Is Kimi better than Claude?
Neither wins outright, because Kimi and Claude are not sold the same way. Kimi K3 is an open-weight model from Moonshot AI, 2.8 trillion parameters with a 1,048,576-token context, that you can download and host yourself. Claude is Anthropic's closed family with a published rate card, and Claude Sonnet 5 wrote the cleanest code we have measured (Aug 3, 2026). Pick Kimi when the weights must live on your own hardware or in your own region. Pick Claude for customer-facing writing, a supported gateway, and Claude Code. In Taskade, Claude runs on Pro and up with your own Anthropic API key.
What TSK-1 Found
TSK-1 hasn't run these two head-to-head in the same test. We tested Kimi on Jul 31, 2026; Claude's deepest tests were Jul 30, Aug 1 and Aug 3, 2026. What those tests do show: Kimi K3's steps failed least often of the models tested that day (Jul 31, 2026 - 7.2%, fewest steps, most accurate account of its own work). Claude Sonnet 5 writes the cleanest code we have measured (Aug 3, 2026), and the family has produced the best-looking build we have measured (Jul 30, 2026), the most complete app of its test (Aug 1, 2026), and that cleanest code. Taskade's Auto routing picks a model per task from its non-Claude catalog. Claude runs in Taskade on your own Anthropic key (an agent chat on your key uses no Taskade credits, and an automation step uses one fixed step fee).
- Kimi: Jul 31, 2026 — fewest failed steps of the models tested (7.2%), fewest steps overall, most accurate account of its own work.
- Claude: Aug 3, 2026 — the cleanest code we have measured; plus the best-looking build we have measured (Jul 30, 2026) and the most complete app of its test (Aug 1, 2026).
See the full evidence at /tsk/kimi, /tsk/claude, and the TSK-1 hub.
How Fast Is Kimi K3?
Kimi K3's output speed depends more on who serves it than on the model. Artificial Analysis measured 17 API endpoints for K3 (medians on a 10,000-token input, checked 2026-10-20): output ran from about 32 tokens per second on the slowest host to about 422 on the fastest, LithosAI's Ultra Chat tier. Moonshot's own API measured about 42, so the same weights ran roughly 10 times faster on the fastest host. Speed figures move week to week, so re-check them on the Artificial Analysis provider page before you pick a host.
| Speed signal | Kimi K3 |
|---|---|
| Fastest measured host (2026-10-20) | About 422 output tokens/s |
| Moonshot first-party API (2026-10-20) | About 42 output tokens/s |
| TSK-1 build time | Finishes later than every other model tested |
| TSK-1 reliability | Only 7.2% of its build actions failed in one round, the most reliable of that test |
TSK-1's verdict on Kimi K3: it builds more than almost any model and finishes later than all of them. Reasoning is always on for K3, which adds tokens before the answer. Claude's speed also varies by model tier and host, and this page does not rank the two on it. To run K3 in Taskade without a Max plan, see AI builders that take your own API key.
Kimi Code vs Claude Code
On the coding side, Moonshot's dedicated model is Kimi K2.7 Code, a 1-trillion-parameter multimodal model built for coding, and Claude Code is Anthropic's terminal agent. Reach for Kimi K3 or Kimi K2.7 Code when an agentic coding loop, reading files, editing code, running tests and iterating, has to run on your own infrastructure. Reach for Claude Code for conversational pair programming, code review reasoning and terminal-agent work behind a supported gateway, with Opus 5.5 at $4 in and $20 out per 1M tokens. The durable pattern is routing, not replacement: an open-weight model inside the loop, a governed closed model on the output that reaches a customer.
The Headline
This is a distribution comparison before it is a capability comparison. Kimi K3 is a 2.8-trillion-parameter mixture-of-experts model with 104 billion parameters used per token, a 1,048,576-token context, and weights you can download. Claude is a closed family with a published rate card, a 1M-token context that carries no long-context surcharge, and Anthropic's safety training on top.
