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Mistral Large 4 Launched: 1.05 Trillion Parameters, Claims to Be Europe and the U.S.’s Strongest Open-Source Model

52 minutes ago

Beating AI News: French AI firm Mistral has launched its new flagship model, Mistral Large 4, internally codenamed "Le Chonk". It adopts a Mixture of Experts (MoE) architecture, with a total of 1.05 trillion parameters, though only 49 billion are activated per inference. The model natively supports text and image input, and offers a maximum context length of 1 million tokens. Its API is now available for preview, while the model weights are scheduled to be released publicly on October 27. Mistral claims Large 4 is currently the highest-performing open-weight model developed in the U.S. and Europe. Its official DeepSWE v1.1 software engineering benchmark score stands at 62%, compared to GLM-5.3 (61%), DeepSeek V4 Pro (57%), and Qwen 3.8 Max (51%). For financial benchmarks, Finch scores 67%, on par with DeepSeek V4 Pro; satellite image target localization task DIOR-RSVG scores 73%, higher than GPT-6 Astra's 68%. However, these results are primarily from Mistral's self-testing, not based on a unified standardized testing framework. When Zhipu AI launched GLM-5.3, it announced a score of 66.9% on the same DeepSWE v1.1 benchmark, while Kimi K3 reached 67.5% – both outperforming Large 4's 62%. The gap may stem from different testing configurations and execution methods used by various teams. Large 4 is trained from scratch: Mistral used around 4,000 Nvidia Grace Blackwell GPUs in its own European data centers over approximately two months. Mistral also positions "European autonomy" as a key selling point, noting that the model – from training and API services to future independent deployment – can all be hosted on European infrastructure.

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DeFi Development has been authorized to launch the CHAD preferred stock repurchase plan.

Nasdaq-listed DeFi Development Corp. has authorized the launch of a CHAD preferred stock repurchase program, which allows for the repurchase of all outstanding CHAD preferred shares, including any future shares the company may issue. The program enables the company to conduct repurchases flexibly when CHAD’s share price falls below its $10 par value per share. The Solana treasury-focused company stated it has no immediate plans to repurchase CHAD, noting it first wants the security to build market liquidity and stabilize at or near its $10 par value. This indefinite authorization adds a potential capital allocation tool to DeFi Development (DFDV)’s existing $300 million CHAD ATM issuance program. The company previously said it plans to raise funds via this program to purchase additional SOL, and paid CHAD’s first dividend on October 1.

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Banbury Road trains 32 models together, with the models independently developing distinct areas of expertise.

Beating AI Express News: AI lab Banbury Road has released Kardashev-0.7, a system composed of 32 distinct models. The team trained these models together via reinforcement learning, without predefining each model’s specific tasks, enabling them to develop unique specialties and complementary capabilities during training. This methodology is called RL for Population Scaling (RLPS). Prior RLPS experiments only reached up to 16 models. In the 8-model trial, the jointly trained model population scored 81.70%, outperforming 71.04% for 8 independently trained models and 72.65% for the same model sampled 8 times. In the 16-model experiment, 22 questions were answered correctly by only one model each, verifying that different models did learn distinct capabilities. Kardashev-0.7 expands this to 32 models. Banbury Road claims it delivers state-of-the-art performance at 0.007 to 0.02x inference cost and 0.03x memory usage. Its API beta is currently on a waitlist. However, the population scores from the earlier 8 and 16-model trials were derived post-hoc by picking the best result from multiple model outputs using standard answers. Since real-world applications lack standard answers, Banbury Road has not yet disclosed a full solution for the system to automatically select correct responses.

15 minutes ago

Spark leads $2.4 million funding round: Antseed to build an AI inference version of BitTorrent

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Fed Governor Bowman: The Federal Reserve plans to revamp its banking regulatory framework and reassess the asset thresholds for stricter regulation.

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MetaMask’s staking service has essentially completed the exit processing for Lido validators, and will release a full post-mortem analysis report.

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