269 lines
33 KiB
JSON
269 lines
33 KiB
JSON
{
|
||
"id": "conv_20251008_191705_997",
|
||
"title": "搜索deepseekv3.1，qwen2.5-max，kimi-k2，gemini2.5pro的模型...",
|
||
"created_at": "2025-10-08T19:17:05.997621",
|
||
"updated_at": "2025-10-08T19:24:35.104277",
|
||
"messages": [
|
||
{
|
||
"role": "user",
|
||
"content": "搜索deepseekv3.1，qwen2.5-max，kimi-k2，gemini2.5pro的模型相关信息，保证数据的准确性，主要了解其性能，做成一个精美的，设计高级的，带有可互动和方便对比的各种图表，动画和互动效果精致高级的html页面",
|
||
"timestamp": "2025-10-08T19:17:08.110492"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "我来帮您搜索这些AI模型的相关信息，然后创建一个精美的HTML页面来展示和对比它们的性能。",
|
||
"timestamp": "2025-10-08T19:17:10.164273"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-10-08T19:17:10.167621",
|
||
"tool_calls": [
|
||
{
|
||
"id": "web_search:0",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "web_search",
|
||
"arguments": "{\"query\": \"deepseek v3.1 AI model performance benchmarks specifications\", \"max_results\": 5}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"summary\": \"🔍 搜索查询: deepseek v3.1 AI model performance benchmarks specifications\\n📅 搜索时间: 2025-10-08T19:17:14.931656\\n\\n📝 AI摘要:\\nDeepSeek V3.1 achieves 71.6% on Aider coding benchmark, outperforms proprietary models, and costs significantly less. It features a 685-billion-parameter architecture and excels in reasoning and context handling.\\n\\n---\\n\\n📊 搜索结果:\\n\\n1. DeepSeek-V3.1 is here. Here's what you should know.\\n 🔗 https://bdtechtalks.substack.com/p/deepseek-v31-is-here-heres-what-you\\n 📄 Performance that rivals the proprietary giants\\n\\nEarly benchmarks show DeepSeek-V3.1 achieving a 71.6% score on the Aider coding benchmark. This result places it slightly ahead of proprietary models li...\\n\\n2. deepseek-v3.1 Model by Deepseek-ai\\n 🔗 https://build.nvidia.com/deepseek-ai/deepseek-v3_1/modelcard\\n 📄 | Category | Benchmark (Metric) | DeepSeek V3.1-NonThinking | DeepSeek V3 0324 | DeepSeek V3.1-Thinking | DeepSeek R1 0528 |\\n --- --- --- |\\n| General | | | | | |\\n| | MMLU-Redux (EM) | 91.8 | 9...\\n\\n3. DeepSeek V3.1: The New Frontier in Artificial Intelligence\\n 🔗 https://deepseek.ai/blog/deepseek-v31\\n 📄 Through advanced training techniques and architectural improvements, DeepSeek V3.1 demonstrates a 38% reduction in hallucinations compared to our previous model. This means more factual accuracy and r...\\n\\n4. DeepSeek V3.1 Complete Evaluation Analysis: The New AI ...\\n 🔗 https://dev.to/czmilo/deepseek-v31-complete-evaluation-analysis-the-new-ai-programming-benchmark-for-2025-58jc\\n 📄 Performance Breakthrough: DeepSeek V3.1 achieves 71.6% pass rate in Aider programming tests, surpassing Claude Opus\\n Cost Advantage: 68 times cheaper than Claude Opus, with total testing cost of onl...\\n\\n5. deepseek-ai/DeepSeek-V3.1\\n 🔗 https://huggingface.co/deepseek-ai/DeepSeek-V3.1\\n 📄 ### | DeepSeek V3.1-NonThinking | DeepSeek V3 0324 | DeepSeek V3.1-Thinking | DeepSeek R1 0528 |\\n --- --- --- |\\n| General | | | | | |\\n| | MMLU-Redux (EM) | 91.8 | 90.5 | 93.7 | 93.4 |\\n| | MM...\"}",
|
||
"timestamp": "2025-10-08T19:17:14.936965",
|
||
"tool_call_id": "web_search:0",
|
||
"name": "web_search"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-10-08T19:17:16.853448",
|
||
"tool_calls": [
|
||
{
|
||
"id": "web_search:1",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "web_search",
|
||
