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Topic: "model-pricing"
Cohere Command A Reasoning beats GPT-OSS-120B and DeepSeek R1 0528
command-a-reasoning deepseek-v3.1 cohere deepseek intel huggingface baseten vllm-project chutes-ai anycoder agentic-ai hybrid-models long-context fp8-training mixture-of-experts benchmarking quantization reasoning coding-workflows model-pricing artificialanlys reach_vb scaling01 cline ben_burtenshaw haihaoshen jon_durbin _akhaliq willccbb teortaxestex
Cohere's Command A Reasoning model outperforms GPT-OSS in open deep research capabilities, emphasizing agentic use cases for 2025. DeepSeek-V3.1 introduces a hybrid reasoning architecture toggling between reasoning and non-reasoning modes, optimized for agentic workflows and coding, with extensive long-context pretraining (~630B tokens for 32k context, ~209B for 128k), FP8 training, and a large MoE expert count (~37B). Benchmarks show competitive performance with notable improvements in SWE-Bench and other reasoning tasks. The model supports a $0.56/M input and $1.68/M output pricing on the DeepSeek API and enjoys rapid ecosystem integration including HF weights, INT4 quantization by Intel, and vLLM reasoning toggles. Community feedback highlights the hybrid design's pragmatic approach to agent and software engineering workflows, though some note the lack of tool use in reasoning mode.
Grok 4: xAI succeeds in going from 0 to new SOTA LLM in 2 years
grok-4 grok-4-heavy claude-4-opus xai perplexity-ai langchain cursor cline model-releases benchmarking long-context model-pricing model-integration voice performance scaling gpu-optimization elonmusk aravsrinivas igor_babuschkin yuchenj_uw
xAI launched Grok 4 and Grok 4 Heavy, large language models rumored to have 2.4 trillion parameters and trained with 100x more compute than Grok 2 on 100k H100 GPUs. Grok 4 achieved new state-of-the-art results on benchmarks like ARC-AGI-2 (15.9%), HLE (50.7%), and Vending-Bench, outperforming models such as Claude 4 Opus. The model supports a 256K context window and is priced at $3.00/M input tokens and $15.00/M output tokens. It is integrated into platforms like Cursor, Cline, LangChain, and Perplexity Pro/Max. The launch was accompanied by a controversial voice mode and sparked industry discussion about xAI's rapid development pace, with endorsements from figures like Elon Musk and Arav Srinivas.
GPT 4.1: The New OpenAI Workhorse
gpt-4.1 gpt-4.1-mini gpt-4.1-nano gpt-4o gemini-2.5-pro openai llama-index perplexity-ai google-deepmind coding instruction-following long-context benchmarks model-pricing model-integration model-deprecation sama kevinweil omarsar0 aidan_mclau danhendrycks polynoamial scaling01 aravsrinivas lmarena_ai
OpenAI released GPT-4.1, including GPT-4.1 mini and GPT-4.1 nano, highlighting improvements in coding, instruction following, and handling long contexts up to 1 million tokens. The model achieves a 54 score on SWE-bench verified and shows a 60% improvement over GPT-4o on internal benchmarks. Pricing for GPT-4.1 nano is notably low at $0.10/1M input and $0.40/1M output. GPT-4.5 Preview is being deprecated in favor of GPT-4.1. Integration support includes Llama Index with day 0 support. Some negative feedback was noted for GPT-4.1 nano. Additionally, Perplexity's Sonar API ties with Gemini-2.5 Pro for the top spot in the LM Search Arena leaderboard. New benchmarks like MRCR and GraphWalks were introduced alongside updated prompting guides and cookbooks.