That difference decides more of the design than any benchmark does. If the weights have to live in your region, on your hardware, or inside an air-gapped network, only one of these two is even a candidate. If you need a supported gateway with contractual terms and a per-token price you can forecast, the other is the safer buy.
TL;DR: Kimi K3 is open-weight at 2.8T total, 104B used per token, with a 1,048,576-token context, under Moonshot's bespoke Kimi K3 License. Claude is closed, with published rates from $1/$5 per 1M on Haiku 4.5 to $10/$50 on Fable 5.1, and no surcharge on its 1M context. Taskade Genesis runs frontier models from top AI labs. Kimi K3 is on Taskade credits on Max and Enterprise (or on a Together AI key from Business up), and Claude runs on your own Anthropic key, Pro and up.
The Routing Matrix
The useful question is never "which model is best". It is "which model for this step, and what does that choice cost".
| Task | Reach for | Why | Cost consequence |
|---|---|---|---|
| Long-context reasoning over a whole repo or corpus | Either | Both reach ~1M tokens | Claude bills the full window at standard rates, but its 4.7+ tokenizer emits ~30% more tokens for the same text — budget per document, not per token |
| Agentic coding, multi-step tool use | Kimi K3 or Kimi K2.7 Code | Open weights let the loop run on your own infrastructure; K2.7 Code is purpose-built for coding | Self-hosting turns per-token spend into fixed GPU spend — good above sustained volume, bad below it |
| Conversational pair programming, terminal agent work | Claude | Claude Code plus Opus 5.5 | $4 in / $20 out per 1M on Opus 5.5 |
| Cheap high-volume classification and extraction | Claude Haiku 4.5, or a self-hosted open-weight model | Haiku is the cheapest published first-party Claude rung | $1 in / $5 out per 1M, halved again with the Batch API |
| Customer-facing writing and nuanced instruction following | Claude | Anthropic's strongest published differentiator, plus Constitutional AI on the output | Sonnet 5.5 at $2/$10 per 1M |
| Vision-to-text work | Either | K3 is image-text-to-text; Claude takes vision + text | Image tokens bill as input tokens on both sides |
| On-premise, air-gapped, or region-locked deployment | Kimi only | Claude does not ship weights | Infrastructure cost replaces API cost entirely |
| Fine-tune on proprietary data and keep the result | Kimi only | Weights are downloadable | Read the Kimi K3 License before redistributing the result |
| Repeated prompts over a stable prefix | Claude | Cache hits bill at 0.1× base input | A 90% saving on the cached portion |
| Bulk asynchronous jobs | Claude | Batch API is 50% off in both directions | Halves the rate card on anything that can wait |
Read the matrix as two columns of one system. Most real setups end up using an open-weight model where volume and control dominate, and a governed closed model where the output reaches a person.
What Claude Actually Costs
Anthropic publishes both sides of its pricing, which makes it the easy half of this comparison to plan against.
| Claude plan | Month-to-month | Billed annually |
|---|---|---|
| Free | $0 | — |
| Pro | $20/mo | $17/mo, billed $200 up front |
| Max | From $100/mo — 5x or 20x more usage than Pro | Not disclosed |
| Team | $25/seat/mo, premium seat $125/seat/mo | $20/seat/mo, premium seat $100/seat/mo |
| Enterprise | Seat price plus usage at API rates | — |
API rates, per 1 million tokens:
| Model | Input | Output |
|---|---|---|
| Claude Haiku 4.5 | $1 | $5 |
| Claude Sonnet 5.5 (Sonnet 5 kept the same price) | $2 | $10 |
| Claude Opus 5.5 | $4 | $20 |
| Claude Fable 5.1 | $10 | $50 |
Three modifiers matter more than the headline numbers. The Batch API is 50% off in both directions. A cache hit bills at 0.1× base input. And web search on the API is $10 per 1,000 searches, which is a line item agentic workloads discover late.
