The data and trust layer for global AI models.
YesAllInAI tracks model quality, price, context, speed, benchmark movement, community evidence, and ecosystem routing signals for teams choosing what to build with next.
| Rank | Model | Score | Context | Price | Signal |
|---|---|---|---|---|---|
| #1 | Claude 3.5 Sonnet Anthropic · Coding | 92.1 | 200K | $3/$15 | verified |
| #2 | GPT-4o OpenAI · Frontier | 91.4 | 128K | $2.5/$10 | verified |
| #3 | Gemini 1.5 Pro Google · Frontier | 89.2 | 2M | $3.5/$10.5 | verified |
| #4 | Llama 3.1 405B Meta · Open | 87.8 | 128K | $2.7/$2.7 | verified |
| #5 | Mistral Large 2 Mistral AI · Open | 86.9 | 128K | $2/$6 | verified |
模型可视化分析
用图表查看质量、成本、延迟、上下文和供应商覆盖情况。
LLM intelligence leaderboard
Rankings built for selection, routing, procurement, and community verification.
| 排名 | 模型 | 供应商 | 分数 | 代码 | 数学 | 上下文 | 价格 | 速度 |
|---|---|---|---|---|---|---|---|---|
| 1 | Claude 3.5 Sonnet codinglong-contextwriting | Anthropic | 92.1 | 94 | 88 | 200K | $3/$15 | 79 tok/s |
| 2 | GPT-4o multimodalfastagent-ready | OpenAI | 91.4 | 88 | 89 | 128K | $2.5/$10 | 104 tok/s |
| 3 | Gemini 1.5 Pro long-contextmultimodalgoogle | 89.2 | 84 | 86 | 2M | $3.5/$10.5 | 62 tok/s | |
| 4 | Llama 3.1 405B open-weightself-hostablereasoning | Meta | 87.8 | 85 | 86 | 128K | $2.7/$2.7 | 41 tok/s |
| 5 | Mistral Large 2 multilingualcodingagents | Mistral AI | 86.9 | 89 | 84 | 128K | $2/$6 | 86 tok/s |
| 6 | DeepSeek-Coder V2 codingopen-weightcost-efficient | DeepSeek | 86.5 | 93 | 87 | 128K | $0.14/$0.28 | 72 tok/s |
Benchmark coverage
Every score should move toward source-linked, reviewable, versioned evidence.
| 评测 | 类别 | 领先模型 | 最佳分数 | 更新时间 |
|---|---|---|---|---|
GPQA Graduate-level science reasoning benchmark. | Reasoning | Claude 3.5 Sonnet | 67.2% | 2026-05-01 |
MMLU Massive multitask language understanding. | Knowledge | GPT-4o | 88.7% | 2026-05-01 |
MMLU-Pro Harder MMLU variant with more options. | Knowledge | Claude 3.5 Sonnet | 78.1% | 2026-05-01 |
AIME Competition math reasoning. | Math | GPT-4o | 76.4% | 2026-05-01 |
MATH Multi-level mathematical problem solving. | Math | Llama 3.1 405B | 73.8% | 2026-05-01 |
HumanEval Python function synthesis tasks. | Coding | DeepSeek-Coder V2 | 90.2% | 2026-05-01 |
Trust layer for AI model data
YesAllInAI is designed to combine third-party benchmarks, crawler evidence, reviewer approval, and community submissions before data reaches public rankings.
Official sources, benchmark links, reviewer approval, and versioned scoring.
Automated source adapters feed a human review queue before public release.
Users submit corrections, model launches, eval notes, and source evidence.
Contribution reputation prepares the path for future Web3 rewards.
Benchmark heatmap
Category-normalized benchmark proxy scores by model.
社区正在讨论的 AI 模型动态
查看附近用户、创作者和 AI 团队分享的模型观点、路由策略、评测心得。
Claude 3.5 Sonnet still feels strongest for refactoring large repos. The gap is fewer correction loops.
We are routing short extraction jobs to lower-cost open models, then escalating uncertain outputs.
Open-weight coding models are becoming good enough for private internal agents.
Yes ecosystem bridge
Media and data create trust, YesRouter converts model demand into API usage, and YesClaw will connect enterprise digital employees with buyers.
Route from model intelligence to API usage, pricing, and compute demand.
Prepare the bridge from model selection to enterprise agent and digital employee transactions.
Data uploads, corrections, and evaluations become the foundation for future incentive rights.