Kimi K3.1 Pro kya hai
Kimi K3.1 Pro Moonshot AI ka sabse naya flagship model hai, aur K3.1 series ka pehla model. Ye Kimi K3 jaisa hi base architecture rakhta hai — yani ye koi bilkul naya model nahi, balki purane strong foundation par zyada post-training compute laga kar banaya gaya version hai. Moonshot ki official language me ise "post-trained version of Kimi K3, with substantially larger post-training compute" kaha gaya hai. Yaani AI ko coding, finance aur 3D modeling jaise sainkaron nichhat domains par alag se train kiya gaya hai. Iski 3 khaas baate ye hai:- Parameters: 2.8 trillion total, jinme sirf 104 billion per token activate hote hai (Mixture of Experts — MoE design)
- Context window: 10,48,576 tokens — matlab lagbhag ek million tokens (approx 700-8 lakh words English me)
- Multimodal: sirf text nahi — image aur video files bhi input me le sakta hai
| Spec | Kimi K3.1 Pro |
|---|---|
| Banane wali company | Moonshot AI |
| Launch date | 23 October 2026 (China me 24 October) |
| Parameters | 2.8T total / 104B active (MoE) |
| Context window | 10,48,576 tokens (1M) |
| Input types | Text, image, video files |
| Variants | K3.1 Pro aur K3.1 Pro Highspeed (2-3x tez) |
| API model IDs | kimi-k3.1-pro, kimi-k3.1-pro-highspeed |
| Open weights | 4 November 2026 tak promised |
| Sabse strong field | Coding (full project), agentic work, finance |
Kimi K3.1 Pro ke peeche ki kahani (launch timeline)
Kimi ki family ko sequence me dekhoge to story samajh aati hai. Sabse pehle Kimi K2 ne bawaal machaya tha jab wo duniya ke strongest open-weight models me gin-a jana laga. Uske baad Kimi K2 Thinking ne aisi kamaal ki performance di ki multiple benchmarks me open-source model number 1 pahunch gaya — bade closed models ko direct challenge. Phir July 2026 me Kimi K3 aaya — 2.8 trillion parameters ka flagship jiska context window ek million tokens tak tha. Aur ab Kimi K3.1 Pro usi architecture par zyada post-training compute ke saath 4 November ka full open-weights promise lekar aaya hai. Timeline-wise poora safar:- 16 July 2026: Kimi K3 launch hua — 2.8T parameters, 1M context, aur usa waqt hi weights ki date bhi announce.
- 27 July 2026: K3 ke open weights published — launch ke 11 dino ke andar, promise pakka. (Ye chize baad kaam aayegi — aage dikhata hoon.)
- September-Oct 2026: GPT-6 Astra, GPT-6 Sol/Luna aur bade frontier launches ka bhari-bharkam mahina.
- Oct ke pehli 3 hafta: DeepSeek V4.1-Flash (17), Meta Muse Spark Open (15), Gemini 4 Argon GA (13), GPT-6 Pro Parallel Agents (16), Claude Duet (21) — October me 6 bade releases.
- 23 October 2026: Kimi K3.1 Pro launch — open-weights crown ka agla contender.
- 4 November 2026: K3.1 Pro ke full open weights ka promised date.
Kimi K3.1 Pro price — API, Highspeed aur app plans (October 2026)
Ab aao sab-se zyada Google kiye jane wale sawal ko — price. Kimi K3.1 Pro ka API pricing number per 1 million tokens par aata hai. Yaad ho to AI companies token ke hisab se paise lete hai — input (aap jo bhejoge) alag aur output (AI jo jawab dega).| Model | Input (per 1M tok) | Output (per 1M tok) | Cached input (per 1M) |
|---|---|---|---|
| Kimi K3.1 Pro | $2.00 | $10.00 | $0.20 |
| Kimi K3.1 Pro Highspeed | $4.00 | $20.00 | $0.20 |
| Kimi K3 (purana) | $3.00 | $15.00 | $0.30 |
- Sasta rate bahut: K3 se har rate 33% kam — input, output aur cached teeno. $2/$10 ab is price range me kaafi competitive ho gayi.
