5-24B-A2B | 32.7% | Oct 2026 | Lab model card | |
| 43 | | 32% | Apr 2026 | Lab model card | |
| 44 | LFM3-1.7B | 28.5% | Oct 2026 | Lab model card | |
| 45 | | 28.3% | Jan 2026 | Lab model card | |
| 46 | | 24% | Jul 2026 | Lab model card | |
| 47 | | 21% | Jul 2026 | Lab model card | |
| 48 | Mercury | 18% | Jun 2025 | Lab model card | |
| 49 | | 17.6% | Nov 2025 | Lab model card | |
| 50 | o3-mini | 10.8% | low | Jan 2025 | Official leaderboard |
| 51 | o1-preview | 7.70% | Sep 2024 | Official leaderboard | |
| 52 | Claude 3.5 Sonnet | 4.60% | Jun 2024 | Official leaderboard | |
| 53 | Claude 3.5 Sonnet (October 2024) | 4.60% | Oct 2024 | Official leaderboard | |
| 54 | | 4.60% | Jan 2025 | Official leaderboard | |
| 55 | | 3.10% | Jun 2024 | Official leaderboard | |
| 56 | | 3.10% | Dec 2024 | Official leaderboard | |
| 57 | Claude 3 Opus | 1.50% | Feb 2024 | Official leaderboard | |
| 58 | Claude 3 Sonnet | 1.50% | Feb 2024 | Official leaderboard | |
| 59 | GPT-4 Turbo Apr 2024 | 1.50% | Apr 2024 | Official leaderboard | |
| 60 | GPT-4o May 2024 | 1.50% | May 2024 | Official leaderboard | |
| 61 | | 1.50% | May 2024 | Official leaderboard | |
| 62 | | 1.50% | Jun 2024 | Official leaderboard | |
| 63 | | 1.50% | Jul 2024 | Official leaderboard | |
| 64 | o1-mini | 1.50% | Sep 2024 | Official leaderboard | |
| 65 | | 0% | Apr 2024 | Official leaderboard | |
| 66 | | 0% | Apr 2024 | Official leaderboard | |
| 67 | | 0% | Jul 2024 | Official leaderboard |
The benchmark's own leaderboard has 18 models. Switch the scores above to rank them together.
6 models have held number one in labs’ own numbers since its release in July 2024 3 models have held number one across all independent runs since its release in July 2024 9 models have held number one with claims included since its release in July 2024
Lab claims count from the day the card came out, independent runs from the model’s release. We draw one row each for the benchmark’s own leaderboard and what labs claim in their cards.
Sources: Official SciCode leaderboard and labs’ model cards
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Sources: Official SciCode leaderboard and labs’ model cards
17 labs put it in a launch post in the last six months
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| Oct 27, 2026 | LFM3-1.7B | 28.5 | SciCode |
| Oct 22, 2026 | 36.8 | SciCode | |
| Oct 15, 2026 | 58.5 | SciCode | |
| Oct 15, 2026 | 41 | SciCode | |
| Oct 15, 2026 | 47.1 | SciCode, subtask | |
| Oct 13, 2026 | LFM2.5-24B-A2B | 32.7 | SciCode |
| Oct 5, 2026 | Beam 1.0 | 49.7 | SciCode |
| Oct 1, 2026 | 47.6 | SciCode (Artificial Analysis score quoted by Upstage) | |
| Sep 20, 2026 | 58.9 | SciCode | |
| Sep 13, 2026 | 47.6 | SciCode (Agentic Scientific Coding) | |
| Sep 8, 2026 | Mercury 2.5 | 38 | SciCode |
| Aug 25, 2026 | Granite 4.2 30B | 38.8 | SciCode |
| Aug 11, 2026 | 32.6 | SciCode | |
| Aug 10, 2026 | 43.6 | SciCode | |
| Jul 31, 2026 | 37.4 | SciCode | |
| Jul 30, 2026 | 48.7 | SciCode | |
| Jul 23, 2026 | 44.2 | Scicode | |
| Jul 16, 2026 | Kimi K3 | 58.7 | SciCode |
| Jul 6, 2026 | 50.3 | SciCode (AA Coding Index category) | |
| Jun 23, 2026 | 59.8 | SciCode | |
| Jun 9, 2026 | 38.2 | SciCode | |
| Jun 4, 2026 | 44.6 | SciCode (subtask) | |
| Jun 1, 2026 | 51.3 | SciCode | |
| May 20, 2026 | 38 | SciCode (65 problems, 288 subproblems, with background) | |
| May 20, 2026 | 53.5 | SciCode | |
| Apr 20, 2026 | 47 | SciCode Research Coding | |