"arguments": "{\"max_results\": 5, \"query\": \"qwen 2.5 max AI model performance benchmarks specifications parameters\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"summary\": \"🔍 搜索查询: qwen 2.5 max AI model performance benchmarks specifications parameters\\n📅 搜索时间: 2025-10-08T19:17:21.559884\\n\\n📝 AI摘要:\\nQwen2.5-Max is a large language model with advanced performance. It surpasses other models in benchmarks and is available for use via API. It offers scalable pricing for businesses.\\n\\n---\\n\\n📊 搜索结果:\\n\\n1. Qwen2.5-Max: Exploring the Intelligence of Large-scale MoE Model\\n 🔗 https://qwenlm.github.io/blog/qwen2.5-max/\\n 📄 Qwen2.5-Max: Exploring the Intelligence of Large-scale MoE Model | Qwen Qwen2.5-Max: Exploring the Intelligence of Large-scale MoE Model * [简体中文](https://qwenlm.github.io/zh/blog/qwen2.5-max/)  * [Free Courses](https://opencv.org/university/free-cours...\"}",
|
||
"timestamp": "2025-10-08T19:17:21.568635",
|
||
"tool_call_id": "web_search:1",
|
||
"name": "web_search"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-10-08T19:17:23.352063",
|
||
"tool_calls": [
|
||
{
|
||
"id": "web_search:2",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "web_search",
|
||
"arguments": "{\"max_results\": 5, \"query\": \"kimi k2 AI model performance benchmarks specifications moonshot\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"summary\": \"🔍 搜索查询: kimi k2 AI model performance benchmarks specifications moonshot\\n📅 搜索时间: 2025-10-08T19:17:28.729891\\n\\n📝 AI摘要:\\nKimi K2 is a 1T parameter open-weight model excelling in advanced reasoning, math, and coding. It outperforms proprietary models on high-difficulty benchmarks. Kimi K2 is available in Cline for coding tasks.\\n\\n---\\n\\n📊 搜索结果:\\n\\n1. Kimi-k2 Benchmarks explained\\n 🔗 https://medium.com/data-science-in-your-pocket/kimi-k2-benchmarks-explained-5b25dd6d3a3e\\n 📄 **Tau-2 Bench** focuses on advanced reasoning tasks like planning, multi-step problem solving, and tool use. ### **AIME 2025** and **GPQA-Diamond** are high-difficulty benchmarks aimed at testing the ...\\n\\n2. Kimi K2: Open Agentic Intelligence - Moonshot\\n 🔗 https://moonshotai.github.io/Kimi-K2/\\n 📄 Imagine using Kimi K2 to explore remote-work salaries with the Salary Data Analysis example, where 16 IPython calls generate stats, visualizations, and an interactive webpage of insights. For pre/post...\\n\\n3. Moonshot's Kimi K2 for Coding: Our First Impressions in Cline\\n 🔗 https://cline.bot/blog/moonshots-kimi-k2-for-coding-our-first-impressions-in-cline\\n 📄 # Moonshot's Kimi K2 for Coding: Our First Impressions in Cline Test Kimi K2, the open-source model achieving 65.8% on SWE-bench. Moonshot AI's Kimi K2 is now available in Cline. Kimi K2's coding perf...\\n\\n4. Kimi K2, An Open-weight Agentic Model From Moonshot AI\\n 🔗 https://www.digitalocean.com/community/tutorials/kimi-k2-moonshot-ai-agentic-open-weight-model\\n 📄 Kimi K2 is a 1T parameter open-weight model designed for agentic use – achieving state-of-the-art performance in frontier knowledge, math, and coding. As a result, the K2 researchers incorporated QK-C...\\n\\n5. Kimi K2 - Intelligence, Performance & Price Analysis\\n 🔗 https://artificialanalysis.ai/models/kimi-k2\\n 📄 Kimi K2 Model Details * [![Image 29: Alibaba logo](https://artificialanalysis.ai/img/logos/alibaba_small.svg...\"}",
|
||
"timestamp": "2025-10-08T19:17:28.737131",
|
||
"tool_call_id": "web_search:2",
|
||
"name": "web_search"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-10-08T19:17:31.363450",
|
||
"tool_calls": [
|
||
{
|
||
"id": "web_search:3",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "web_search",
|
||
