SemiAnalysis vs Together AI

Unlocking Claude's Full Value: Plans, Workflows, and Setup

This article analyzes the economics of Anthropic's Claude subscription plans, comparing them to OpenAI's offerings. SemiAnalysis found a $200 Claude Max plan provides about $11,700 worth of Claude Opus 5.5 usage at API prices, versus roughly $2,100 for OpenAI's equivalent. Most subscribers use only a small fraction of their allowance, making the plans profitable for Anthropic. Recent product changes, including the merger of Cowork into Claude chat, new model versions (Opus 5.5, Sonnet 5.5, Haiku 5.5), and monthly API credits on Max and Team plans, make it easier to use the full allowance. The article provides a comprehensive guide with model routing tables, a map of the Claude stack, a 5-layer operating system, and 17 workflows to help users maximize their subscription value.

• SemiAnalysis study published October 5 finds a $200 Claude Max plan provides ~$11,700 of Claude Opus 5.5 API usage, vs ~$2,100 for OpenAI's GPT-6.1 Sol.
• Anthropic's gross margin on an Opus-heavy Max subscriber is +6% at 20% usage and -369% at full usage.
• Cowork merged into Claude chat on September 16.

How GLM5.3 Sparse Attention Affects HBM Memory Usage

SemiAnalysis analyzes how GLM-5.3's sparse attention affects HBM and NAND memory usage. Sparse attention reduces KV cache memory and bandwidth during attention operations but does not reduce overall memory capacity needs, as top-k selection requires full context in HBM. The SGLang team's HiSparse system offloads KV cache to host DRAM to overcome capacity limits. InferenceX benchmarks show GB200 and GB300 cost advantages over AMD MI355X ATOM at different latency targets, and GLM-5's architecture is suspected to be optimized for Moore Threads MTT S4000.

• GLM-5 (and GLM-5.x) is a 744B total, 40B active-parameter mixture-of-experts model with 1 shared expert and 8 of 256 experts routed per token (sparsity 32).
• HiSparse, designed by the SGLang team, offloads KV cache entries from device HBM to host DRAM using an LRU cache with layer-wise overlapping from HiCache.
• InferenceX benchmarks show GB200 costs approximately $0.044 per million total tokens at 150 tokens/s, about 12% lower than $0.049 for MI355X ATOM.

SemiAnalysis Releases ClusterMAX 3.0 GPU Cloud Ratings

SemiAnalysis released ClusterMAX 3.0, its third GPU cloud rating system for neoclouds, covering 77 providers and expanding its market view to 323. Nebius joined CoreWeave in the Platinum tier, while Google Cloud moved to Gold. The methodology includes audits of compute, networking, storage, orchestration, reliability, and security, and the report introduces standardized SLAs and highlights security concerns. Key trends include financing structures, Blackwell/GB300 deployments, Vera Rubin transition, and agentic coding impact. Separately, Australian AI data center operator Firmus, backed by Nvidia, withdrew its planned IPO due to market volatility. The company had sought to raise $5 billion at a $30.6 billion valuation. It will now pursue private funding after raising $2 billion from Nvidia, Coatue, Blackstone, and Jane Street in August, totaling over $3 billion in equity. Firmus also has agreements with Meta for GPU capacity in Southeast Asia.

• ClusterMAX 3.0 was released covering 77 providers, with the market view expanded to 323 providers.
• Nebius was upgraded to Platinum tier, joining CoreWeave, while Google Cloud moved to Gold.
• Firmus withdrew its planned IPO, citing market volatility, and will pursue private funding.

Introducing Together Link: Run open models inside the harness you already use

Together AI announces Together Link, a new product that allows running open models within existing harnesses, alongside new research blog posts on ThunderKittens for NVIDIA Vera Rubin NVL72, ThunderAgent, and ICML 2026 participation.

Open or Closed AI: Founders' Choices at TechCrunch Disrupt 2026

The article previews upcoming discussions at TechCrunch Disrupt 2026 (Oct 13-15, San Francisco) on AI model strategy. It highlights how startups increasingly use multiple models—open and proprietary—for different tasks, weighing cost, performance, and flexibility. Sessions feature leaders from Together AI, Pathway, Oumi, Nvidia, and Ricursive Intelligence. Topics include whether to build, customize, or rent AI models, optimizing AI hardware (chip design), and trade-offs between frontier APIs (like OpenAI, Google) and open-weight models. The article emphasizes that model choice shapes product differentiation, infrastructure needs, and long-term business viability. It's a conference promotion with substantive insights on AI architecture decisions.

• TechCrunch Disrupt 2026 takes place Oct 13-15 at Moscone West, San Francisco.
• The conference features 200+ sessions, 250+ speakers, and 300+ exhibiting startups.
• Sessions cover multi-model strategies, custom model development, and AI-designed hardware.

Together AI Product Team Shares AI-Native Workflow Stack

Together AI's product team, including CPO Charles Zedlewski and PMs Necoline Hubner and Pavneet Ahluwalia, outlined their AI-native product development stack in a podcast. The stack comprises four components: a shared context repository accessible by any AI harness, skills for feature research and PRD writing, an orchestrator for leadership, and agent evals. The team emphasizes collective productivity over individual gains, using multiple models like GLM and Kimi instead of relying solely on Anthropic. They validate products through agent-based tests, checking if AI agents can complete tasks using their documentation and API. The article also highlights Together AI's $8.3 billion valuation from its Series C round.

• Together AI is valued at $8.3 billion.
• Together AI's product team uses a four-component AI-native stack: shared context repo, skills, orchestrator, and agent evals.
• The team uses multiple AI models (e.g., GLM, Kimi) rather than only Anthropic's models.