| Apr 20, 2026 | Kimi K2.6 | 52.2 | SciCode |
| Mar 19, 2026 | 36.4 | SciCode | |
| Mar 11, 2026 | 42 | SciCode (subtask) | |
| Feb 24, 2026 | Mercury 2 | 38 | SciCode |
| Feb 19, 2026 | 59 | SciCode | |
| Feb 14, 2026 | 48.5 | Scicode | |
| Feb 12, 2026 | 44.4 | SciCode | |
| Jan 27, 2026 | Kimi K2.5 | 48.7 | SciCode |
| Jan 5, 2026 | 28.3 | SciCode (sub-problem) | |
| Dec 23, 2025 | 41 | SciCode | |
| Dec 15, 2025 | 33.3 | SciCode (subtask) | |
| Nov 6, 2025 | Kimi K2 Thinking | 44.8 | SciCode, no tools |
| Oct 27, 2025 | 36 | SciCode (AA) | |
| Jul 28, 2025 | GLM-4.5 | 41.7 | SciCode |
| Jun 26, 2025 | Mercury | 18 | SciCode (% accuracy) |
26 system cards report it, from 10 labs
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Oct 19, 2026 | | 43.6 | SciCode, high, 39.2 xhigh (Table 2) | Capabilities |
| Oct 15, 2026 | | 47.1 | SciCode (subtask), problem accuracy 15.10 (Table 10) | Capabilities |
| Oct 15, 2026 | | 32.7 | SciCode (subtask), BF16 acc. (Table 22) | Capabilities |
| Oct 15, 2026 | | 41 | SciCode, released 24GB checkpoint (Table 7). BF16 44.49 | Capabilities |
| Aug 5, 2026 | | 40.1 | SciCode. The model card prints 37.4 (Table 6, Reasoning mode) | Capabilities |
| Jul 30, 2026 | | 48.7 | SciCode (5. Evaluations table) | Capabilities |
| Jul 27, 2026 | Kimi K3 technical report | 58.7 | SciCode, cited from Artificial Analysis (Table 2) | Capabilities |
| Jul 24, 2026 | | 24 | SciCode, thinking (Table 5) | Capabilities |
| Jul 24, 2026 | | 21 | SciCode, thinking (Table 5) | Capabilities |
| Jul 24, 2026 | | 43 | SciCode, thinking (Table 5) | Capabilities |
| Jul 24, 2026 | | 40 | SciCode, thinking (Table 5) | Capabilities |
| Jun 23, 2026 | | 59.8 | SciCode (Table 11) | Capabilities |
| Jun 9, 2026 | | 44.6 | SciCode (subtask) (Table 10) | Capabilities |
| May 26, 2026 | | 47 | SciCode, AA Index v4.0 protocol (Table 4) | Capabilities |
| May 26, 2026 | | 43 | SciCode, AA Index v4.0 protocol (Table 4) | Capabilities |
| Apr 27, 2026 | | 32 | SciCode, 1 run (Table 10) | Capabilities |
| Mar 19, 2026 | | 36.4 | SciCode (Table 1) | Capabilities |
| Mar 11, 2026 | | 42 | SciCode (subtask) (Table 5) | Capabilities |
| Feb 19, 2026 | | 59 | SciCode, printed as whole percent (results table, p.4) | Capabilities |
| Feb 14, 2026 | | 48.5 | Scicode (Table 11). Table 13 prints 52.1 | Capabilities |
| Jan 30, 2026 | Kimi K2.5 technical report | 48.7 | SciCode (Table 4) | Capabilities |
| Jan 5, 2026 | | 28.3 | SciCode sub-problem 28.3, main problem 3.9 (Table 5) | Capabilities |
| Dec 23, 2025 | | 33.3 | SciCode (subtask) (Table 3) | Capabilities |
| Nov 6, 2025 | | 17.6 | SciCode_subtask_accuracy, reasoning on, SFT stage 4 (Table 6). Problem accuracy 6.9 | Capabilities |
| Aug 8, 2025 | GLM-4.5 technical report | 41.7 | SciCode (Table 4) | Capabilities |
| Aug 8, 2025 | GLM-4.5-Air technical report | 37.3 | SciCode (Table 4) | Capabilities |
One logged change that moves scores
Changes by the maintainers, so a score from before a change may not compare with one from after it. Newest first, each with the maintainers' own source. The list may be incomplete. What we log.
- Harness changed July 24th, 2024
The SciCode maintainers added scientist-written background and an evaluation setting that supplies it. Scores from this setting measure performance with extra scientific guidance and differ from scores without that guidance. Source
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