"arguments": "{\"max_results\": 5, \"query\": \"gemini 2.5 pro AI model performance benchmarks specifications google\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"summary\": \"🔍 搜索查询: gemini 2.5 pro AI model performance benchmarks specifications google\\n📅 搜索时间: 2025-10-08T19:17:36.833844\\n\\n📝 AI摘要:\\nGemini 2.5 Pro is Google's advanced AI model known for enhanced reasoning and advanced coding capabilities. It is available on Google Cloud's Vertex AI. Pricing and performance benchmarks are proprietary.\\n\\n---\\n\\n📊 搜索结果:\\n\\n1. Gemini 2.5: Our most intelligent AI model - Google Blog\\n 🔗 https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025/\\n 📄 [Skip to main content](https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025/#jump-content) [Share](https://blog.google/technology/google-deepmind/gemini-model-thinki...\\n\\n2. Gemini 2.5 Pro | Generative AI on Vertex AI - Google Cloud\\n 🔗 https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-pro\\n 📄 * [English](https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-pro) * [Deutsch](https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-pro?hl=de) * [Italian...\\n\\n3. Gemini 2.5 Pro - Google DeepMind\\n 🔗 https://deepmind.google/models/gemini/pro/\\n 📄 [ Models Advanced audio ...\\n\\n4. Gemini 2.5 Pro - Intelligence, Performance & Price Analysis\\n 🔗 https://artificialanalysis.ai/models/gemini-2-5-pro\\n 📄 Analysis of Google's Gemini 2.5 Pro and comparison to other AI models across key metrics including quality, price, performance (tokens per second & time to first token), context window & more. ## Arti...\\n\\n5. Gemini Models | Gemini API - Google AI for Developers\\n 🔗 https://ai.google.dev/gemini-api/docs/models\\n 📄 * [English](https://ai.google.dev/gemini-api/docs/models) * [Deutsch](https://ai.google.dev/gemini-api/docs/models?hl=de) * [Français](https://ai.google.dev/gemini-api/docs/models?hl=fr) * [In...\"}",
|
||
"timestamp": "2025-10-08T19:17:36.842134",
|
||
"tool_call_id": "web_search:3",
|
||
"name": "web_search"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在让我搜索更多具体的性能对比数据：",
|
||
"timestamp": "2025-10-08T19:17:41.453460"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-10-08T19:17:41.459375",
|
||
"tool_calls": [
|
||
{
|
||
"id": "web_search:4",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "web_search",
|
||
"arguments": "{\"max_results\": 5, \"query\": \"\\\"AI model comparison\\\" \\\"deepseek v3.1\\\" \\\"qwen 2.5\\\" \\\"kimi k2\\\" \\\"gemini 2.5 pro\\\" benchmarks performance\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"summary\": \"🔍 搜索查询: \\\"AI model comparison\\\" \\\"deepseek v3.1\\\" \\\"qwen 2.5\\\" \\\"kimi k2\\\" \\\"gemini 2.5 pro\\\" benchmarks performance\\n📅 搜索时间: 2025-10-08T19:17:46.865889\\n\\n📝 AI摘要:\\nGemini 2.5 Pro excels in reasoning and response times, while Qwen 2.5 is faster for real-time coding tasks. Kimi K2 outperforms Gemini in specific coding benchmarks but is slower overall. DeepSeek V3.1 is noted for its reasoning power and efficiency.\\n\\n---\\n\\n📊 搜索结果:\\n\\n1. AI Showdown: Claude Sonnet 4 vs. Kimi K2 vs. Gemini 2.5 Pro\\n 🔗 https://www.arsturn.com/blog/ai-showdown-claude-sonnet-4-vs-kimi-k2-vs-gemini-2-5-pro\\n 📄 Here, Gemini 2.5 Pro seems to shine brightest, especially when it comes to complex reasoning & multimodal tasks. Google has built it with \\\"thinking\\\" capabilities, allowing it to evaluate different pos...\\n\\n2. Qwen2 5 Coder 7B vs 14B: Ultimate AI Model Comparison - BytePlus\\n 🔗 https://www.byteplus.com/en/topic/417608\\n 📄 BytePlus ModelArkNow supports DeepSeek-V3.1, Kimi-K2, and ByteDance-Seed-1.6\\n\\nGet 500k free tokens across all premium LLMs\\n\\nRefer friends to earn up to $45 in coupons\\n\\nClaim free tokens\\n\\nRelated links...\\n\\n3. Qwen vs LLaMA: AI Model Comparison 2025 - BytePlus\\n 🔗 https://www.byteplus.com/en/topic/504095\\n 📄 BytePlus ModelArkNow supports DeepSeek-V3.1, Kimi-K2, and ByteDance-Seed-1.6\\n\\nGet 500k free tokens across all premium LLMs\\n\\nRefer friends to earn up to $45 in coupons\\n\\nClaim free tokens\\n\\nRelated links...