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Chaoyan Intelligence Releases and Open-Sources Its First Large Model, Apex-35B-V1.0
On October 28, 2026, Beijing-based large model startup Chaoyan Intelligence released and open-sourced its first large model, Apex-35B-V1.0. According to a report by Zhidongxi, the model has a total parameter count of 35 billion, activates approximately 3 billion parameters per token, supports a 262,000-token context window, and is optimized through mid-training and post-training based on Qwen3.6-35B-A3B. In testing, Apex-35B-V1.0 scored 60.9 and 40.0 on PaperBench and FrontierScience, respectively, higher than GLM-5.3's 42.5 and 30.7; it scored 84.0 on BFCL V4, higher than DeepSeek-V4.1-Flash-1017's 75.0. The model has been released with open weights under the Apache 2.0 license, with official provision of approximately 72GB BF16 weights and GGUF quantized versions at different precisions. No further developments have been disclosed at present.
raining and post-training based on Qwen3.6-35B-A3B. In testing, Apex-35B-V1.0 scored 60.9 and 40.0 on PaperBench and FrontierScience, respectively, higher than GLM-5.3's 42.5 and 30.7; it scored 84.0 on BFCL V4, higher than DeepSeek-V4.1-Flash-1017's 75.0. The model has been released with open weights under the Apache 2.0 license, with official provision of approximately 72GB BF16 weights and GGUF quantized versions at different precisions. No further developments have been disclosed at present.
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Mistral calls Le Chonk the world's best open-weight model
On October 29, 2026, Mistral released the official version of Le Chonk, a trillion-parameter open-weight model, calling it the world's best open-weight model for automated coding and cybersecurity, available for free. Pierre Stock, Mistral's Vice President of Science, said the model was trained on increasingly difficult cybersecurity and coding problems, enabling it to match or surpass models from Chinese labs such as DeepSeek and Z.ai. Stock also emphasized that the West needs its own advanced open-weight models, because closed-source models can be shut down remotely, whereas open weights cannot have access cut off once released. Currently, this event has only been reported by Wired AI, with no further developments yet.
, Mistral's Vice President of Science, said the model was trained on increasingly difficult cybersecurity and coding problems, enabling it to match or surpass models from Chinese labs such as DeepSeek and Z.ai. Stock also emphasized that the West needs its own advanced open-weight models, because closed-source models can be shut down remotely, whereas open weights cannot have access cut off once released. Currently, this event has only been reported by Wired AI, with no further developments yet.
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Memory Tensor releases and open-sources MemT 1.0 memory-native large model
On October 20, 2026, Memory Tensor released and open-sourced the MemT 1.0 series of memory-native large models. According to a report by QbitAI, the series includes MemT-1.0-Nano, with 36B parameters and 3B activated (based on Qwen3.6-35B-A3B), and MemT-1.0-Flash, with 288B parameters and 17B activated (based on DeepSeek-V4-Flash); a memory computation pathway running in parallel with attention was added to selected Transformer modules. The team built a 34-billion-token memory training corpus, of which 20 billion tokens were used for continued pre-training and 14 billion for post-training. In a comprehensive evaluation across 11 memory benchmarks, Flash and Nano averaged scores of 50.6 and 47.0, respectively, ranking first and second among the parametric memory methods evaluated; on 500 SWE-bench Verified tasks, the proportion of tasks that Nano solved successfully in all three attempts rose from 54.00% on the no-experience baseline to 62.80%. Subsequent reports said that MemT-1.0 connects a native memory module in parallel within the Transformer, turning memory into a state in the forward computation stream, and released up to 288B weights and a 66-page technical report; the authors said that under a 256K ultra-long history, memory usage dropped from 5.06G to a constant 40M, and KV cache was reduced
by QbitAI, the series includes MemT-1.0-Nano, with 36B parameters and 3B activated (based on Qwen3.6-35B-A3B), and MemT-1.0-Flash, with 288B parameters and 17B activated (based on DeepSeek-V4-Flash); a memory computation pathway running in parallel with attention was added to selected Transformer modules. The team built a 34-billion-token memory training corpus, of which 20 billion tokens were used for continued pre-training and 14 billion for post-training. In a comprehensive evaluation across
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Memory Tensor open-sources and releases the MemT 1.0 memory-native model series
On October 20, 2026, Memory Tensor open-sourced and released the MemT 1.0 series of memory-native models. The series includes two models: Nano has 36B parameters with 3B activated, based on Qwen3.6-35B-A3B; Flash has 288B parameters with 17B activated, based on DeepSeek-V4-Flash. The models add a memory branch parallel to attention in selected Transformer modules, encoding experience as updatable matrix states that participate in subsequent generation. On 500 SWE-bench Verified tasks, Nano achieved an average success rate of 69.73% at key checkpoints, versus 66.07% for the no-experience baseline; under a 128K long history, end-to-end inference speed is approximately 1.86 times that of the full-context approach, and the persistent state footprint per trajectory is one sixty-sixth of the full-context KV Cache. There are currently no subsequent development facts.