The tokenizer footnote nobody prices in
Claude 4.7 and later use a newer tokenizer that produces roughly 30% more tokens for the same text. A rate card comparison between two labs is therefore not a cost comparison unless both sides are measured on the same corpus. Run your own text through both tokenizers before you conclude that a lower per-token price is a lower bill.
The good news in the other direction: Anthropic bills the full 1M-token context at standard rates. A 900,000-token request costs the same per token as a 9,000-token one. There is no long-context tier, which is unusual and genuinely worth something on document-heavy work.
And Kimi's price?
Moonshot AI publishes Kimi's API rates on its developer platform, and the honest answer is: read them at the source, at platform.kimi.ai, because a number copied into a comparison page starts aging the day it is published. Note that the older platform.moonshot.ai address now redirects to platform.kimi.ai — update any integration still pointing at the old host.
What is structurally true regardless of the current figure is that an open-weight model has a self-hosting floor a closed model cannot have. Above sustained volume that floor, not the rate card, is the number that decides the architecture.
License & Distribution: The Real Divide
This is the row most comparisons get wrong, so it is worth stating precisely.
| Dimension | Kimi K3 | Claude |
|---|---|---|
| License | Kimi K3 License — a bespoke Moonshot license covering the code repository and the weights | Commercial terms of service |
| Is it MIT or Apache 2.0? | No. Modified MIT applied to the earlier K2 line and does not carry forward | n/a |
| Weights downloadable | ✅ Yes, Hugging Face | ✗ No |
| Self-host | ✅ Yes, license permitting | ✗ Anthropic gateway only |
| Fine-tune on your data | ✅ Yes | ✗ No |
| Redistribute a fine-tune | ⚠️ Read the license first | ✗ No |
| Audit the weights | ✅ Yes | ✗ No |
| Data residency / air-gap | ✅ Achievable | ✗ Not achievable |
Say it plainly: Kimi K3's weights are downloadable and self-hostable, under a vendor-specific license your legal team should read before you redistribute or resell. That is a real and meaningful difference from a closed model. It is not the same freedom as a standard permissive license — DeepSeek ships MIT across both code and weights, and Qwen's open weights are Apache 2.0. On the open-weight spectrum, Kimi sits between those and Claude, not alongside them.
Anyone who tells you K3 is "MIT licensed" is repeating a K2-era fact.
Architecture: One Published, One Undisclosed
Kimi K3's structure is public, and it is unusual enough to be worth understanding before you design around it.
- Sparse by design. 104B of 2.8T parameters are used per token, so running cost tracks the number used, not the headline count. The headline number is a capacity claim, not a compute claim.
- A hybrid attention design. 69 of 93 layers are Kimi Delta Attention, a linear-cost variant, with 24 gated multi-head latent attention layers interleaved. The intent is holding quality across a million-token window without paying full attention cost at every layer.
- Vendor claims are vendor claims. Moonshot makes stronger comparative statements about how this design performs against full attention. Benchmark on your own workload before you build a roadmap on them.
Claude's architecture is undisclosed. What Anthropic publishes instead is the safety framework, Constitutional AI, the tier ladder from Haiku through Sonnet, Opus and Fable, and a rate card. For a lot of buyers that trade is the point: less to inspect, more to hold someone accountable for.
Self-Host vs Managed Gateway
Kimi K3 can be self-hosted. Claude cannot. But "can" and "should" are different questions, and a 2.8-trillion-parameter model is not a weekend deployment.
| Kimi K3 self-hosted | Claude | Taskade Genesis | |
|---|---|---|---|
| Possible at all | ✅ Yes, license permitting | ✗ No | ✅ Managed routing across providers |
| Hardware | Serious multi-GPU, high-VRAM infrastructure | None | None |
| Cost shape | Fixed infrastructure + ops headcount | Per token, published | Included in the subscription |
| Data residency | ✅ Fully under your control | Anthropic's regions | Managed |
| Who owns uptime | You | Anthropic | Taskade |
| Breaks even when | Volume is high and sustained | Always available | Immediately, at low and mid volume |
At low and moderate volume a managed gateway wins on every measure except control. Self-hosting K3 makes sense when volume is genuinely high and sustained, or when data residency makes the question moot — and in the second case it is not a cost decision at all.
