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@@ -49,10 +49,10 @@ We have conducted a comprehensive evaluation of Ling-3.0-flash across multiple a
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  <!-- 这是一张图片，ocr 内容为：DEEPSEEK CLAUDE- NEMOTRON- LING-3.0 GPT-5.4 STEP-3.7- V4-FLASH- RING-2.6-1T MINIMAX- 3-SUPER FLASH (HIGH) MINI (HIGH) FLASH (XHIGH) 120B-A12B (MAX) (MAX) 120B-A12B 124B-A5.1B 284B-A13B 198B-A11B 230B-A10B 1T-A63B SIZE CODING AGENT SWE-BENCH PRO 47.9 34.1 48.3 56.2 56.3 52.6 53.9 56.6 SWE-BENCH MULTILINGUAL 72.4 71.0 42.7 56.7 724 76.5 73.3 75.9 57.0 TERMINAL-BENCH 2.1 39.3 55.8 55.0 71.2 62.0 43.1 39.0 59.2 77.0 65.1 55.8 ARTIFACTSBENCH 66.8 64.0 51.6 68.7 25.3 19.2 MINIAPPENCH 28.0 14.8 46.3 20.7 58.8 5.8 52.2 ANTSWEBENCH 48.6 46.4 GENERAL AGENT 8.9 11.3 28.0 TAU3-BANKING-AA 11.3 10.1 22.9 14.6 30.5 55.2 53.6 69.0 65.5 MCP-ATLAS 52.6 61.2 49.4 66.7 53.5 24.9 20.3 44.8 54.4 28.4 44.8 11.9 SKILLSBENCH BFCL-V4 59.5 73.0 68.3 73.1 65.4 63.6 60.6 64.8 GDPVAL V2-AA 920 1377 1189 1017 1107 699 1159 SEARCH AGENT WIDESEARCH 75.2 70.2 62.2 73.6 19.5 74.4 56.8 79.5 72.2(W/ CTX) 74.0(W/CTX) BROWSECOMP 73.2 71.7 31.3 75.8 76.3 82.0(MA) 82.1(MA) DRACO 71.3 75.8 61.3 66.8 70.4 INSTRUCTION FOLLOWING 75.7 74.5 IFBENCH 72.6 67.3 79.2 69.0 44.6 56.6 91.4 93.6 93.3 SYSBENCH 86.5 93.9 90.7 86.2 94.9 77.3 71.3 LIFEBENCH 69.2 72.5 71.8 74.1 66.9 60.2 REASONING 91.7 94.2 AIME26 93.2 95.0 95.8 92.9 96.5 94.4 HMMT-FEB26 87.0 71.9 93.5 85.6 94.8 87.9 84.9 83.9 83.7 74.5 87.0 IMO-ANSWERBENCH 77.0 82.1 86.1 66.9 22.7 HLE 19.9 20.6 18.3 18.3 34.8 28.1 30.0 LIVECODEBENCH 82.8 78.1 80.8 87.0 75.6 83.7 78.7 91.6 (2408-2505) LONG CONTEXT & MULTI-TURN DIALOGUE 39.2 MRCR 128K 27.7 90.1 56.1 40.8 90.8 92.5 88.5 92.7 81.1 MRCR 256K 25.7 76.5 50.5 35.8 84.3 70.7 AA-LCR 65.1 64.3 68.7 63.7 63.0 58.3 63.4 89.3 MULTI-IF 86.2 87.7 82.9 84.6 84.8 82.3 84.6 -->
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  ![](https://intranetproxy.alipay.com/skylark/lark/0/2026/png/23157180/1785843565924-387dbbd7-90f8-4241-a58d-664cc5e6b486.png)
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- > + Thinking mode is enabled by default. Unless otherwise specified, the default parameters for Ling-3.0-flash are as follows: temperature=0.6, top_p=0.95, top_k=20.
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  > + <font style="color:rgb(16, 18, 20);">SWE-Bench Series:Evaluated using OpenHands as the agent harness with tailored prompts. Decoding uses </font>`temperature=0.6, top_p=0.95, max_new_tokens=32K`<font style="color:rgb(16, 18, 20);">, with a 256K context window.</font>
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  > + <font style="color:rgb(16, 18, 20);">Terminal-Bench 2.1: Evaluated under the Artificial Analysis (AA) protocol using the default Terminus 2 harness, a unified 2-hour timeout, the provided JSON parser in preserve-thinking mode, and 3 runs per task (mean). Decoding uses </font>`temperature=0.6, top_p=1.0, max_new_tokens=32K`<font style="color:rgb(16, 18, 20);">, with a 256K context window.</font>
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- > + <font style="color:rgb(16, 18, 20);">MiniAppBench: A 500-task coding benchmark evaluating whether models can turn a single user request into complete, usable interactive HTML apps in real-world application-generation scenarios. Evaluated with temperature=1.0, top_p=1.0, max_tokens=128K.
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  > + <font style="color:rgb(16, 18, 20);">AntSWEBench: </font><font style="color:rgb(0, 0, 0);">AntSWEBench is an internally used software engineering benchmark that covers mainstream programming languages such as Java, JavaScript, and Python, including various development scenarios like new feature, bug fix, and code refactoring.</font>
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  > + <font style="color:rgb(16, 18, 20);">Tau3-banking-AA: Aligned with the AA leaderboard, utilizing GPT-5.4-mini (medium reasoning) for both the user simulator and the natural-language assertion judge.</font>
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  > + MCP-Atlas: Evaluated on the 500-task public set using the official v1 harness with a 20-turn limit and Gemini-2.5-Pro as the claim-coverage judger.