f memory-native models. The series includes two models: Nano has 36B parameters with 3B activated, based on Qwen3.6-35B-A3B; Flash has 288B parameters with 17B activated, based on DeepSeek-V4-Flash. The models add a memory branch parallel to attention in selected Transformer modules, encoding experience as updatable matrix states that participate in subsequent generation. On 500 SWE-bench Verified tasks, Nano achieved an average success rate of 69.73% at key checkpoints, versus 66.07% for the no
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Mistral Large 4 Opens Its Weights, Becoming the Top Non-Chinese Open-Source Model
Mistral AI updated and opened the weights of its flagship model Mistral Large 4, with 1.05T total parameters and 52B activated parameters. It scores 26 on the Artificial Analysis Intelligence Index v5, 1 point higher than the Preview version, and leads the US open-source model Muse Spark Open (25) by 1 point. The model outputs 82K tokens per task, 39% fewer than the Preview's 135K, and its hallucination rate on AA-Omniscience v1.1 dropped from 41% to 28%, though accuracy remains 26%. Pricing is $0.70/$2.10 per 1M input/output tokens, about 50% lower than the Preview, under the MEOW license; operating a Model-as-a-Service business requires a separate commercial license.
Mistral AI updated and opened the weights of its flagship model Mistral Large 4, with 1.05T total parameters and 52B activated parameters. It scores 26 on the Artificial Analysis Intelligence Index v5, 1 point higher than the Preview version, and leads the US open-source model Muse Spark Open (25) by 1 point. The model outputs 82K tokens per task, 39% fewer than the Preview's 135K, and its hallucination rate on AA-Omniscience v1.1 dropped from 41% to 28%, though accuracy remains 26%. Pricing is
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Mistral Open-Sources Mistral Large 4 Weights, Leading GLM-5.3 by 1 Point on Coding Benchmark
Mistral released the weights of its frontier open-source model Mistral Large 4 (ML4) on Hugging Face on Thursday; the model had only been available in preview three weeks earlier. Mistral calls ML4 "the world's best open-source agentic coding model," scoring 41% on Terminal-Bench 4.0, slightly above GLM-5.3's 40%, and says it uses an average of 47% fewer tokens per task than GLM-5.3; it scored 79% on the CyberGym E2E defensive cybersecurity test, compared with 29% for GLM-5.3. Mistral Vice President of Science Pierre Stock called ML4 merely a "stepping stone" and confirmed that smaller versions will be released in the coming months, one of which can run on a laptop.
Mistral released the weights of its frontier open-source model Mistral Large 4 (ML4) on Hugging Face on Thursday; the model had only been available in preview three weeks earlier. Mistral calls ML4 "the world's best open-source agentic coding model," scoring 41% on Terminal-Bench 4.0, slightly above GLM-5.3's 40%, and says it uses an average of 47% fewer tokens per task than GLM-5.3; it scored 79% on the CyberGym E2E defensive cybersecurity test, compared with 29% for GLM-5.3. Mistral Vice Presi
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Mistral Large 4 open-weight release: improved coding and safety capabilities, API price halved
Mistral AI released the open weights of Mistral Large 4 (codename Le Chonk) on Hugging Face on October 29. The model has 100 billion parameters out of a total parameter scale of the 100-billion class, with about 4.9 billion parameters actually invoked per token, using a MoE architecture with 128 experts, of which 4 are activated each time plus 1 shared expert. Compared with the October 6 preview version, Mistral says it continued reinforcement learning over three weeks, raising the Terminal-Bench 4.0 score from 28% to 41%; the Artificial Analysis coding index is 48%, on par with GLM-5.3 while using an average of 47% fewer tokens per task. API pricing drops to $0.70 per million input tokens and $2.10 per million output tokens; the new Mistral Ecosystem Open-Weight License permits commercial use, but reselling model services requires a separate agreement, and the full version requires 8 Nvidia B200 or B300 GPUs.
Mistral AI released the open weights of Mistral Large 4 (codename Le Chonk) on Hugging Face on October 29. The model has 100 billion parameters out of a total parameter scale of the 100-billion class, with about 4.9 billion parameters actually invoked per token, using a MoE architecture with 128 experts, of which 4 are activated each time plus 1 shared expert. Compared with the October 6 preview version, Mistral says it continued reinforcement learning over three weeks, raising the Terminal-Benc
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