Choose Claude If…
A fair comparison names the cases where the other side is simply the better buy. There are several here.
- Your output is customer-facing prose. Long-form writing quality and nuanced instruction following are Anthropic's most consistently cited strengths, and there is no self-hosting workaround for "the draft reads better".
- You need someone contractually accountable. A published rate card, enterprise agreements, and a named safety framework are things an open-weight download cannot give you. For regulated buyers that is often the whole decision.
- You want a finished product, not a model. Projects, Artifacts and Claude Code are polished surfaces your team can use today without any infrastructure work at all.
- Your volume is modest. Below meaningful sustained throughput, the API is cheaper and vastly simpler than standing up multi-GPU inference — and Haiku 4.5 at $1/$5 per 1M is inexpensive by any measure.
Choose Kimi If…
- The weights have to be yours. Data residency, air-gapped environments, or an audit requirement that a vendor description cannot satisfy.
- You are fine-tuning on proprietary data and need to keep the resulting model.
- Your volume is high and sustained enough that a fixed infrastructure bill beats a per-token one.
- You want optionality. A downloadable model cannot be deprecated out from under you the way a hosted endpoint can — Moonshot set its own V1 series to sunset on 31 August 2026, which is exactly the risk open weights insure against.
The Taskade Angle: Open-Weight Models Plus Your Own Claude Key
Most comparison pages end with "pick one". The operating reality of 2026 is that serious teams run several models and route between them.
Taskade Genesis runs frontier models from top AI labs, with Auto as the default and the AI allowance included in the subscription. Kimi K3 runs on Taskade credits on Max and Enterprise. On Business and up, a Together AI key lets custom agents and Ask Agent pick Kimi K3 instead (see which models each key serves). Claude does not run on Taskade credits. It runs only on your own Anthropic key, on Pro and up: store it as an AI Model Key and your custom agents, Ask Agent and the built-in AI automation steps run on it (see BYOK), or add an Anthropic Claude provider step to an automation, which bills your Anthropic account plus one fixed step fee in credits (see BYOK AI integration). Taskade Genesis app building and Taskade EVE stay on Taskade credits either way. Paid plans start at Pro $10/mo billed annually, with Business $25, Max $100 and Enterprise $250 per month billed annually.
Four patterns that hold up, whichever models you connect:
- Open-weight in the loop, governed model on the output. The agent's tool-using steps run on a cost-efficient model. The paragraph a customer actually reads runs on the model you are willing to be accountable for.
- Cheap tier for triage, premium tier for the hard 10%. Bulk classification and extraction go to the cheapest capable rung. Escalations route up.
- Long-context ingest, short-context reasoning. One step reads the whole corpus. The next step reasons over a distilled summary at a fraction of the tokens.
- Everything behind Workspace DNA. Whichever model runs a step, the result lands in the same project graph, so the next agent inherits the context instead of re-deriving it.
See 10 Best Open-Source AI LLMs in 2026 for how Kimi sits against the rest of the open-weight field.
Frequently Asked Questions
Which is better, Kimi or Claude?
Neither wins outright, because they are not sold the same way. Kimi K3 from Moonshot AI is an open-weight mixture-of-experts model at 2.8 trillion total parameters with 104 billion used per token, a 1,048,576-token context, and downloadable weights you can host yourself. Claude from Anthropic is a closed model family with a published per-token rate card, a 1M-token context billed at standard rates, and Constitutional AI safety training. Pick Kimi when you need the weights on your own hardware or in your own region. Pick Claude when you need a governed, supported gateway and Anthropic's writing and reasoning quality. Taskade Genesis runs frontier models from top AI labs with Auto, and you connect Claude through your own Anthropic key.
