←Search results

Search “Agents”

By relevance · Total 563 results Clear search

Saturday

  1. ZhidongxiSelected

    DeepSeek V4.1-Flash updated to V4.1-Flash-1017 and opens search and fetch APIs

    DeepSeek V4.1-Flash has been updated to version V4.1-Flash-1017; the official web, App and API have been updated in sync, and the model weights are available for download on HuggingFace and ModelScope. In the DeepSWE v1.1 test, the new model's max tier achieved a single-task pass rate of 77.0%, 2.8 percentage points higher than the previous version, and the average cost per task dropped from about 3.3 yuan to about 1.3 yuan. DeepSeek also opened to the public the Search and Fetch APIs of its self-developed search engine; both interfaces are priced at 5 yuan per thousand calls, and this part of access via server-side tool calls will be charged per call starting from 00:00 Beijing time on November 1.

    DeepSeek V4.1-Flash has been updated to version V4.1-Flash-1017; the official web, App and API have been updated in sync, and the model weights are available for download on HuggingFace and ModelScope. In the DeepSWE v1.1 test, the new model's max tier achieved a single-task pass rate of 77.0%, 2.8 percentage points higher than the previous version, and the average cost per task dropped from about 3.3 yuan to about 1.3 yuan. DeepSeek also opened to the public the Search and Fetch APIs of its sel

    Matched fieldsBody

    #hot-signal#Model releases#Open-source ecosystem#English text is being prepared.

Friday

  3. Unite.AISelected

    Reflection open-sources 501B-parameter MoE model Beam under Apache 2.0

    On October 16, Reflection released its first open-weight model Beam on Hugging Face under the Apache 2.0 license, and Baseten provided day-0 support the same day through Model APIs and Dedicated Inference. Beam is a 501B total-parameter MoE autoregressive Transformer with 23B activated per token, a context window of up to 1 million tokens, pretraining on 23.8 trillion tokens, and weights released in BF16, FP8, and NVFP4 precisions. The evaluation results given by Reflection are GPQA Diamond 90.5%, SWE-bench Verified 80.9%, AIME 2026 97.8%, Terminal-Bench 2.1 80.1%, and MCP Atlas 78.7%, and it also open-sourced the terminal agent tool Mirror CLI.

    same day through Model APIs and Dedicated Inference. Beam is a 501B total-parameter MoE autoregressive Transformer with 23B activated per token, a context window of up to 1 million tokens, pretraining on 23.8 trillion tokens, and weights released in BF16, FP8, and NVFP4 precisions. The evaluation results given by Reflection are GPQA Diamond 90.5%, SWE-bench Verified 80.9%, AIME 2026 97.8%, Terminal-Bench 2.1 80.1%, and MCP Atlas 78.7%, and it also open-sourced the terminal agent tool Mirror CLI.

    Matched fieldsBody

    #hot-signal#Model releases#Open-source ecosystem#Agents
  4. Artificial Analysis full articleSelected

    Reflection Releases Its First Model Beam, Scoring 30 on the Artificial Analysis Intelligence Index

    Reflection has released its first model, Beam, an open-weight model with 501B total parameters and 23B activated parameters. It scores 30 on the Artificial Analysis Intelligence Index, ranking second among US open-weight models, behind only Meta's Muse Spark Open (39) and ahead of NVIDIA's Nemotron 3.5 Super VL (26). Beam consumes about 26k output tokens per Intelligence Index task, of which about 19k are used for reasoning and 7k for answering; it scores 48.5% on AutomationBench-AA, but only 3.0% on Terminal-Bench 4.0. On AA-Omniscience, it answers 33% of questions correctly, with a hallucination rate of 47% and a net index score of 1.5.

    Reflection has released its first model, Beam, an open-weight model with 501B total parameters and 23B activated parameters. It scores 30 on the Artificial Analysis Intelligence Index, ranking second among US open-weight models, behind only Meta's Muse Spark Open (39) and ahead of NVIDIA's Nemotron 3.5 Super VL (26). Beam consumes about 26k output tokens per Intelligence Index task, of which about 19k are used for reasoning and 7k for answering; it scores 48.5% on AutomationBench-AA, but only 3.

    Matched fieldsBody

    #company#Model releases#Open-source ecosystem#Evaluations / benchmarks

Thursday