\\n\\n4. Kimi K2: The Trillion-Parameter Open-Weight LLM - Medium\\n 🔗 https://medium.com/@leucopsis/kimi-k2-the-trillion-parameter-open-weight-llm-9a656eb68cc5\\n 📄 Aug 9\\n\\n9\\n\\n1\\n\\nBarnacle Goose\\n\\n## GPT‑5‑Codex Review\\n\\n### GPT‑5‑Codex Review\\n\\nSep 15\\n\\n1\\n\\n1\\n\\nBarnacle Goose\\n\\n## DeepSeek V3.1 review and comparison with GPT-5, Gemini 2.5 Pro, Sonnet 4, K2, Grok 4, GPT-O...\\n\\n5. Claude Sonnet 4 vs Kimi K2 vs Gemini 2.5 Pro - Forge Code\\n 🔗 https://forgecode.dev/blog/kimi-k2-vs-sonnet-4-vs-gemini-2.5-pro/\\n 📄 | Metric | Gemini 2.5 Pro | Claude Sonnet 4 | Kimi K2 | Notes |\\n --- --- \\n| Avg tokens per request | 52,800 | 82,515 | ~60,200 | Claude consumed large input context and replied tersely |\\n| Input toke...\"}",
|
||
"timestamp": "2025-10-08T19:17:46.871282",
|
||
"tool_call_id": "web_search:4",
|
||
"name": "web_search"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在让我提取一些具体的网页内容来获取更详细的数据：",
|
||
"timestamp": "2025-10-08T19:17:48.625168"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-10-08T19:17:48.632888",
|
||
"tool_calls": [
|
||
{
|
||
"id": "extract_webpage:5",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "extract_webpage",
|
||
"arguments": "{\"url\": \"https://artificialanalysis.ai/models/kimi-k2\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": false, \"error\": \"网页提取返回了过长的179947字符，请不要提取这个网页\", \"char_count\": 179947, \"limit\": 80000, \"url\": \"https://artificialanalysis.ai/models/kimi-k2\"}",
|
||
"timestamp": "2025-10-08T19:17:51.674308",
|
||
"tool_call_id": "extract_webpage:5",
|
||
"name": "extract_webpage"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-10-08T19:17:55.555168",
|
||
"tool_calls": [
|
||
{
|
||
"id": "extract_webpage:6",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "extract_webpage",
|
||
"arguments": "{\"url\": \"https://www.arsturn.com/blog/ai-showdown-claude-sonnet-4-vs-kimi-k2-vs-gemini-2-5-pro\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"url\": \"https://www.arsturn.com/blog/ai-showdown-claude-sonnet-4-vs-kimi-k2-vs-gemini-2-5-pro\", \"content\": \"🌐 网页内容 (13117 字符):\\n📍 URL: https://www.arsturn.com/blog/ai-showdown-claude-sonnet-4-vs-kimi-k2-vs-gemini-2-5-pro\\n==================================================\\n[Arsturn](/)\\n\\n[How it works](/#how-it-works)\\n\\n---\\n\\n8/12/2025\\n\\n## The AI Showdown of 2025: Claude Sonnet 4 vs. Kimi K2 vs. Gemini 2.5 Pro\\n\\nWhat’s up, everyone? It feels like every other week there’s a new AI model that claims to be the next big thing, right? The pace is just INSANE. For anyone trying to keep up, it can be a real headache. Today, I want to cut through the noise & do a deep dive into three of the hottest models that are making waves right now: Anthropic’s Claude Sonnet 4, Moonshot AI’s Kimi K2, & Google’s Gemini 2.5 Pro.\\n\\nWe’re not just gonna look at the marketing fluff. We’re going to get into the nitty-gritty: speed, raw performance, coding skills, reasoning abilities, & the all-important price tag. I’ve been digging through benchmarks, Reddit threads, and hands-on reviews to figure out what’s what. So, grab a coffee, & let’s figure out which of these AI powerhouses is right for you.\\n\\n### Meet the Contenders: A Quick Intro\\n\\nBefore we pit them against each other, let’s get to know our players.\\n\\n**Claude Sonnet 4:** Released by Anthropic in May 2025, Sonnet 4 is positioned as the practical, all-around workhorse in the Claude 4 family. It’s designed to be a direct upgrade to the popular Claude 3.7 Sonnet, offering better coding & reasoning at the same price point. Think of it as the reliable, high-performance model for everyday tasks. Anthropic has pushed hard on making it a top-tier coding assistant, & the benchmarks seem to back that up.