跳到主要内容 Kimi K3 Is Impressive, but "Better and Much Cheaper" Is Too Simplistic : r/LLMDevs 在 Reddit 上投放广告 打开聊天 创建 创建帖子 打开收件箱 展开用户菜单 转载 转到“LLMDevs” r/LLMDevs • 22天前 s243a Kimi K3 Is Impressive, but "Better and Much Cheaper" Is Too Simplistic Kimi K3 令人印象深刻，但“更好且便宜得多”这一说法过于简单化 Discussion
Kimi K3 is getting a lot of hype. Some claims say it beats Fable 5, GPT-5.6 Sol, even Opus 5. I don't buy the strong version. My read: Kimi K3 sits between the previous frontier tier (Opus 4.8 / GPT-5.5) and the current one (Fable 5 / GPT-5.6 Sol), genuinely good, but not quite there. On Artificial Analysis's Intelligence Index, Kimi scores 57, behind both Fable 5 and GPT-5.6 Sol, roughly level with Opus 4.8 and GPT-5.5. x Kimi K3 正在引发大量关注。有说法称它超越了 Fable 5、GPT-5.6 Sol，甚至 Opus 5。我并不认同这种过于夸张的说法。我的看法是：Kimi K3 处于上一代前沿水平（Opus 4.8 / GPT-5.5）与当前前沿水平（Fable 5 / GPT-5.6 Sol）之间，确实表现良好，但尚未达到顶尖水准。根据 Artificial Analysis 的智力指数，Kimi 的得分为 57，低于 Fable 5 和 GPT-5.6 Sol，大致与 Opus 4.8 和 GPT-5.5 持平。
The benchmark headline problem 基准测试标题问题
"Kimi beats Fable at X" often hides which X: frontend generation, a specific harness, an effort setting, or pass@k with multiple attempts allowed. DeepSWE shows this clearly, and the cost evidence here is genuinely mixed. “Kimi 在 X 上超越 Fable”这一说法常常掩盖了具体的 X 是什么：是前端生成、特定的工具链、某种设置，还是允许多次尝试的 pass@k？DeepSWE 对此有清晰展示，而此处的成本证据确实参差不齐。
In one Kimi K3 Max vs GPT-5.6 Sol Max comparison, Sol wins pass@1 (72.7% vs 68.5%), but Kimi is cheaper per rollout ($4.65 vs $8.37) and pulls ahead at higher pass@k. A separate small programming micro-benchmark found Sol cheaper per correct answer than Kimi — but that wasn't DeepSWE, so it shouldn't be generalized. These aren't necessarily contradictory; they measure different things: one high-confidence attempt vs several cheap ones, cost-per-rollout vs cost-per-correct-solve. Anyone citing a single DeepSWE cost number without specifying which is skipping the part that matters. linkedin 在一次 Kimi K3 Max 与 GPT-5.6 Sol Max 的对比中，Sol 在 pass@1 上表现更优（72.7% vs 68.5%），但 Kimi 每轮推理成本更低（4.65 美元 vs 8.37 美元），且在更高阶的 pass@k 指标上反超。另一项小型编程微基准测试发现，Sol 每正确答案的成本低于 Kimi——但该测试并非 DeepSWE，因此不能推广。这些结果并不必然矛盾，因为它们衡量的是不同方面：一次高置信度尝试 vs 多次低成本尝试，每轮推理成本 vs 每次正确解答成本。任何仅引用单一 DeepSWE 成本数据而不说明具体指标的人，都跳过了真正关键的部分。linkedin
Why I still rank it below 为什么我仍然认为它排名低于
Interesting programming pulls from math, algorithms, systems tradeoffs, and domain knowledge outside the codebase. That's why broader reasoning benchmarks matter even for coding. They're a proxy for whether a model c an transfer concepts when a task isn't "edit this function" but "figure out the right approach first." 来自数学、算法、系统权衡以及代码库之外领域知识的有趣编程技巧，正是为什么即使在编程领域，更广泛的推理基准测试也至关重要。它们是衡量模型能否在任务并非“修改这个函数”而是“首先确定正确方法”时，成功迁移概念的代理指标。