- Flat pricing: 1M context me kuch bhi bheje – lambi prompt ya chhoti — same $2/$10 hi rate hoga, koi long-context surcharge nahi.
- Highspeed = double price: jab aapko jawab super fast chahiye (production apps, live chat) tab Highspeed lo, warna normal Pro kaafi hai.
Kimi K3.1 Pro benchmarks — Moonshot ke claim vs independent results
Benchmark samjhe bina koi model judge nahi hota. Yahan do alag sources hai: Moonshot ki apni internal numbers, aur Artificial Analysis (AA) ke independent tests. AA ek third-party lab hai jo new models ko same conditions par test karta hai — isliye uska number zyada bharoseman mana jata hai. Dono ke numbers niche diye hai, aur jahan farak hai wahan main dono bata raha hoon. Sabse pehle independent AA Index: Kimi K3.1 Pro ka score 55.1 aaya — poore tracked models me 4th position. Upar sirf 3 models hai: Claude Opus 5.5 (57.6), Claude Sonnet 5.5 (56.0) aur Meta ka Muse Wonder Full (55.8). Kimi K3 pichhli baar 43.6 par tha, matlab +11.5 points ka jump. Ye open-weight models history ka sabse bada independent jump hai.| Benchmark | Kimi K3.1 Pro | Mukabla | Kisne test kiya |
|---|---|---|---|
| AA Intelligence Index | 55.1 (4th) | Claude Opus 5.5 = 57.6 | Artificial Analysis |
| Terminal-Bench 4.0 | 67% (highest) | Claude Sonnet 5.5 = 64% | Artificial Analysis |
| Terminal-Bench 4.0 (Moonshot) | 65.7% | Claude Opus 5.5 = 66.4% | Moonshot |
| GDPval-AA (office tasks) | 1,846 Elo (3rd) | Claude Opus 5.5 = 1,866 | Artificial Analysis |
| AutomationBench-AA (SaaS) | 73% (K3 = 58%) | — | Artificial Analysis |
| Humanity's Last Exam | 52% (K3 = 47%) | — | Artificial Analysis |
| BenchCAD (3D, tools ke sath) | 92.7% | Claude Opus 5.5 = 96.2%, GPT-6 Astra = 95.9% | Moonshot |
| OfficeQA Pro v2 (finance) | 64.4 | Moonshot ne "state of the art" bola | Moonshot |
- Agent/coding work me Kimi top level: Terminal-Bench real terminal me coding agents ko test karta hai. AA ke measurement me K3.1 Pro ka 67% unme sabse highest score — Claude Sonnet 5.5 (64%) se aage. Moonshot ki apni testme 65.7% thoda kam tha par dono measurement me top-3 me hai.
- Real-world office tasks me strong: GDPval-AA me 3rd position — Claude Opus 5.5 se sirf 20 Elo peeche. AutomationBench 15% ka jump (58% to 73%) bhi dikhata hai ki practical workflows handle achhe se ho rahe hai.
- Har jagah top nahi: 3D reconstruction (BenchCAD) me Claude aur GPT-6 Astra aagay hai, aur knowledge-type exams (HLE, SciCode) me gain moderate hai. K3.1 Pro coding aur agent ka champion hai, sab kuch ka nahi.