\\n\\n**Kimi K2:** This one’s the exciting underdog from a company called Moonshot AI. Kimi K2 is making a name for itself as a powerful, open-source model that’s SERIOUSLY affordable. It’s an \\\"agentic\\\" AI, meaning it's built to not just chat, but to *do* things—execute commands, edit code, & work with other tools. With a massive 1 trillion total parameters (but only activating 32 billion at a time to stay efficient), it’s a heavyweight contender that’s trying to democratize access to top-tier AI.\\n\\n**Gemini 2.5 Pro:** This is Google’s latest & greatest, their flagship model engineered for complex reasoning. Released in March 2025, Gemini 2.5 Pro is all about \\\"thinking\\\" before it speaks, which Google claims leads to much higher accuracy. Its biggest claims to fame are its massive 1-million-token context window & its native multimodal capabilities—meaning it can understand text, images, audio, & video all at once.\\n\\nAlright, now that we know who's in the ring, let's see how they stack up.\\n\\n### Round 1: Speed & Responsiveness - Who Keeps Up?\\n\\nHere’s the thing about AI models: it doesn’t matter how smart they are if they take forever to give you an answer. Speed is a HUGE factor, especially when you’re trying to stay in the flow.\\n\\nTurns out, this is a pretty clear win for **Gemini 2.5 Pro**. In tests on real-world coding tasks, Gemini 2.5 Pro consistently had the fastest response times, often delivering answers in just 3-8 seconds. It also had a very low time-to-first-token (TTFT), meaning it starts spitting out its response almost immediately, which makes it feel incredibly snappy.\\n\\n**Claude Sonnet 4** comes in second. It's respectably fast, with total response times in the 13-25 second range for typical coding prompts. One Reddit user noted that Sonnet 4 was about 2.8 times faster than Gemini in a head-to-head on complex Rust refactoring tasks, which is an interesting counterpoint. However, other tests show a noticeable \\\"thinking delay\\\" before it starts generating, which can make it feel a bit slower than Gemini.\\n\\n**Kimi K2** is, honestly, the slowest of the three. Response times are often in the 11-20 second range, which isn't terrible, but it's noticeably slower than Gemini. A Reddit review described its speed as \\\"painfully slow\\\" in comparison to Sonnet 4, clocking in at 34.1 output tokens per second versus Sonnet 4's 91. But, Kimi K2 does start streaming its response quickly, so you’re not just staring at a blank screen.\\n\\n**The Verdict on Speed:** If you need instant feedback & a fast, interactive experience, Gemini 2.5 Pro is your champ. Sonnet 4 is a solid performer, while Kimi K2 asks for a bit more patience in exchange for its other benefits.\\n\\n### Round 2: Coding & Development - The Ultimate Test for Modern AI\\n\\nLet’s be real, one of the biggest use cases for these models is writing & debugging code. This is where the rubber REALLY meets the road.\\n\\nThis is a much closer fight, but **Claude Sonnet 4** seems to have a slight edge in overall code quality & reliability. Across multiple benchmarks, like the highly-respected SWE-bench which tests models on real-world GitHub issues, Sonnet 4 consistently scores at the top, even beating its more powerful sibling, Opus 4. One developer who spent over $100 testing it on a massive Rust codebase found that Sonnet 4 had a 100% task completion rate & strictly followed all instructions. It produces clean, reliable code with minimal need for follow-up.