The gap here is concrete. Fable 5 scored 88% on FrontierMath Tier 4, about 13 points above GPT-5.5's ~75%. Artificial Analysis also has Fable 5 leading its AA-Omniscience knowledge benchmark. GPT-5.6 Sol trails Fable by roughly a point on the aggregate Intelligence Index while costing about a third as much, and it topped GeneBench-Pro, a hard genomics/quantitative-biology benchmark, at 31.5% — a decent proxy for general scientific reasoning, if not coding directly. aiweekly 这里的差距是实实在在的。Fable 5 在 FrontierMath Tier 4 上得分为 88%，比 GPT-5.5 的 75% 高出约 13 个百分点。人工分析（Artificial Analysis）的 AA-Omniscience 知识基准测试中，Fable 5 也位居榜首。GPT-5.6 Sol 在综合智能指数上落后 Fable 约 1 个百分点，但成本仅为后者的三分之一，并在 GeneBench-Pro 这一难度较高的基因组学/定量生物学基准测试中取得 31.5% 的成绩——这虽非直接编码能力，但可作为通用科学推理能力的一个合理参考。aiweekly
Kimi K3 doesn't show up as a contender on any of these. Its strengths sit in a different lane: frontend generation, some agentic coding, not the deep cross-domain reasoning the newest tier is winning on. That's the real basis for ranking it below Fable 5 and GPT-5.6 Sol: not just index position, but a measured gap in the cross-disciplinary reasoning that separates "good coding agent" from "frontier model." Kimi K3 在这些榜单中并未被视为竞争者。它的优势在于前端生成、部分代理式编程，而非最新一代模型所胜出的深度跨领域推理能力。这才是它排名低于 Fable 5 和 GPT-5.6 的真正原因：不仅仅是索引位置的差距，更在于跨学科推理能力上的实质性差距，正是这一差距将“优秀的编码代理”与“前沿模型”区分开来。
API price ≠ task price API 价格 ≠ 任务价格
Kimi's tokens are cheap ($3/$15 per million vs Sol's $5/$30). But cheaper tokens don't guarantee cheaper tasks — longer runs, more turns, more retries eat the margin. Artificial Analysis found Kimi and Sol nearly tied on cost per task ($0.94 vs $1.04), despite the sticker-price gap. My guess: Kimi's edge holds on short, easy, cache-friendly work, and shrinks as tasks get harder. myclaw Kimi 的 token 价格便宜（每百万$3/$15，对比 Sol 的$5/$30）。但更便宜的 token 并不意味着任务成本更低——更长的运行时间、更多的对话轮次和重试次数会吃掉利润空间。Artificial Analysis 发现，尽管存在标价差距，Kimi 和 Sol 在每项任务的成本上几乎持平（分别为$0.94 vs $1.04）。我的猜测是：Kimi 在短小、简单且缓存友好的任务上仍具优势，但随着任务难度增加，这一优势会逐渐缩小。myclaw
Subscriptions are murkier still 订阅服务更是模糊不清
I burned 6.87% of my monthly Moderato quota in a few hours doing GitHub-connected code review. That's not a controlled benchmark, just one real data point. Kimi's docs confirm Agent, Deep Research, Kimi Code, and connectors all draw from one shared cr edit pool metered by token use. A $19/month price tells you little about how far that actually goes in real agentic work. kimi 我用了几个小时进行连接 GitHub 的代码审查，就消耗了每月 Moderato 配额的 6.87%。这并非受控基准测试，只是一个真实数据点。Kimi 的文档确认，Agent、Deep Research、Kimi Code 和各类连接器都共享同一个信用额度池，按 token 使用量计费。每月 19 美元的价格并不能说明实际在智能体工作中的使用范围究竟有多大。kimi
One aside: engineer vs. scientist 顺便一提：工程师与科学家
Subjectively, Claude tends to commit to a complete implementation in one pass; GPT/Codex explores well but often needs more "continue" prompts to finish. That changes effective cost because finishing in one shot beats needing three follow-ups, even at a higher sticker price.