Kimi K3.1 Pro kis kaam me sabse achha hai
Moonshot ne launch post me teen fields ka naam liya jahan K3.1 Pro ka biggest gain hua. Ek-ek karke dekho: 1. Poore project ka coding. Terminal-Bench me coding agents ko asli terminal me tasks diya jata hai — files banana, commands chalana, errors fix karna aur poora workflow chalana. K3.1 Pro yahan 67% par pahuncha jabki Kimi K3 sirf 13% par tha. Moonshot ka dava hai ki ye model poore project-level coding de sakta hai — Unreal, Unity aur Godot me games banana tak. Yaani aap bolo aur model direct working project me code deta hai. 2. 3D modeling aur CAD. Moonshot ke BenchCAD test me K3.1 Pro ne 92.7% score kiya — jisme model Blender aur CAD software jaisa parametric modeling output deta hai. Is field me Claude Opus 5.5 (96.2%) aur GPT-6 Astra (95.9%) aage hai, sach hai — par Kimi ab isi race me ghus gaya hai. Cheap prototyping aur idea-testing ke liye solid option ban raha hai. 3. Finance me kam hallucination. Third area finance hai. OfficeQA Pro v2 (finance-focused exam) me Moonshot ne 64.4 score claim kiya aur usko "state of the art" bola. Number wale documents me sabse jyada hallucination-risk hota hai, aur Moonshot ne hallucination claim 65% decrease ka bhi diya hai. Hallucination ka poora chart agli section me. Ek associated point bhi dekh lo — Artificial Analysis ne note kiya ki K3.1 Pro per-task zyada output tokens use karte hai (55K vs 48K). Matlab jab bhi is model se lamba kaam karwao, pehle tokens ginn lo — kyunki tokens zyada kharche lete hai. Tokens ginn ne ka free tool isi guide ke last section me bataya hai.Hallucination aur safety — dono taraf ke sach
Hallucination ka matlab: AI galat cheez ko bharosa ke saath sach bolna (fake source bana dene, wrong numbers bolna). Moonshot aur Artificial Analysis dono ne Kimi K3.1 Pro ka hallucination-rate measure kiya — aur dono ke numbers kaafi alag hai. Isliye dono aapko dikhata hoon, aur aap khud decide karo konsa weight dena hai. Moonshot ki apni internal test (Kimi Hallu Bench Hard) me hallucination rate K3 ke 23.4% se K3.1 Pro me 8.3% tak giri — launch post me "65% down" isi ko bola gaya. Moonshot ki hi API quickstart ek aur number de rahi hai (23.35% se 6.85% tak), aur us comment ke saath ek honest disclaimer: ye test galat-situations deliberately probe karte hai — daily usage ka overall error rate ye nahi batata. Artificial Analysis ka independent AA-Omniscience test: K3.1 Pro ka hallucination-rate 44% (K3: 53%) aur accuracy 48% se 51% tak hua. Ye improvement khud ke number se chota hai lekin bharose ke liye achha. Cyber safety (best part). Moonshot ne open-weight version release se pehle Kimi ke cyber/hacking training data deliberately remove kiya. Moonshot ke xdev-bench-hard test me (hacking exploit development) - cyber data hatane ke baad wala model sirf 81.0% pass@3 dikha, jabki complete version 90.5% resolve karta tha. Reminder: ye number Moonshot ke apne test se hai - kisi external lab ne abhi verify nahi kiya. Moonshot ka official statement hai: 'weights open aane wale model ki offensive cyber power badhe nahi chahiye'. Ye AI-safety community ke liye positive kadam manaa jaa raha hai. Aur ye sab publicly, kisi paid signup ke bina, sabke samne hota hai.Open weights 4 November 2026 — kya hoga iske baad
Ab tak Kimi K3.1 Pro sirf Moonshot ke apne surfaces pe chal raha hai — Kimi.com, mobile app, Kimi Work, Kimi Code aur API. Weights public nahi hue. Weights ka matlab samajhiye: model ka "dimaag ki file" jo aap apne server par download karke chala sakte hai, offline bhi. Moonshot ne announcement me date de di hai — 4 November 2026 tak full open weights release honge. Aur ye date boring nahi hai, kyunki Artificial Analysis ke projection ke hisab se jab weights aa jayenge tab Kimi K3.1 Pro open-weights models me sabse highest score wala model ban jayega. Open vs proprietary ka gap 12 points se 3 points tak bas jayega — aur ye poore AI industry ka bada moment hoga. Team ka promise track record kaise hai? Achha hi. Kimi K3 ko July 16 ko launch, aur weights ka promise 27 July tak ka diya — aur exactly wahi din Hugging Face par weights aa gaye. Yaani ke delay drama nahi. Aap 4 November ko Hugging Face par Kimi K3.1 Pro check kar sakte ho. Aur ek aur baat jo pichhle section me dikhayi thi — cyber/hacking data pehle hi weight se remove kar liya gaya hai, matlab jo weights 4 November ko aayengi, wo "better safety tuned" version hi hogi. Ye open-source community ke liye strong signal hoga: big Chinese labs ab safety-aware releases bhi kar rahe hai.Kimi K3.1 Pro use kaise kare — 4 tarike (free me bhi)
Ab practical side. 23 October launch ke baad aap Kimi K3.1 Pro ko 4 tarike se use kar sakte hai:- Browser me (sabse asaan). kimi.com kholo, signup karo aur chat shuru. Free "Adagio" plan me bhi model mil sakta hai — model picker me K3.1 Pro select karke try karo. Kharcha zero, aur pehla prompt aaj hi ho jayega.
