Coding benchmark # FrontierSWE leaderboard > FrontierSWE results for 18 AI models, led by GPT-6 Astra at 65.5%. What the benchmark measures, who runs it, and a source for every score. - Canonical page: https://noometry.com/benchmarks/frontierswe - Last updated: 2026-10-10 - Title: FrontierSWE Leaderboard (October 2026): Scores by Model As of October 2026, GPT-6 Astra has the highest published FrontierSWE score on Noometry at 65.5%, out of 18 models with results. Last verified October 10, 2026 ## About FrontierSWE 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 FrontierSWE 1. GPT-6 Astra 65.5% 2. Claude Opus 5.5 62.3% 3. Claude Sonnet 5.5 61.9% 4. Claude Fable 5.1 56.3% 5. Gemini 4 Argon 55% 6. Claude Opus 5 52% 7. Claude Fable 5 47% 8. GPT-5.6 Sol 32.2% 9. GLM-5.3 30.2% 10. Grok 4.7 29.5% 11. Kimi K3 25.9% 12. Grok 4.6 25.3% 13. Gemini 3.7 Flash 20.3% 14. Gemini 3.8 Flash 19.6% 15. GLM-5.3-Flash 18.1% 16. 020406080 Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise) ## All results FrontierSWE results by model | # | Model | Provider | Score | Setting | Source | Date | | --- | --- | --- | --- | --- | --- | --- | | 1 | [GPT-6 Astra](https://noometry.com/models/gpt-6-astra) | [OpenAI](https://noometry.com/providers/openai) | 65.5% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 2 | [Claude Opus 5.5](https://noometry.com/models/claude-opus-5-5) | [Anthropic](https://noometry.com/providers/anthropic) | 62.3% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 3 | [Claude Sonnet 5.5](https://noometry.com/models/claude-sonnet-5-5) | [Anthropic](https://noometry.com/providers/anthropic) | 61.9% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 4 | [Claude Fable 5.1](https://noometry.com/models/claude-fable-5-1) | [Anthropic](https://noometry.com/providers/anthropic) | 56.3% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 5 | [Gemini 4 Argon](https://noometry.com/models/gemini-4-argon) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 55% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 6 | [Claude Opus 5](https://noometry.com/models/claude-opus-5) | [Anthropic](https://noometry.com/providers/anthropic) | 52% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 7 | [Claude Fable 5](https://noometry.com/models/claude-fable-5) | [Anthropic](https://noometry.com/providers/anthropic) | 47% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 8 | [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 32.2% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 9 | [GLM-5.3](https://noometry.com/models/glm-5-3) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 30.2% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 10 | [Grok 4.7](https://noometry.com/models/grok-4-7) | [xAI](https://noometry.com/providers/xai) | 29.5% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 11 | [Kimi K3](https://noometry.com/models/kimi-k3) | [Moonshot AI](https://noometry.com/providers/moonshot) | 25.9% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 12 | [Grok 4.6](https://noometry.com/models/grok-4-6) | [xAI](https://noometry.com/providers/xai) | 25.3% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 13 | [Gemini 3.7 Flash](https://noometry.com/models/gemini-3-7-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 20.3% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 14 | [Gemini 3.8 Flash](https://noometry.com/models/gemini-3-8-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 19.6% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 15 | [GLM-5.3-Flash](https://noometry.com/models/glm-5-3-flash) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 18.1% | max | [Epoch AI](https://epoch.ai/benchmarks) | | | 16 | [Qwen3.8 Max](https://noometry.com/models/qwen3-8-max) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 17.8% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 17 | [Muse Spark 1.2](https://noometry.com/models/muse-spark-1-2) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 12% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | | 18 | [Inkling](https://noometry.com/models/inkling) | [T Thinking Machines Lab](https://noometry.com/providers/thinking-machines) | 4.1% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | | ## Compare the leaders - [GPT-6 Astra vs Claude Opus 5.5](https://noometry.com/compare/claude-opus-5-5-vs-gpt-6-astra) - [GPT-6 Astra vs Claude Sonnet 5.5](https://noometry.com/compare/claude-sonnet-5-5-vs-gpt-6-astra) - [GPT-6 Astra vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gpt-6-astra) - [GPT-6 Astra vs Gemini 4 Argon](https://noometry.com/compare/gemini-4-argon-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 Opus 5.5 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-claude-opus-5-5) ## Other coding benchmarks - [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified) - [DeepSWE](https://noometry.com/benchmarks/deepswe) - [FrontierCode](https://noometry.com/benchmarks/frontiercode) - [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) - [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 FrontierSWE score? As of October 2026, GPT-6 Astra has the highest published FrontierSWE score on Noometry at 65.5%, out of 18 models with results. ### What is the best open-weight model on FrontierSWE? GLM-5.3 has the highest FrontierSWE accuracy among open-weight models at 30.2%, ranking 9 of 18 overall. ### Cite this page Noometry. (2026). FrontierSWE leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/frontierswe Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/frontierswe.md).