not much happened today
o3 o4-mini gpt-5 sonnet-3.7 gemma-3 qwen-2.5-vl gemini-2.5-pro gemma-7b llama-3-1-405b openai deepseek anthropic google meta-ai-fair inference-scaling reward-modeling coding-models ocr model-preview rate-limiting model-pricing architectural-advantage benchmarking long-form-reasoning attention-mechanisms mixture-of-experts gpu-throughput sama akhaliq nearcyan fchollet reach_vb philschmid teortaxestex epochairesearch omarsar0
OpenAI announced that o3 and o4-mini models will be released soon, with GPT-5 expected in a few months, delayed for quality improvements and capacity planning. DeepSeek introduced Self-Principled Critique Tuning (SPCT) to enhance inference-time scalability for generalist reward models. Anthropic's Sonnet 3.7 remains a top coding model. Google's Gemma 3 is available on KerasHub, and Qwen 2.5 VL powers a new Apache 2.0 licensed OCR model. Gemini 2.5 Pro entered public preview with increased rate limits and pricing announced, becoming a preferred model for many tasks except image generation. Meta's architectural advantage and the FrontierMath benchmark challenge AI's long-form reasoning and worldview development. Research reveals LLMs focus attention on the first token as an "attention sink," preserving representation diversity, demonstrated in Gemma 7B and LLaMa 3.1 models. MegaScale-Infer offers efficient serving of large-scale Mixture-of-Experts models with up to 1.90x higher per-GPU throughput.
not much happened today
gemini-2.0-flash imagen-3 mistral-small-3.1 mistral-3 gpt-4o-mini claude-3.5-haiku olm0-32b qwen-2.5 shieldgemma-2 julian fasttransform nvidia google mistral-ai allen-ai anthropic langchainai perplexity-ai kalshi stripe qodoai multimodality image-generation context-windows model-pricing open-source-models image-classification frameworks python-libraries partnerships jeremyphoward karpathy abacaj mervenoyann
At Nvidia GTC Day 1, several AI updates were highlighted: Google's Gemini 2.0 Flash introduces image input/output but is not recommended for text-to-image tasks, with Imagen 3 preferred for that. Mistral AI released Mistral Small 3.1 with 128k token context window and competitive pricing. Allen AI launched OLMo-32B, an open LLM outperforming GPT-4o mini and Qwen 2.5. ShieldGemma 2 was introduced for image safety classification. LangChainAI announced multiple updates including Julian powered by LangGraph and integration with AnthropicAI's MCP. Jeremy Howard released fasttransform, a Python library for data transformations. Perplexity AI partnered with Kalshi for NCAA March Madness predictions.
GPT4o August + 100% Structured Outputs for All (GPT4o August edition)
gpt-4o-2024-08-06 llama-3-1-405b llama-3 claude-3.5-sonnet gemini-1.5-pro gpt-4o yi-large-turbo openai meta-ai-fair google-deepmind yi-large nvidia groq langchain jamai langsmith structured-output context-windows model-pricing benchmarking parameter-efficient-expert-retrieval retrieval-augmented-generation mixture-of-experts model-performance ai-hardware model-deployment filtering multi-lingual vision john-carmack jonathan-ross rohanpaul_ai
OpenAI released the new gpt-4o-2024-08-06 model with 16k context window and 33-50% lower pricing than the previous 4o-May version, featuring a new Structured Output API that improves output quality and reduces retry costs. Meta AI launched Llama 3.1, a 405-billion parameter model surpassing GPT-4 and Claude 3.5 Sonnet on benchmarks, alongside expanding the Llama Impact Grant program. Google DeepMind quietly released Gemini 1.5 Pro, outperforming GPT-4o, Claude-3.5, and Llama 3.1 on LMSYS benchmarks and leading the Vision Leaderboard. Yi-Large Turbo was introduced as a cost-effective upgrade priced at $0.19 per million tokens. In hardware, NVIDIA H100 GPUs were highlighted by John Carmack for their massive AI workload power, and Groq announced plans to deploy 108,000 LPUs by Q1 2025. New AI tools and techniques include RAG (Retrieval-Augmented Generation), the JamAI Base platform for Mixture of Agents systems, and LangSmith's enhanced filtering capabilities. Google DeepMind also introduced PEER (Parameter Efficient Expert Retrieval) architecture.