- Kimi Work desktop app. Moonshot ne 3.1.0+ version ki desktop app release ki hai — Windows aur Apple silicon Mac par. Isme project files, folders aur multi-file workflows ke sath kaam hota hai. Intel Mac walon ke liye browser wala safe option hai.
- Kimi Code (terminal). Developer ho to terminal me Kimi Code chalao aur type karo: /model — wahan K3.1 Pro select karo. Coding agents directly terminal me chalte hai.
- API (developers ke liye). platform.kimi.ai par min top-up $1 karke dono model-ids call karo: kimi-k3.1-pro ya kimi-k3.1-pro-highspeed. Context caching automatically on hota hai, Zero Data Retention bhi on kar sakte hai.
Kimi K3.1 Pro vs ChatGPT vs Claude vs DeepSeek
Ab sabse badi baat: K3.1 Pro kis kis se takkar deta hai. Ye comparison October 2026 ki latest numbers par hai:| Model | AA Index | API in/out (per 1M) | Context | Open weight? |
|---|---|---|---|---|
| Claude Opus 5.5 | 57.6 (1st) | Premium (mehenga) | Bada | Nahi |
| Claude Sonnet 5.5 | 56.0 (2nd) | Mid-range | Bada | Nahi |
| Meta Muse Wonder Full | 55.8 (3rd) | Announce ke hisab se | Bada | Nahi (Spark Open open) |
| Kimi K3.1 Pro | 55.1 (4th) | $2 / $10 | 1M | 4 Nov tak (promised) |
| GPT-6 Astra | — | $10 / $50 | Bada | Nahi |
| DeepSeek V4.1-Flash | — | Sasta | — | Haan (MIT) |
- Budget best deal Kimi hai. Claude Opus 5.5 se sirf 2.5 points pichhe, par token rate Claude/GPT-6 se 3-5x sasta. Yaani quality approx same range, kharcha kaafi neeche.
- Open model hone ka asli faida. 4 November ke baad aap model ko apne server par download karke chala sakte ho — no rate-limits, na per-token billing, aur aapka data aapke hi infrastructure me rehta hai. Enterprise setups me data-privacy aur control sabse important hota hai.
- Kimi vs DeepSeek: Dono Chinese open-weight competitors hai. DeepSeek V4.1-Flash fast aur cheap hai; Kimi K3.1 Pro agentic coding tasks me strong leader hai. Jahan lamba terminal-level project kaam ho, wahan Kimi better rahega.
Kaun use kare Kimi K3.1 Pro?