Bottom line
Kimi K3 is a legitimately strong near-frontier model, likely the better economic choice for easy-to-medium tasks. But "clearly better than Fable/Sol" and "obviously much cheaper" both overstate the evidence. DeepSWE cost comparisons point in different directions depending on setup — that's the actual state of the data, not a gap in this analysis. What would change my mind: a larger, harness-controlled study measuring cost-per-correct-completion across a real mix of easy and hard tasks.
共享 RedditforBusiness • 已推广 "I mean, I'd LOVE to try running ads on Reddit, but it's not like you can target by subreddit!!!!" 注册 ads.reddit.com 排序方式： 评论区域 Recoil42 • 22天前
Thanks Claude.
s243a • 22天前
Here is the thread that used 6.7% of my monthly usage (only pat of the conversation displayed):
https://www.kimi.com/share/19f9c2e8-52a2-84d5-8000-000090bb69e7
The link should give the full conversation. Here is part of it:
Prompt
GitHubFirst review the UnifyWeaver PR, "mu_cosine: filing path decoder Stage A + process-expression contract fixtures #4… 以上为必要节选，完整内容请查看原文。
I'll load the GitHub tools and pull up that PR. Found the repo (s243a/UnifyWeaver). Now let me pull up PR #4000. Got the PR description. Now let me look at the actual changes — the file list, CI status, and the diff itself. I've gone through the PR in full — the description, all 9 changed files (5,760 additions), and the CI runs (all green). Here's the explanation first, then my review.
What the PR description is actually saying
The setting
prototypes/mu_cosine is a research prototype inside UnifyWeaver that studies ML-assisted filing: given a bookmark and a frozen, previously-ranked menu of candidate folders (ranked by a frozen "e5" embedding model), decide where in a folder hierarchy it should be ecide where in a folder hierarchy it should be filed. The project is run with an unusually strict provenance/scientific-validity discipline: every artifact is hash-bound, every decision is advisory and receipted, and anything that smells like data leakage or unjustified automation is designed to fail closed (raise an error rather than degrade gracefully).
Two design documents govern this PR, and the PR's whole point is that it implements exactly what those documents authorize as the first engineering step — and nothing more:
DESIGN_filing_path_decoder_handoff.md §11 — the filing path decoder
DESIGN_expression_encoder_future.md §11 — the process-expression encoder (a planned learned encoder for "process expressions," the little DSL strings like e5(routing(e5,haiku,t=[0.02],menus=[10])) that describe composed scoring/routing pipelines)
Part 1 — "Stage A of the filing path decoder"
The decoder is planned in stages. Stage A is the most conservative possible slice: it never creates folders, never mutates the graph, never calls an external API, and can only emit two outcomes:
SELECT_EXISTING — "file it in this existing folder," where the folder is simply the first eligible candidate in the parent task's frozen ranking order (no re-scoring, no re-ranking), and
ABSTAIN — with a typed reason code (ambiguous_existing, no_eligible_candidate, resource_censored, …).
...
No_Inspection4415 • 17天前
Is Opus 5 a better model in your opinion?
s243a • 16天前
I think the models that are better than kimi k3 are Fable 5, Opus 5 and GPT 5.6 dol. That said, I've only been using kimi to explain to me the stuff that claude or gpt are doing, kind of like a reviewer. I do this using the github plugin in the chat. I find kimi k3 as a reviewer is better than perplexity but I don't have access to the top models on my perplexity plan. Perplexity computer is impressive but costly.
I saw a benchmark agregate saying that Grok 4.5 is better than kimi k3. I'm suspicious of that result. For one thing I don't like Grok's writing style, and I only use Grok as a worker under the direction of gpt or claude. I don't think grok's even as good as opus 4.8 but that's probably a vibes based opinion since I know grok 4.5 has it's fans.
While I use gpt claude and kimi a fair amount, I feel my opinion on kimi is somewhat vibes based because I haven't used kimi yet as a coding agent.
habeeb-s • 7天前
What models do you you use for what kind of tasks and why?
创建于 2023年2月17日 公共 6万1497 用户标识 1llegi
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Kimi K3