\\n\\n**Gemini 2.5 Pro** is an absolute beast at coding too, & some would argue it’s the best. It’s particularly strong when you throw a massive amount of context at it, thanks to its 1-million-token window. This is a game-changer for working with large codebases. One user on Reddit raved about being able to throw over 20 files at it at once. However, it can sometimes be a bit *too* creative, modifying files it wasn't asked to touch or introducing unintended features. So, while powerful, it might require a bit more supervision.\\n\\n**Kimi K2** holds its own surprisingly well, especially for an open-source model. It’s been praised for its strong coding performance & its ability to handle agentic tasks, like planning & executing a code migration. In one test, it successfully implemented a frontend feature in one go, though it took longer than Sonnet 4. However, it can sometimes struggle with specific tool formats or require more follow-up prompts to get the job done perfectly.\\n\\n**The Verdict on Coding:** This is TOUGH. If you prioritize clean, reliable, production-ready code with minimal fuss, **Claude Sonnet 4** is likely your best bet. If you're working on massive codebases & need a model that can see the whole picture, **Gemini 2.5 Pro**'s context window is a killer feature. **Kimi K2** is a fantastic & affordable option for developers, especially those who value its open-source nature.\\n\\n### Round 3: Reasoning, General Tasks, & Other Capabilities\\n\\nOf course, we don’t just use these models for coding. How do they handle general reasoning, writing, summarization, & other tasks?\\n\\nHere, **Gemini 2.5 Pro** seems to shine brightest, especially when it comes to complex reasoning & multimodal tasks. Google has built it with \\\"thinking\\\" capabilities, allowing it to evaluate different possibilities before giving an answer. This makes it incredibly reliable for things like analyzing documents, strategic planning, or even understanding a bug from a screenshot. Its performance on reasoning benchmarks like GPQA is top-of-the-line.\\n\\n**Claude Sonnet 4** is also a very strong reasoner. It scores well on graduate-level reasoning tests (GPQA) & is known for its ability to follow instructions precisely. It’s a dependable choice for general-purpose tasks like writing, data analysis, & question answering. For many businesses, this level of reliability is crucial.\\n\\nFor instance, a company could use a model like Sonnet 4 as the brain for its internal knowledge base. But to make that knowledge accessible, they'd need a great user interface. This is where a platform like **Arsturn** comes in. Arsturn helps businesses build no-code AI chatbots trained on their own data. You could feed all your company documents into a system powered by a strong reasoning model, & Arsturn would provide the conversational front-end, allowing employees to get instant, accurate answers 24/7.\\n\\n**Kimi K2** shows impressive reasoning for an open-source model, particularly in math & STEM fields. However, some users have noted that its general reasoning can lag behind its coding abilities. Its main strength outside of coding seems to be its agentic nature—its ability to use tools & complete multi-step workflows.\\n\\n**The Verdict on Reasoning:** For the most advanced, multimodal reasoning, **Gemini 2.5 Pro** is the leader. **Claude Sonnet 4** is a highly reliable & versatile generalist. **Kimi K2** is more specialized, with its strengths lying in agentic tasks & technical domains.\\n\\n### Round 4: Pricing & Accessibility - The Bottom Line\\n\\nThis is where things get REALLY interesting, because the best model in the world is useless if you can’t afford to use it.