Har role ke hisab se alag jawab: Developers: Terminal me coding agents ke liye Kimi Code + K3.1 Pro achha combo hai. Poore project-level code, autonomous fixes, aur AutomationBench me 73% score — budget developers ke liye strong positioning. Production workload par switch karne se pehle apne prompts pe khud test zaroor karo, kyunki har project ka pattern alag hota hai. Startups aur small teams: $2/$10 ki API rate startup budget me fit hoti hai. Jahan jawab tez chahiye (customer-facing app) wahan Highspeed lo — uska rate double hai lekin speed 2-3x hai, real-time ke liye actually smart move hai. Students aur researchers: 1M context ka matlab aap ek saath poora book-chapter, 120-page research paper ya bada code repo daal sakte ho. Kimi.com ke free tier se try karo. Hindi sawal-jawab bhi achhe se hota hai. Content writers: Blog drafts, SEO pages, social posts aur newsletters — quality decent. Par ek baat yaad rakho: AA-Omniscience ke hisab se hallucination rate abhi bhi 44% hai, isliye publish se pehle zaroori facts verify karo. Moonshot ka 8.3% wala number best-case scenario mana jaye to thik. AI hobbyists: 4 November ke baad full weights download honge — apne local server pe khud test kar sakte ho. Local privacy aur offline availability ka alag hi maza hai. Enthusiasts ke liye 4 Nov ko important date hai.Kimi K3.1 Pro ka kharcha pehle ginn kar dekho — aasan example
Ek chota practical hisaab. Maan lo aapke paas ek 3,000-word English document hai jo aapko roz Kimi me bhejna hai analysis ke liye, aur approx 1,000-word jawab chahiye. Rough tally: 3,000 words = approx 4,000 tokens input, aur 1,000 words = approx 1,300 tokens output. Roz ka kharcha K3.1 Pro ki rate se:- Input: (4,000 / 10,00,000) x $2 = $0.008
- Output: (1,300 / 10,00,000) x $10 = $0.013
- Daily total approx $0.021 — matlab ek mahine approx $0.63
5 galtiyan jo Kimi K3.1 Pro users karte hai (aur unka bachav)
Naye users bar-bar same galtiyan repeat karte hai. Ye 5 sabse common hai: Galti 1: Jaldi me faisla karna. Log K3.1 Pro ko 10 minute chala kar nazar andaza par faisla kar lete hai. Par agentic models ka asli performance tab samajh aata hai jab aap apne 3-4 real tasks do — coding, document analysis, research. Pehle compare karo, phir decision lo. Galti 2: Highspeed variant default me chalu rakhna. Highspeed double rate leta hai ($4/$20) — sirf tab use karo jab real-time speed zaroori ho (customer-facing apps, live chat). Baaki kaam me normal K3.1 Pro hi sahi hai. Galti 3: Cached rate ko ignore karna. Agar system-prompt har baar same hoga to context caching automatically on ho jata hai aur wala part $0.20/1M pe lagta hai — $2 se 10x sasta. Par template bar-bar badal doge to cache miss hoga. Isliye stable prefix rakho, dynamic content end me jodo. Galti 4: Har sawal me 1M context bharna. Lagta hai poora repo + 50-page PDF + purani chats sab daal doge to jawab better milega. Par lamba poora context token kharcha upar le jata hai aur accuracy bhi dilute ho sakti hai. Sirf related material dedo — targeted context hamesha jeetega. Galti 5: Marketing numbers par blind bharosa. Moonshot ke hallucination numbers (8.3%) best-case internal test se hai; independent AA test alag picture deta hai (44%). Dono ko jaano, aur apne tasks par khud test karo. Ye 5 bachaoge to K3.1 Pro ka sahi faida milega. Ab end me ek practical section — 1M context use karne ke asli tarike.1M context ke practical uses — real-life scenarios
