Kimi K3
- Input / 1M tokens
- $0.19
- Output / 1M tokens
- $16.00
- Providers
- 18
- Uptime, 24 h
- 100%
API models · Side by side
Kimi K3 vs Claude Sonnet 5.5
Kimi K3 adds an open-weight option to a comparison with Claude Sonnet 5.5. Both offer large-context hosted APIs, while Kimi also supports video input. The choice depends on what you send, how much the model writes back and whether you need control over deployment.
Claude Sonnet 5.5
- Input / 1M tokens
- $2.00
- Output / 1M tokens
- $10.00
- Providers
- 5
- Uptime, 24 h
- 100%
The short answer
Consider Kimi when video input or access to model weights is part of the project. Sonnet remains a relevant choice for text, images and file-based workflows. Compare input and output charges separately: a low prompt rate can still produce a substantial bill for long answers.
Estimate your monthly bill
Enter your own usage. We apply each model's current rates, including long-prompt pricing, for a 30-day month.
Try:
Kimi K3$251.52per month
Claude Sonnet 5.5$270.00per month
A token is roughly three-quarters of an English word. Cached input, reasoning tokens, tools and media are not included.
Compare the details
Compare context, supported inputs and usage costs. Highlighted rows show a difference.
| Feature | Kimi K3 | Claude Sonnet 5.5 |
|---|---|---|
| Input formats | image, text, video | file, image, text |
| Output formats | text | text |
| Context / position limit | 1,048,576 | 1,000,000 |
| Endpoint output limit (tokens) | 943,718 | 128,000 |
| Developer features | frequency penalty, Reasoning output, logit bias, logprobs, Output limit, min p, presence penalty, Reasoning controls, Thinking effort, repetition penalty, Response format, Seed, Stop sequences, Structured output, Temperature, Tool selection, Tool calling, top k, top logprobs, Sampling controls | Reasoning output, Completion limit, Output limit, Reasoning controls, Thinking effort, Response format, Stop sequences, Structured output, Tool selection, Tool calling, Response length |
| Cached input / 1M tokens | $0.18 | $0.10 |
| Cache write / 1M tokens | Unknown | $2.50 |
| USD input / 1M tokens | $0.19 | $2.00 |
| USD output / 1M tokens | $16.00 | $10.00 |
| Available API providers | 18 | 5 |
| Best provider uptime, last 24 h | 100% | 100% |
| Price range across providers (per 1M tokens) | Input $0.18–$4.50, output $9.50–$22.50 across 18 providers | Set by Anthropic |
| Model identifier | moonshotai/kimi-k3 | anthropic/claude-sonnet-5.5 |
API pricing
Prices are in USD per million tokens. Input is what you send; output is what the model generates. Chat subscriptions and API usage are billed separately.
Tools, images and additional reasoning can add charges. The examples below cover input and output tokens only.
Compare local modelsWhat those prices mean in practice
Each example uses the same token budget for both models. These are calculations, not measured workloads. Cache discounts, media, tools and extra reasoning are excluded.
| Workload | Kimi K3 | Claude Sonnet 5.5 |
|---|---|---|
| 1,000 short requestsPer request: 2,000 input + 500 output tokens | $8.38 | $9.00 |
| 100 document summariesPer request: 20,000 input + 1,000 output tokens | $1.98 | $5.00 |
| 10 long-document requestsPer request: 300,000 input + 2,000 output tokens | $0.90 | $6.20 |
Estimate = requests × (input tokens × input price + output tokens × output price) ÷ 1,000,000. Long-prompt rates are applied where the pricing data provides them. Real requests can use different amounts of output.
Beyond the price tag
Video changes the job you can send
Kimi accepts text, images and video through the compared endpoint. Sonnet accepts text, images and files. For a recorded demonstration, Kimi can be considered for the visual sequence itself. A transcript sent to Sonnet is a different input: it may leave out an on-screen error or a step performed without narration. Choose the evidence your task needs before comparing the answers.
Open weights bring a deployment choice
Moonshot AI releases Kimi K3 weights under its own license. That creates an option beyond renting an API, but it does not turn Kimi into a small laptop model. Its model card describes 2.8 trillion total parameters and 104 billion activated parameters. Activated parameters describe the computation used for a token; they are not the amount of model memory a server must hold.
A hosted request is the simpler starting point
The API comparison below does not require you to download or serve either model. Self-hosting Kimi is a separate infrastructure decision involving compatible software, memory, storage and operational work. It makes sense to price that deployment only after you know the workload. An API token bill and a GPU rental price measure different things, so they cannot be compared directly without a usage assumption.
Long input and long output favour different budgets
For summarisation, a large document may produce only a short answer. For code generation, a modest instruction can produce a long patch. Enter both amounts in the calculator instead of choosing from the input price alone. The cost examples keep token quantities equal; actual replies and reasoning can differ. Your most common request shape is more useful than a single headline price.
Moving a coding workflow needs more than a model name
If your editor already uses Sonnet, try Kimi with the same repository instructions and tool permissions before changing the default. Compare whether it follows project conventions, returns usable tool arguments and completes the requested change. Kimi’s different deployment options may be valuable, but they do not by themselves establish that an existing integration will behave the same way.
Common questions
Can I use Kimi without running my own GPUs?
Yes. This page compares hosted API access. Running the released weights yourself is an alternative, not a requirement.
Does a million-token context mean an equally long answer?
No. Context covers the material a request can hold; the service can impose a separate output limit. Neither number guarantees that a model will use every part of a long document accurately.