Is Kimi K3 open source?
Kimi K3 is open-weight, but it is not MIT and it is not Apache 2.0. Moonshot AI releases it under a bespoke "Kimi K3 License" that covers both the code repository and the model weights. The weights are downloadable from Hugging Face and self-hostable, so audit, fine-tuning and on-premise deployment are all on the table. Read the license text before you redistribute a fine-tune or resell inference, because a vendor-specific license is not interchangeable with a standard open-source one. The earlier K2 line used a Modified MIT license. That does not carry forward to K3. Claude weights are never released.
How big is Kimi K3, and what is its context window?
Kimi K3 is a mixture-of-experts model with 2.8 trillion total parameters and 104 billion used per token. It routes across 896 experts, using 16 routed experts plus 2 shared experts per token. It has 93 layers, split into 69 Kimi Delta Attention layers and 24 gated multi-head latent attention layers, over a 160,000-token vocabulary. The context window is 1,048,576 tokens. It is multimodal, accepting image and text input and returning text.
How much does Claude cost in 2026?
Anthropic publishes both consumer and API pricing at claude.com/pricing. Claude Free is $0. Pro is $20 per month month-to-month, or $17 per month with the annual discount, billed as $200 up front. Max starts from $100 per month and offers a choice of 5x or 20x more usage than Pro. Team is $25 per seat per month month-to-month or $20 per seat billed annually, with a separate premium seat at $125 monthly or $100 annually. Enterprise is a seat price plus usage at API rates. On the API, per 1 million tokens: Haiku 4.5 is $1 in and $5 out, Sonnet 5.5 is $2 in and $10 out (Sonnet 5 kept the same price), Opus 5.5 is $4 in and $20 out, and Fable 5.1 is $10 in and $50 out.
How much does Kimi K3 cost per token?
Moonshot AI publishes Kimi K3 API rates on its developer platform at platform.kimi.ai, and we do not quote a figure here because the published rate card should be read at source rather than copied into a comparison page that ages. Note that the older platform.moonshot.ai address now redirects to platform.kimi.ai, so update any bookmark or integration base URL. What is structurally true regardless of the current number: because the weights are downloadable, Kimi has a self-hosting floor that a closed model does not have, and at high sustained volume that floor is the real cost lever.
Does Claude charge extra for its 1 million token context?
No. Anthropic bills the full 1M-token context at standard rates on current Claude models, so a 900,000-token request is billed at the same per-token rate as a 9,000-token request. There is no long-context surcharge. There is a subtler cost effect to plan for instead: Claude 4.7 and later use a newer tokenizer that produces roughly 30 percent more tokens for the same text. Price per token is not price per page, so budget long-context work on documents processed, not on a raw token count carried over from an older model.
Can Kimi replace Claude for coding?
For agentic coding, where the model drives multi-step tool use across reading files, editing code, running tests and iterating, Kimi is a credible open-weight option and Moonshot also ships Kimi K2.7 Code, a 1-trillion-parameter multimodal model dedicated to coding. For conversational pair programming, code review reasoning and terminal-agent work through Claude Code, Claude remains a strong default with a supported gateway behind it. The durable pattern is not replacement. It is routing: an open-weight model inside the loop, a governed closed model on the output that reaches a customer.
What is Kimi Delta Attention?
Kimi Delta Attention, or KDA, is the linear-cost attention variant Moonshot AI uses for most of Kimi K3's depth. Of the model's 93 layers, 69 are KDA layers and 24 are gated multi-head latent attention layers, a hybrid designed to hold quality across a very long window without paying full quadratic attention cost at every layer. Moonshot's own materials make stronger comparative claims about how the design performs against full attention. Treat those as vendor claims and benchmark on your own workload before designing around them.
Which Kimi models are live besides K3?