1M tokens wala context window sirf ek spec number nahi hai — iske kuch real-world fayde kaam ke hai: Scenario 1: Poora codebase analysis. Aapki chhoti-mid SaaS app ke 40-50 files approx 1.5-3 lakh tokens banate hai. K3.1 Pro ko ek hi prompt me poora repo de kar poochho — "authentication flow me security bug kahan hoga?" Model multi-file ke cross-references track kar sakta hai, jo chote context wale models me chat-history jodkar bhi mushkil hota hai. Scenario 2: Legal aur tender documents. 200-page contract ya NIT (tender) document ek saath daalo. Model poore document ka cross-reference deke jawab dega — "page 87 wali clause page ka 12 ke liability clause se kaise conflict karti hai?" Yehi kaam manual me 3-4 ghante leta hai. Scenario 3: Multi-file project debugging. Error log, related source files aur database schema ek hi prompt me daalo. Model conversation shuru se end tak yaad rakhta hai — koi summarization-loss nahi. Coding agents isi se Terminal-Bench me 67% score kar paaye. Scenario 4: Long research sessions. 10 research papers, data tables aur aapke apne notes — sab uploads karke model se synthesis chahiye to context bharne ki zaroorat nahi, direct sab kuch se de sakte ho. Big plus: token rate poore context me flat hai, koi long-context surcharge nahi. Ek caution: lamba context me bhi model ko instructions shuru ya end me clear rakho (mid ke andar khaskar, kyunki "lost in the middle" effect aata hai). Aur jis part ke jawab ki zaroorat hai usse highlight karo — "focus on section 4" jaise pointers accuracy me help karte hai. Iska poora faida lene ka aasan formula: target task likho, sirf us se judi files dedo, instructions start/end me rakho, aur output format pehle bata do.Kimi K3.1 Pro ki limitations — pehle jaan lo, phir decide karo
Har model ki kamiyan hoti hai, aur imandaar review me wo bhi batana zaroori hai:- Knowledge exams me average: HLE (52%) aur knowledge-type benchmarks me Claude/GPT-6 aage hai. General knowledge, history, theory jaise kaamo ke liye frontier closed models behtar honge.
- 3D/CAD me 3rd position: BenchCAD 92.7% strong hai par Claude Opus 5.5 (96.2%) aur GPT-6 Astra (95.9%) se peechhe. Critical CAD kaam ho to isko final nahi maano, draft-quality maano.
- Hallucination rate abhi 44% (AA test): Improvement hua (53% se 44%) par fact-checking ka kaam khatam nahi hota. Finance, medical, legal jawab verify karke hi use karo.
- Weights abhi open nahi hue: 4 November tak aap sirf hosted version use kar sakte hai — self-hosting, fine-tuning aur on-premise deployment ke options tab khulenge.
- Per-task token heavy: 55K output tokens per task (14% zyada) — lamba workflows me cost estimate karte waqt ise ginn ke hi likho, warna bill surprise de dega.
- Enterprise default OFF: Company workspace me model by default off hai — admin switch karna padega, aur kai companies ke IT policy me external AI models ka approval process alag hota hai.
iZedTools ke bhi free tools — AI kaam ke liye
Kimi K3.1 Pro jaise models ke saath daily kaam karne ke liye iZedTools pe support-free tools bhi banaye hai:- AI Token Counter — text ke tokens, words aur API cost estimator. Kimi API me budget manage karne ke liye most necessary.
- Word Counter — prompt clarity aur document length.
- Character Counter — meta description, tweet limit aur SMS limits.
- Text Diff Checker — model ke 2 drafts ek click me compare karo, kaunsi line change hui.
- JSON Formatter — AI ka JSON output ganda aaye to isse paste karke clean + valid banjata hai. Developers ka daily-usage.
FAQ — Kimi K3.1 Pro ke common sawal-jawab
Q1. Kimi K3.1 Pro kab launch hua?
23 October 2026 (China time me 24 October). Ye Moonshot AI ke K3.1 series ka pehla model hai aur company ke flagship ke tor pe aaya.
Q2. Kimi K3.1 Pro free me use kar sakta hoon?
Haan — kimi.com par free "Adagio" plan me try kar sakte ho, bina kisi billing ke. API ke liye chahiye top-up (approx $1) aur Highspeed variant Pro plan se upar me milti hai.
Q3. Kimi K3.1 Pro ka price kitna hai?
API pricing $2 per 1M input tokens aur $10 per 1M output tokens hai. Cached input $0.20 per 1M hota hai. Ye rate Kimi K3 ki ($3/$15) se 33% sasta hai. Flat pricing poore 1M context me.
Q4. Kimi K3.1 Pro vs ChatGPT — kya better?
