Xing4.0 Card - IFBench: leaderboard

Use source-specific boards; detailed task footnotes in retained README. Do not replace task-protocol differences by generic benchmark fusions. Retain exact comparator names, unreported reasoning modes unknown. - **SWE-bench Verified**: We evaluate Xing4.0-29B-A4B using the `SWE-agent` harness with `temperature=1.0, top_p=0.95, repetition_penalty=1.05` and a `210`K context window. - **Terminal-Bench 2.1**: We evaluate in `terminus-2` with `temperature=0.8, top_p=0.95, repetition_penalty=1.05, max_tokens=64K` and a `24`-hour timeout. We report the average score over `3` runs. - **SWE-bench Multilingual**: We evaluate Xing4.0-29B-A4B using the `SWE-agent` harness with `temperature=1.0, top_p=0.95, repetition_penalty=1.05` and a `210`K context window. - **Claw-Eval**: We evaluate using the [official harness](https://github.com/claw-eval/claw-eval) with `temperature=0.8, top_p=0.95, repetition_penalty=1.05, max_tokens=16384` and a 256K context window. We report the average score over `3` runs. - **Tau3-Bench**: We evaluate using the [official harness](https://github.com/sierra-research/tau-bench) with `temperature=0.8, top_p=0.95, repetition_penalty=1.05, max_tokens=16384`. We report the average pass^1 over `4` runs. - **DeepresearchBII**: We evaluate using the `OpenCode` harness with `temperature=0.8, top_p=0.95, repetition_penalty=1.05, max_tokens=32768` and a `256`K context window, with Exa MCP server enabled. - **AIME2026**: We evaluate with `temperature=1.0, top_p=0.95, repetition_penalty=1.05, max_tokens=131072`. We report the average score over 5 independent runs. - **IFBench**: We evaluate with `temperature=0.8, top_p=0.95, repetition_penalty=1.05, max_tokens=81920`. - **AA.LCR**: We evaluate with `temperature=1.0, top_p=0.95, repetition_penalty=1.05, max_tokens=65536`. We report the average score over `3` runs. Metric: Score (%). Higher is better. Source: https://huggingface.co/XingChen-AGI/Xing4.0-29B-A4B/raw/main/README.md

Metric: Score (%). Source: huggingface.co. Saturation forecast: Around January 2027. 3 models tracked.

Top models

No result here: #3 Claude Opus 5.5, #5 GPT-6 Astra, #7 Gemini 4 Argon.

Interactive version: theaggregate.ai/benchmark?slug=xing4-0-card-ifbench · How It Works · Data refreshed daily, snapshot 2026-10-17.