Data Provider / Broker · vs · Publisher & Media Owner · Agency & Consultancy
Artificial Analysis vs SemiAnalysis
Structured technology and market comparison · 2026
Direct Feature Comparison
Artificial Analysis · vs · SemiAnalysisIndependent AI model benchmarking and selection platform.
AI infrastructure and semiconductor research, data models, tools and consulting.
Analyze all overlapping signals and tech stacks for Artificial Analysis and SemiAnalysis
Compare mutual enterprise clients, monetization models, live market signals, and partner networks directly in the interactive Knowledge Graph.
Comparison Analysis
What is the main difference between Artificial Analysis and SemiAnalysis?
When comparing Artificial Analysis and SemiAnalysis, both platforms operate within the Measurement & Analytics Platform ecosystem. Artificial Analysis is positioned as Independent AI model benchmarking and selection platform, whereas SemiAnalysis focuses on AI infrastructure and semiconductor research, data models, tools and consulting. Decision-makers evaluate both solutions when orchestrating their commercial monetization and technology stack.
What are the top alternatives to Artificial Analysis and SemiAnalysis?
When evaluating Artificial Analysis and SemiAnalysis, enterprise buyers also consider other platforms in Measurement & Analytics Platform. You can discover the full competitive landscape and evaluate other alternatives by viewing their respective footprint profiles on Polaris7.
How do the corporate scale, structure, and headquarters of Artificial Analysis and SemiAnalysis compare?
Artificial Analysis is headquartered in United States (founding year unrecorded) with 10–49 employees. SemiAnalysis is based in United States (founded in 2020) with 50–200 employees. Complete ownership, subsidiary trees, and corporate affiliations are documented across Polaris7.
Market Signals
Recent Market Signals & Activity: Artificial Analysis vs SemiAnalysis
Documented market movements, strategic partnerships, product releases, and regulatory developments mapped across Polaris7.
Artificial Analysis
Recent Signals
- ·Trending Topics (DACH/CEE Innovation & Tech)AI / LLM
Mistral Large 4 Open Weights, Ranks Ahead of Meta
Mistral AI has released the open weights for Mistral Large 4, nicknamed 'Le Chonk', a trillion-parameter mixture-of-experts model with 52 billion active parameters. It processes text and images, supports over 160 languages, and is available on Hugging Face under a modified MIT license, permitting self-hosting and fine-tuning, but commercial resale requires a separate license. In Artificial Analysis' updated open-source ranking, it scored 26 points, placing eighth, ahead of Meta's Muse Spark Open but behind seven Chinese models like DeepSeek V4.1 Flash and GLM-5.3. The model excels in coding and cybersecurity, ranking second on the Coding Agent Index and third on the Cyber Index among open models. API pricing has been halved to €0.63 per million input tokens. Mistral positions it for programming agents and cybersecurity, with a specialized penetration-testing variant for partners like Orange Cyberdefense, highlighting Europe's AI competition with China and the US.
- Mistral AI released open weights for Mistral Large 4, a ~1 trillion parameter MoE model with 52B active parameters.
- The model is available on Hugging Face under a modified MIT license, with commercial resale requiring a separate license.
- Mistral Large 4 scored 26 points in Artificial Analysis' open-source ranking, placing 8th, ahead of Meta's Muse Spark Open.
- ·Trending Topics (DACH/CEE Innovation & Tech)AI Models & Benchmarks
Artificial Analysis Intelligence Index v5 Penalizes Open-Weight Models
Artificial Analysis released version 5 of its Intelligence Index, a major update that now relies on private test data for about 60% of scoring to prevent models being trained on known questions. The index, equally weighted across knowledge work, programming, science, and general knowledge, significantly downgraded open-weight models, widening the gap between the best proprietary and open-weight models from 3 to 12 points. Claude Opus 5.5 (Anthropic) retained the top spot with 58 points, with GPT-6 Astra close behind at 57. Open-weight models saw notable declines: Kimi K3.1 Pro (Moonshot AI) dropped from 55 to 46, DeepSeek V4.1 Flash from 45 to 32, and Xiaomi's MiMo-V2.6-Pro lost 16 points to 30. The update introduces new benchmarks (FrontierCode 2.0, Terminal-Bench-Science) and increased evaluation cost (e.g., $10 per task for Claude Opus 5.5). Rankings are highly dependent on test selection and weighting, and private tests reduce external verifiability.
- Artificial Analysis released Intelligence Index v5, with 60% of scoring based on private test sets.
- Gap between top proprietary and best open-weight model widened from 3 to 12 points.
- Claude Opus 5.5 remains top with 58 points, followed by GPT-6 Astra with 57.
- ·OpenAI BlogAI Models
OpenAI Launches GPT-6.1 Terra Model with Computer Use
OpenAI has introduced GPT-6.1 Terra, a cost-efficient AI model that enhances computer use, coding, and factual accuracy. Positioned as the balanced middle tier between high-end GPT-6.1 Sol/Astra and budget GPT-6 Luna, it offers significant performance gains at a lower price. On benchmarks like Agents' Last Exam, Terra improves by 15 percentage points over GPT-5.6 Terra and can match Astra's performance at one-tenth the cost. API pricing is $1 per million input tokens and $5 per million output tokens, 50-58% lower than GPT-5.6 Terra, making it ideal for high-volume automation. Terra solves 50.3% of tasks on Terminal-Bench 4.0, more than double its predecessor, and reduces hallucination rates from 88% to 38% on AA-Omniscience. Available immediately to ChatGPT Plus, Pro, Enterprise, and Edu subscribers in ChatGPT Work, Codex, and via the OpenAI API, it is rated 'Critical' under OpenAI's Preparedness Framework, prompting enhanced safety measures.
- OpenAI launched GPT-6.1 Terra, a cost-efficient model positioned between GPT-6.1 Sol/Astra and GPT-6 Luna, offering computer use, coding, and improved factuality.
- API pricing is $1 per million input tokens and $5 per million output tokens, 50-58% lower than GPT-5.6 Terra.
- On Agents' Last Exam, Terra improves by 15 percentage points over GPT-5.6 Terra and matches Astra-level performance at one-tenth the price.
SemiAnalysis
Recent Signals
- ·Linas NewsletterAI
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.
- ·SemiAnalysis
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.
- ·SemiAnalysisAI Infrastructure
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.
Compare their exact ecosystem overlaps.
Explore all deep relationships in Polaris7. Discover exactly which mutual clients, integrated technologies, and overlapping partners Artificial Analysis and SemiAnalysis share across the market ecosystem.
Unlock full capability matrix, client overlaps & technology stacks