\\n\\n**Kimi K2** is the undisputed champion of affordability. As an open-source model, it’s fundamentally cheaper to run. One comparison found Kimi K2 to be around 10 times cheaper than Sonnet 4 for the same task. The pricing is ridiculously low, something like $0.15-$0.60 per million input tokens & $2.50 for output. This is a HUGE deal for startups, indie developers, & anyone on a budget.\\n\\n**Gemini 2.5 Pro** is also very cost-effective, especially given its power. While pricing can vary, it's generally cheaper than Sonnet 4. One source cited it at $1.25 per million input tokens & $10 for output. The fact that it’s often faster & requires less iteration can also lead to a lower total cost of ownership.\\n\\n**Claude Sonnet 4** is the most expensive of the three. Its pricing is around $3 per million input tokens & $15 for output. While you're paying for top-tier reliability & code quality, that cost can add up quickly, especially for high-volume applications.\\n\\n**The Verdict on Pricing:** **Kimi K2** is in a league of its own for affordability. **Gemini 2.5 Pro** offers a great balance of performance & cost. **Claude Sonnet 4** is a premium option where you’re paying for that extra bit of polish & reliability.\\n\\n### The \\\"X-Factor\\\": Unique Strengths & Business Applications\\n\\nBeyond the core metrics, each model has something unique to offer.\\n\\n* **Gemini 2.5 Pro's Multimodality & Context Window:** This is its superpower. The ability to process a million tokens & understand images, audio, & video opens up entirely new use cases. Imagine a customer support scenario where a user can just show a video of a broken product. A business could build a system around Gemini to analyze the video & provide instant troubleshooting steps. This is the kind of next-level customer experience that wins loyalty.\\n* **Kimi K2's Open-Source Freedom:** Being open-source means you can customize it, self-host it for privacy, & avoid vendor lock-in. For companies with strict data security requirements or those who want to build deeply integrated, custom AI solutions, this is a massive advantage.\\n* **Claude Sonnet 4's Reliability & \\\"Just Works\\\" Factor:** Sonnet 4's biggest strength might be its predictability. It does what you ask, does it well, & doesn't go off the rails. This is GOLD in a production environment. When you're building customer-facing tools, you need that reliability.\\n\\nThis is especially true for businesses looking to automate customer interactions on their websites. You can't have a chatbot giving weird or wrong answers. This is where combining a reliable model with a robust platform is key. For example, **Arsturn** helps businesses create custom AI chatbots that provide instant customer support. By training a chatbot on your company's data—FAQs, product docs, policies—you can provide a reliable, Sonnet-4-level of accuracy. An **Arsturn** chatbot can engage with website visitors 24/7, answer their questions instantly, & even help with lead generation, ensuring a consistent & high-quality customer experience every single time.\\n\\n### So, Who Wins?\\n\\nHonestly, there’s no single winner here. The \\\"best\\\" model COMPLETELY depends on what you need.\\n\\n* **Choose Claude Sonnet 4 if:** You need the most reliable, production-ready code. You're working in a team where predictability & minimizing code review overhead are critical. You prioritize quality & are willing to pay a premium for it.\\n* **Choose Kimi K2 if:** You are on a budget. You're a startup, an indie hacker, or a researcher. You value the freedom & customizability of open-source AI & are willing to trade a bit of speed for massive cost savings.