[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f5fPSDG4tC3O5-D-5MlkEDHk0VpGrmtXfgmgvdzu28NM":3},{"article":4,"related":18},{"id":5,"slug":6,"title":7,"seo_title":7,"description":8,"keywords":9,"content":10,"category":11,"image_url":12,"source_guid":13,"published_at":14,"created_at":14,"updated_at":15,"source_url":16,"source_name":17},1370,"mistral-large-4-pairs-cheaper-api-access-with-costly-hosting","Mistral Large 4 pairs cheaper API access with costly hosting","Mistral released Large 4's weights and lowered API prices. Its hardware requirements and cloud licensing exception shape the decision to host it yourself.","[\"Mistral Large 4\",\"Le Chonk\",\"coding models\",\"model hosting\",\"API pricing\"]","\u003Cp>Mistral has released the weights for Large 4, nicknamed “Le Chonk,” and set API prices at $0.70 per million input tokens and $2.10 per million output tokens. The model accepts text and images and produces text and code, \u003Ca href=\"https:\u002F\u002Fthe-decoder.com\u002Fparis-based-mistral-releases-le-chonk-a-strong-coding-model-that-still-trails-china-overall\u002F\" rel=\"noopener noreferrer\">according to the-decoder.com\u003C\u002Fa>.\u003C\u002Fp>\n\u003Cp>Access to the weights makes running it on your own servers possible, but the reported hardware requirements are substantial. Large 4 has roughly one trillion parameters, with 52 billion active per step. The report cites SGLang documentation specifying eight Nvidia B300 GPUs for the standard version or four GB300 GPUs for a more heavily compressed variant. Its modified MIT license permits free use, modification and commercial deployment, but offering Large 4 as a commercial cloud service requires a separate Mistral license.\u003C\u002Fp>\n\u003Cp>For a team choosing between API access and hosting, the useful starting point is its own token bill. \u003Cstrong>Hypothetical calculation:\u003C\u002Fstrong> a workload consuming 100 million input tokens and 20 million output tokens would cost (100 × $0.70) + (20 × $2.10) = \u003Cstrong>$112\u003C\u002Fstrong> at the reported API rates. That is a token-charge calculation, not a performance benchmark or an estimate of hosting costs. It assumes those token volumes and excludes other charges.\u003C\u002Fp>\n\u003Cp>This gives a concrete decision threshold: a team considering hosting purely to reduce spending should compare its complete hardware and operating costs against the API bill for its actual workload. The GPU requirements make access to downloadable weights a separate question from economical deployment. A provider planning to sell hosted access also needs to resolve the separate license before treating free commercial use as sufficient permission.\u003C\u002Fp>\n\u003Cp>The performance evidence supports considering Large 4 for coding, without treating it as the strongest general-purpose choice. The report puts its Terminal-Bench 4.0 result at 41 percent, versus GLM-5.3’s 40 percent. On Artificial Analysis’ broader Intelligence Index, however, Large 4 scores 26 points against 32 for GLM-5.3. That combination suggests a narrower purchasing decision: teams buying primarily for coding have a stronger reason to shortlist it than teams seeking one model across varied tasks. The $112 example supplies a budget baseline; the benchmark split identifies which workload that budget should be evaluated against.\u003C\u002Fp>","AI & Machine Learning","https:\u002F\u002Fseedwire.co\u002Fapi\u002Fimages\u002Farticles\u002F1793320713959-7a8jur31u3p.webp","6982ff94f200009aaf6b6776098b42578991abaa0354129e3b654010d2a87ca0","2026-10-30T00:38:34.897Z",null,"https:\u002F\u002Fthe-decoder.com\u002Fparis-based-mistral-releases-le-chonk-a-strong-coding-model-that-still-trails-china-overall\u002F","the-decoder.com",[19,26,33,40],{"id":20,"slug":21,"title":22,"description":23,"category":11,"image_url":24,"published_at":25},1375,"study-finds-llm-research-claims-age-unevenly-across-models","Study finds LLM research claims age unevenly across models","A study found 44 percent of 50 LLM findings had expired. Its model comparisons show why research age alone cannot guide model selection or workflow safeguards.","https:\u002F\u002Fseedwire.co\u002Fapi\u002Fimages\u002Farticles\u002F1793751185684-b9dptvj7d45.webp","2026-11-04T00:13:05.978Z",{"id":27,"slug":28,"title":29,"description":30,"category":11,"image_url":31,"published_at":32},1374,"athenahealth-links-patient-records-to-paid-claude-accounts","Athenahealth links patient records to paid Claude accounts","Athenahealth's new connection lets paying Claude users query their records. We examine subscription costs, access controls and the limits on medical guidance.","https:\u002F\u002Fseedwire.co\u002Fapi\u002Fimages\u002Farticles\u002F1793664800530-ef6pcsfgu6o.webp","2026-11-03T00:13:20.909Z",{"id":34,"slug":35,"title":36,"description":37,"category":11,"image_url":38,"published_at":39},1373,"adobes-editagent-improves-image-edits-at-a-compute-cost","Adobe’s EditAgent improves image edits at a compute cost","Adobe’s EditAgent combines specialist tools with measured image checks. Its reported gains suggest a role in training data, with a compute tradeoff to weigh.","https:\u002F\u002Fseedwire.co\u002Fapi\u002Fimages\u002Farticles\u002F1793578401524-t2vuwnn0u8a.webp","2026-11-02T00:13:21.752Z",{"id":41,"slug":42,"title":43,"description":44,"category":11,"image_url":45,"published_at":46},1372,"anthropic-tests-expose-limits-of-screening-fine-tuning-data","Anthropic tests expose limits of screening fine-tuning data","Anthropic trained research models on benign-looking fiction that encouraged sabotage. The findings inform how teams should evaluate fine-tuning releases.","https:\u002F\u002Fseedwire.co\u002Fapi\u002Fimages\u002Farticles\u002F1793507192508-kf4jz2sjc.webp","2026-11-01T04:26:32.789Z"]