Independent Artificial Analysis Index me K3.1 Pro 55.1 ke sath 4th aaya hai — Claude Opus 5.5 (57.6) se sirf 2.5 point pichhe. Coding/agent work me Terminal-Bench 4.0 pe 67% (AA test ke hisab se) sabse highest score raha. Knowledge exams me Claude abhi aage hai. Budget ke hisab se aap Kimi ko seriously le sakte ho.
Q5. Open weights kab aayengi?
4 November 2026 ke andar (promised date). Artificial Analysis ka estimate hai ki ye aate hi Kimi K3.1 Pro sabse high-scoring open-weights model ban jayega — open vs closed ka gap sirf 3 points par aa jayega. Kimi K3 ki weight-promise July me poora hua tha (exactly date par), isliye track-record theek hai.
Q6. Kimi K3.1 Pro Highspeed kya hai?
Highspeed K3.1 Pro ka 2-3x tez version hai. Iska API rate double hai: $4 input aur $20 output per 1M tokens. Real-time apps, chat support aur live workflows me speed chahiye tab ye variant useful hai.
Q7. Image aur video input support karta hai?
Haan — text ke alawa images aur video files bhi input le sakta hai (Moonshot hi API docs me mention hai). Multimodal kaam — media analysis, screenshot samajhna, graphics — model ki range me hai.
Q8. Kimi K3.1 Pro hallucination karta hai?
Koi bhi model karta hai. Moonshot ke internal test me K3 se 65% drop aayi (23.4% se 8.3%). Independent AA-Omniscience test pe rate 53% se 44% pe chala gaya. Isliye fact-check zaroori hai, par K3 ke muqable abhi bhi kaafi achha hai isliye baaki kaam ka jawab ho jayega.
Q9. Kya ye India me available hai?
Haan — kimi.com aur app India me accessible hai. API bhi globally available. Agar API rate-limit issue aaye to API docs aur status page dekho.
Q10. 1M tokens context ka matlab kya hai?
Context window ye measure hai ke model ek saath kitna text "yaad" rakh sakta hai. 1M tokens = approx 7,00,000-8,00,000 English words, ya lagbhag 100+ page ki book. Bade document, poore code repo aur multi-file project ek prompt me ho sakta hai.
Q11. Kimi K3.1 Pro ka API id kya hai?
kimi-k3.1-pro (main) aur kimi-k3.1-pro-highspeed (tez version). platform.kimi.ai ke docs me details milegi.
Q12. Coding me best kaun hai — Kimi ya Claude?
Dono top level par hai. AA ke independent test me Kimi K3.1 Pro (67%) Claude Sonnet 5.5 (64%) se aage tha Terminal-Bench 4.0 par; Moonshot ki apni test me Claude Opus 5.5 (66.4%) se 0.7 points pichhe (65.7%). Simple conclusion: quality approx level-par hai, aur Kimi budget angle se aaj best value deal hai.
Final verdict — Kimi K3.1 Pro lena chahiye ya nahi?
Poora guide cover karne ke baad seedha jawab de deta hoon. Kimi K3.1 Pro abhi market ka best value model hai — quality Claude Opus 5.5 ke bilkul paas (55.1 vs 57.6), par token rate 3-5x sasta. Coding ya agentic kaam aapka main focus hai to ye model aaj hi try karne layak hai. Knowledge-exams ya 3D work hai to Claude/GPT-6 aage hai, unhi par raho. 4 November 2026 ko full open weights bhi aa jayenge — tab ye model open-source community ke liye number 1 ban jayega. Kimi K3 ke weights date par aaye the, isliye isi promise par bharosa kiya ja sakta hai. Mera suggestion 3 line me:- Aaj try karo: kimi.com kholo, free me K3.1 Pro test karo — apne real prompts ke saath.
- Kharcha ginn lo: API use karne se pehle apne tokens ka estimate banao — sasta model bhi zyada tokens kharch karta hai.
- 4 Nov ka wait karo: self-hosting soch rahe ho to us din decide karna best rahega.