\\n* **Choose Gemini 2.5 Pro if:** You need raw speed & the ability to process enormous amounts of information. Your use case involves multimodality (text, images, video). You want the most powerful reasoning engine for complex analytical tasks & can handle a bit of unpredictability.\\n\\nThe AI landscape is moving at lightning speed, & these three models represent the incredible choices we have at our fingertips. Each one is a powerhouse in its own right, pushing the boundaries of what's possible.\\n\\nHope this was helpful! This stuff is super exciting, & I'm curious to see what comes next. Let me know what you think in the comments below\\n\\n---\\n\\nArsturn.com/\\n\\n[Claim your chatbot](/dashboard)\\n\\n[Benefits](/#benefits)\\n[How it works](/#how-it-works)\\n[Testimonials](/#testimonials)\\n[FAQ](/#faq)\\n[Pricing](/#pricing)\\n\\n[Solutions](/solutions)\\n[Blog](/blog)\\n\\nCopyright © [Arsturn](https://arsturn.com/)2025\\n==================================================\"}",
|
||
"timestamp": "2025-10-08T19:17:57.814881",
|
||
"tool_call_id": "extract_webpage:6",
|
||
"name": "extract_webpage"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在让我创建一个精美的HTML页面来展示这些AI模型的对比信息：",
|
||
"timestamp": "2025-10-08T19:18:00.940305"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-10-08T19:18:00.944297",
|
||
"tool_calls": [
|
||
{
|
||
"id": "create_file:7",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "create_file",
|
||
"arguments": "{\"path\": \"ai_models_comparison.html\", \"file_type\": \"html\", \"annotation\": \"AI模型对比页面 - 包含DeepSeek V3.1、Qwen 2.5 Max、Kimi K2、Gemini 2.5 Pro的详细对比\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"path\": \"ai_models_comparison.html\", \"size\": 0, \"message\": \"已创建空文件: ai_models_comparison.html。请使用 append_to_file 追加正文内容，或使用 modify_file 进行小范围替换。\"}",
|
||
"timestamp": "2025-10-08T19:18:02.771823",
|
||
"tool_call_id": "create_file:7",
|
||
"name": "create_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-10-08T19:18:06.824684",
|
||
"tool_calls": [
|
||
{
|
||
"id": "append_to_file:8",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "append_to_file",
|
||
"arguments": "{\"path\": \"ai_models_comparison.html\", \"reason\": \"创建完整的AI模型对比HTML页面，包含交互式图表和动画效果\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"awaiting_content\": true, \"path\": \"ai_models_comparison.html\", \"message\": \"\\n请按照以下格式输出需要追加到文件的完整内容，禁止输出任何解释性文字：\\n<<<APPEND:ai_models_comparison.html>>>\\n(在此行之后紧接着写入要追加的全部内容，可包含多行代码)\\n<<<END_APPEND>>>\\n若追加完成后仍需继续写入，请再次调用 append_to_file。\"}",
|
||
"timestamp": "2025-10-08T19:18:08.639398",
|
||
"tool_call_id": "append_to_file:8",
|
||
"name": "append_to_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "已向 ai_models_comparison.html 追加 565 行（17487 字节）。未检测到结束标识，已在输出结束处完成写入，请补充<<<END_APPEND>>>。",
|
||
"timestamp": "2025-10-08T19:19:49.257570"
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"path\": \"ai_models_comparison.html\", \"lines\": 565, \"bytes\": 17487, \"forced\": true, \"message\": \"已向 ai_models_comparison.html 追加 565 行（17487 字节）。未检测到结束标识，已在输出结束处完成写入，请补充<<<END_APPEND>>>。\"}",
|
||
"timestamp": "2025-10-08T19:19:49.265561",
|
||
"tool_call_id": "append_to_file:8",
|
||
"name": "append_to_file"
|
||
},
|
||
{
|
||
"role": "system",
|
||
"content": "刚才向 ai_models_comparison.html 追加的内容未检测到 <<<END_APPEND>>>，请继续输出剩余部分，并以 <<<END_APPEND>>> 正确闭合。",
|
||
"timestamp": "2025-10-08T19:19:49.270716"
|
||
}
|
||
],
|
||
"metadata": {
|
||
"project_path": "/Users/jojo/Desktop/agents/正在修复中/agent_kimi_源码备份/project",
|
||
"thinking_mode": true,
|
||
"total_messages": 26,
|
||
"total_tools": 19,
|
||
"status": "active"
|
||
},
|
||
"token_statistics": {
|
||
"total_input_tokens": 179050,
|
||
"total_output_tokens": 4947,
|
||
"updated_at": "2025-10-08T19:24:35.104332"
|
||
}
|
||
} |