Coding benchmark

# FrontierCode leaderboard

> FrontierCode results for 37 AI models, led by Claude Opus 5.5 at 54.6%. What the benchmark measures, who runs it, and a source for every score.
- Canonical page: https://noometry.com/benchmarks/frontiercode
- Last updated: 2026-10-10
- Title: FrontierCode Leaderboard (October 2026): Scores by Model

As of October 2026, Claude Opus 5.5 has the highest published FrontierCode score on Noometry at 54.6%, out of 37 models with results.

Last verified October 10, 2026

## About FrontierCode

A description with primary sources is being prepared for this benchmark.

- **Category:** [Coding](https://noometry.com/best/coding)
- **Introduced:** 2026
- **Unit:** Percent (random guessing ≈ 0%)
- **Official site:** [epoch.ai](https://epoch.ai/benchmarks)

## Top 15 models

Top models on FrontierCode

1.  Claude Opus 5.5 54.6%
2.  Claude Fable 5 53.5%
3.  Claude Opus 5 53.4%
4.  GPT-6 Astra 53.3%
5.  Claude Sonnet 5.5 52.1%
6.  Claude Fable 5.1 50.9%
7.  GPT-6.1 Sol 50.2%
8.  GPT-6 Sol 49.3%
9.  Grok 4.6 48%
10.  Grok 4.7 47.6%
11.  GPT-5.6 Sol 47.5%
12.  Claude Opus 4.8 46.5%
13.  Kimi K3 44.2%
14.  Gemini 3.7 Flash 43.6%
15.  GPT-5.5 43%
16.  3540455055

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## All results

FrontierCode results by model
| # | Model | Provider | Score | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | [Claude Opus 5.5](https://noometry.com/models/claude-opus-5-5) | [Anthropic](https://noometry.com/providers/anthropic) | 54.6% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 2 | [Claude Fable 5](https://noometry.com/models/claude-fable-5) | [Anthropic](https://noometry.com/providers/anthropic) | 53.5% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 3 | [Claude Opus 5](https://noometry.com/models/claude-opus-5) | [Anthropic](https://noometry.com/providers/anthropic) | 53.4% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 4 | [GPT-6 Astra](https://noometry.com/models/gpt-6-astra) | [OpenAI](https://noometry.com/providers/openai) | 53.3% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 5 | [Claude Sonnet 5.5](https://noometry.com/models/claude-sonnet-5-5) | [Anthropic](https://noometry.com/providers/anthropic) | 52.1% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 6 | [Claude Fable 5.1](https://noometry.com/models/claude-fable-5-1) | [Anthropic](https://noometry.com/providers/anthropic) | 50.9% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 7 | [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol) | [OpenAI](https://noometry.com/providers/openai) | 50.2% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 8 | [GPT-6 Sol](https://noometry.com/models/gpt-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 49.3% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 9 | [Grok 4.6](https://noometry.com/models/grok-4-6) | [xAI](https://noometry.com/providers/xai) | 48% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 10 | [Grok 4.7](https://noometry.com/models/grok-4-7) | [xAI](https://noometry.com/providers/xai) | 47.6% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 11 | [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 47.5% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 12 | [Claude Opus 4.8](https://noometry.com/models/claude-opus-4-8) | [Anthropic](https://noometry.com/providers/anthropic) | 46.5% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 13 | [Kimi K3](https://noometry.com/models/kimi-k3) | [Moonshot AI](https://noometry.com/providers/moonshot) | 44.2% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 14 | [Gemini 3.7 Flash](https://noometry.com/models/gemini-3-7-flash) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 43.6% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 15 | [GPT-5.5](https://noometry.com/models/gpt-5-5) | [OpenAI](https://noometry.com/providers/openai) | 43% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 16 | [Claude Sonnet 5](https://noometry.com/models/claude-sonnet-5) | [Anthropic](https://noometry.com/providers/anthropic) | 42.7% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 17 | [Grok 4.5](https://noometry.com/models/grok-4-5) | [xAI](https://noometry.com/providers/xai) | 42.4% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 18 | [GPT-6 Luna](https://noometry.com/models/gpt-6-luna) | [OpenAI](https://noometry.com/providers/openai) | 42.4% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 19 | [GPT-5.6 Terra](https://noometry.com/models/gpt-5-6-terra) | [OpenAI](https://noometry.com/providers/openai) | 41.3% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 20 | [Gemini 3.8 Flash](https://noometry.com/models/gemini-3-8-flash) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 41.2% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 21 | [GLM-5.3](https://noometry.com/models/glm-5-3) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 40.1% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 22 | [GPT-5.6 Luna](https://noometry.com/models/gpt-5-6-luna) | [OpenAI](https://noometry.com/providers/openai) | 39.8% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 23 | [Claude Opus 4.7](https://noometry.com/models/claude-opus-4-7) | [Anthropic](https://noometry.com/providers/anthropic) | 38.5% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 24 | [Gemini 3.6 Flash](https://noometry.com/models/gemini-3-6-flash) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 34.4% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 25 | [GLM-5.3-Flash](https://noometry.com/models/glm-5-3-flash) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 31.8% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 26 | [Kimi K2.7 Code](https://noometry.com/models/kimi-k2-7-code) | [Moonshot AI](https://noometry.com/providers/moonshot) | 30.1% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 27 | [DeepSeek V4 Pro](https://noometry.com/models/deepseek-v4-pro) |  [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 28.6% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 28 | [GPT-5.4 mini](https://noometry.com/models/gpt-5-4-mini) | [OpenAI](https://noometry.com/providers/openai) | 27% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 29 | [Claude Opus 4.6](https://noometry.com/models/claude-opus-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 26.6% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 30 | [GLM-5.2](https://noometry.com/models/glm-5-2) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 24.5% | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 31 | [Claude Sonnet 4.6](https://noometry.com/models/claude-sonnet-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 24.3% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 32 | [DeepSeek V4 Flash](https://noometry.com/models/deepseek-v4-flash) |  [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 18.8% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 33 | [MiniMax-M3](https://noometry.com/models/minimax-m3) |  [![](/logos/minimax.svg) MiniMax](https://noometry.com/providers/minimax) | 14.7% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 34 | [Inkling](https://noometry.com/models/inkling) | [T Thinking Machines Lab](https://noometry.com/providers/thinking-machines) | 14% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 35 | [Nemotron 3 Ultra](https://noometry.com/models/nemotron-3-ultra) |  [![](/logos/nvidia.svg) NVIDIA](https://noometry.com/providers/nvidia) | 13.6% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 36 | [Qwen3.7 Plus](https://noometry.com/models/qwen3-7-plus) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 10.2% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 37 | [Mistral Medium](https://noometry.com/models/mistral-medium) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 8% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