Alongside K3, Moonshot AI currently lists Kimi K2.7 Code, a 1-trillion-parameter multimodal model aimed specifically at coding, plus Kimi K2.6 and Kimi K2.5, both at 1 trillion parameters. Separately, Moonshot set a full platform sunset for its older V1 series on 31 August 2026, so any integration still pinned to a V1 endpoint needs migrating.
What can Kimi do that Claude cannot?
Three things follow directly from open weights. You can download and inspect the model rather than trusting a description of it. You can run it inside your own network, region or air-gapped environment, which is often the deciding factor for data residency and regulated workloads. You can fine-tune it on proprietary data and keep the resulting model. Claude offers none of those, by design. In exchange, Claude offers a published rate card, a supported gateway, Constitutional AI safety training and consumer surfaces like Projects, Artifacts and Claude Code that no self-hosted deployment gives you for free.
Should I pick Kimi or Claude for an AI agent?
Match the model to the step, not to the project. Reach for Kimi when the agent runs long chains of actions, when the cost of high-volume steps matters, or when the weights need to live on your infrastructure. Reach for Claude when the step produces something a customer reads, when nuanced instruction following matters, or when you want Anthropic's safety posture on the output. In Taskade you can build a multi-agent team where agents run on different models and share the same Workspace DNA memory. Kimi K3 runs on Taskade credits on Max and Enterprise, and on Business and up a Together AI key lets custom agents pick it. Claude joins only through your own Anthropic key, on Pro and up.
Does Taskade let me use both without two subscriptions?
Partly. Taskade Genesis runs frontier models from top AI labs with Auto as the default, and the AI allowance is included in the subscription. Kimi K3 runs on Taskade credits on Max and Enterprise, and on Business and up a Together AI key lets custom agents and Ask Agent pick it. Claude does not run on Taskade credits. It runs only on your own Anthropic key, on Pro and up: stored as an AI Model Key, it serves custom agents, Ask Agent and the built-in AI automation steps, and an Anthropic Claude provider step in an automation bills your Anthropic account for each model call plus one fixed step fee in Taskade credits. An agent chat on your key uses no Taskade credits. Paid plans start at Pro for $10 per month billed annually, with Business at $25, Max at $100 and Enterprise at $250 per month billed annually.
Final Word: Different Distribution, Different Job
Kimi K3 is a 2.8-trillion-parameter open-weight model with a million-token context and a license that is genuinely permissive but genuinely bespoke. It is the choice when control — of hardware, of region, of the model itself — is the requirement.
Claude is a closed frontier family with a published rate card, no long-context surcharge, a tokenizer quirk worth measuring, and a safety framework Anthropic will stand behind. It is the choice when accountability and finish are the requirement.
Neither is the winner. The winner is the setup that puts each one where it belongs and does not have to renegotiate a contract to change its mind.
▲ Memory feeds Intelligence. ■ Intelligence triggers Execution. ● Execution creates Memory. Two distribution models. One workspace. No single point of vendor failure.
This is the origin of living software. 🌱
Build agents on open-weight models in Taskade Genesis →
Related reading
- Claude Fable 5 & Mythos 5 Explained. Anthropic's newest model tier: what it is for and what it costs.
- Claude Models Explained. The current Claude lineup and prices.
- 10 Best Open-Source AI LLMs in 2026. The full open-weight ranking.
- Kimi vs DeepSeek. Bespoke license versus MIT, head to head.
- Qwen vs DeepSeek. The other open-weight duel.
- Multi-Model AI Access. How Taskade routes across providers.
- Multi-Agent Teams. Specialists with different model picks.
- Taskade MCP Server. Use Claude Desktop or Cursor with your workspace.
- Free Claude Alternative. How Taskade compares to Claude as a workspace.
- TSK-1 Kimi profile — Full benchmark evidence for the Kimi family.
- TSK-1 Claude profile — Full benchmark evidence for the Claude family.
- TSK-1 hub — The complete model benchmark dataset.