 
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  <!-- 这是一张图片，ocr 内容为：DEEPSEEK CLAUDE- NEMOTRON- LING-3.0 GPT-5.4 STEP-3.7- V4-FLASH- RING-2.6-1T MINIMAX- 3-SUPER FLASH (HIGH) MINI (HIGH) FLASH (XHIGH) 120B-A12B (MAX) (MAX) 120B-A12B 124B-A5.1B 284B-A13B 198B-A11B 230B-A10B 1T-A63B SIZE CODING AGENT SWE-BENCH PRO 47.9 34.1 48.3 56.2 56.3 52.6 53.9 56.6 SWE-BENCH MULTILINGUAL 72.4 71.0 42.7 56.7 724 76.5 73.3 75.9 57.0 TERMINAL-BENCH 2.1 39.3 55.8 55.0 71.2 62.0 43.1 39.0 59.2 77.0 65.1 55.8 ARTIFACTSBENCH 66.8 64.0 51.6 68.7 25.3 19.2 MINIAPPENCH 28.0 14.8 46.3 20.7 58.8 5.8 52.2 ANTSWEBENCH 48.6 46.4 GENERAL AGENT 8.9 11.3 28.0 TAU3-BANKING-AA 11.3 10.1 22.9 14.6 30.5 55.2 53.6 69.0 65.5 MCP-ATLAS 52.6 61.2 49.4 66.7 53.5 24.9 20.3 44.8 54.4 28.4 44.8 11.9 SKILLSBENCH BFCL-V4 59.5 73.0 68.3 73.1 65.4 63.6 60.6 64.8 GDPVAL V2-AA 920 1377 1189 1017 1107 699 1159 SEARCH AGENT WIDESEARCH 75.2 70.2 62.2 73.6 19.5 74.4 56.8 79.5 72.2(W/ CTX) 74.0(W/CTX) BROWSECOMP 73.2 71.7 31.3 75.8 76.3 82.0(MA) 82.1(MA) DRACO 71.3 75.8 61.3 66.8 70.4 INSTRUCTION FOLLOWING 75.7 74.5 IFBENCH 72.6 67.3 79.2 69.0 44.6 56.6 91.4 93.6 93.3 SYSBENCH 86.5 93.9 90.7 86.2 94.9 77.3 71.3 LIFEBENCH 69.2 72.5 71.8 74.1 66.9 60.2 REASONING 91.7 94.2 AIME26 93.2 95.0 95.8 92.9 96.5 94.4 HMMT-FEB26 87.0 71.9 93.5 85.6 94.8 87.9 84.9 83.9 83.7 74.5 87.0 IMO-ANSWERBENCH 77.0 82.1 86.1 66.9 22.7 HLE 19.9 20.6 18.3 18.3 34.8 28.1 30.0 LIVECODEBENCH 82.8 78.1 80.8 87.0 75.6 83.7 78.7 91.6 (2408-2505) LONG CONTEXT & MULTI-TURN DIALOGUE 39.2 MRCR 128K 27.7 90.1 56.1 40.8 90.8 92.5 88.5 92.7 81.1 MRCR 256K 25.7 76.5 50.5 35.8 84.3 70.7 AA-LCR 65.1 64.3 68.7 63.7 63.0 58.3 63.4 89.3 MULTI-IF 86.2 87.7 82.9 84.6 84.8 82.3 84.6 -->
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  ![](https://intranetproxy.alipay.com/skylark/lark/0/2026/png/23157180/1785843565924-387dbbd7-90f8-4241-a58d-664cc5e6b486.png)
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+ > + Thinking mode is enabled by default. Unless otherwise specified, the default parameters for Ling-3.0-flash are as follows: `temperature=0.6, top_p=0.95, top_k=20`.
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  > + <font style="color:rgb(16, 18, 20);">SWE-Bench Series:Evaluated using OpenHands as the agent harness with tailored prompts. Decoding uses </font>`temperature=0.6, top_p=0.95, max_new_tokens=32K`<font style="color:rgb(16, 18, 20);">, with a 256K context window.</font>
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  > + <font style="color:rgb(16, 18, 20);">Terminal-Bench 2.1: Evaluated under the Artificial Analysis (AA) protocol using the default Terminus 2 harness, a unified 2-hour timeout, the provided JSON parser in preserve-thinking mode, and 3 runs per task (mean). Decoding uses </font>`temperature=0.6, top_p=1.0, max_new_tokens=32K`<font style="color:rgb(16, 18, 20);">, with a 256K context window.</font>
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+ > + <font style="color:rgb(16, 18, 20);">MiniAppBench: A 500-task coding benchmark evaluating whether models can turn a single user request into complete, usable interactive HTML apps in real-world application-generation scenarios. Evaluated with `temperature=1.0, top_p=1.0, max_tokens=128K`.
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  > + <font style="color:rgb(16, 18, 20);">AntSWEBench: </font><font style="color:rgb(0, 0, 0);">AntSWEBench is an internally used software engineering benchmark that covers mainstream programming languages such as Java, JavaScript, and Python, including various development scenarios like new feature, bug fix, and code refactoring.</font>
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  > + <font style="color:rgb(16, 18, 20);">Tau3-banking-AA: Aligned with the AA leaderboard, utilizing GPT-5.4-mini (medium reasoning) for both the user simulator and the natural-language assertion judge.</font>
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  > + MCP-Atlas: Evaluated on the 500-task public set using the official v1 harness with a 20-turn limit and Gemini-2.5-Pro as the claim-coverage judger.