## Compare the leaders

-   [Claude Opus 5.5 vs Claude Fable 5](https://noometry.com/compare/claude-fable-5-vs-claude-opus-5-5)
-   [Claude Opus 5.5 vs Claude Opus 5](https://noometry.com/compare/claude-opus-5-vs-claude-opus-5-5)
-   [Claude Opus 5.5 vs GPT-6 Astra](https://noometry.com/compare/claude-opus-5-5-vs-gpt-6-astra)
-   [Claude Opus 5.5 vs Claude Sonnet 5.5](https://noometry.com/compare/claude-opus-5-5-vs-claude-sonnet-5-5)
-   [Claude Fable 5 vs Claude Opus 5](https://noometry.com/compare/claude-fable-5-vs-claude-opus-5)
-   [Claude Fable 5 vs GPT-6 Astra](https://noometry.com/compare/claude-fable-5-vs-gpt-6-astra)

## Other coding benchmarks

-   [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified)
-   [DeepSWE](https://noometry.com/benchmarks/deepswe)
-   [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only)
-   [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot)
-   [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev)
-   [CursorBench](https://noometry.com/benchmarks/cursorbench)
-   [SWE-bench Multilingual](https://noometry.com/benchmarks/swe-bench-multilingual)
-   [FrontierSWE](https://noometry.com/benchmarks/frontierswe)
-   [SciCode](https://noometry.com/benchmarks/scicode)
-   [GSO](https://noometry.com/benchmarks/gso-bench)
-   [WeirdML](https://noometry.com/benchmarks/weirdml)
-   [LMArena Coding](https://noometry.com/benchmarks/arena-coding)

## Frequently asked questions

### Which model has the highest FrontierCode score?

As of October 2026, Claude Opus 5.5 has the highest published FrontierCode score on Noometry at 54.6%, out of 37 models with results.

### What is the best open-weight model on FrontierCode?

Kimi K3 has the highest FrontierCode accuracy among open-weight models at 44.2%, ranking 13 of 37 overall.

### Cite this page

Noometry. (2026). FrontierCode leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/frontiercode

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/frontiercode.md).