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 Gemini 3.8 Flash
Kimi K3 costs less to read a large prompt; Gemini 3.8 Flash costs less to write the answer. The better value depends on what you send and how much the model writes back. Both can work with images and video, while Gemini also accepts audio. Kimi’s released weights offer a separate path for teams that need to operate their own deployment.
Gemini 3.8 Flash
- Input / 1M tokens
- $0.83
- Output / 1M tokens
- $4.13
- Providers
- 2
- Uptime, 24 h
- 100%
The short answer
Choose Gemini for a feature that needs audio input or generates substantial text. Consider Kimi for long reference material followed by short answers: its lower uncached input rate can outweigh its higher output rate. For a self-hosted deployment, Kimi is the option in this pair, but the API comparison does not establish which hardware you need.
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
Gemini 3.8 Flash$111.38per 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 | Gemini 3.8 Flash |
|---|---|---|
| Input formats | image, text, video | audio, file, image, text, video |
| Output formats | text | text |
| Context / position limit | 1,048,576 | 1,048,576 |
| Endpoint output limit (tokens) | 943,718 | 65,536 |
| 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, Output limit, Reasoning controls, Thinking effort, Response format, Seed, Stop sequences, Structured output, Temperature, Tool selection, Tool calling, Sampling controls |
| Cached input / 1M tokens | $0.18 | $0.08 |
| Cache write / 1M tokens | Unknown | $0.04 |
| USD input / 1M tokens | $0.19 | $0.83 |
| USD output / 1M tokens | $16.00 | $4.13 |
| Available API providers | 18 | 2 |
| 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 Google |
| Model identifier | moonshotai/kimi-k3 | google/gemini-3.8-flash |
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 | Gemini 3.8 Flash |
|---|---|---|
| 1,000 short requestsPer request: 2,000 input + 500 output tokens | $8.38 | $3.71 |
| 100 document summariesPer request: 20,000 input + 1,000 output tokens | $1.98 | $2.06 |
| 10 long-document requestsPer request: 300,000 input + 2,000 output tokens | $0.90 | $2.56 |
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
A short chat and a document summary favour different models
Using the rates in the calculator, 1,000 requests with 2,000 input tokens and a 500-token reply cost about $10.30 on Kimi and $3.71 on Gemini. Change the job to 100 document summaries, each with 20,000 input tokens and a 1,000-token reply, and the gap almost closes: about $2.30 on Kimi against $2.06. It narrows because most of the spending moves from generated text to the material being read. These are equal-token examples, excluding caching, tools, media and extra reasoning.
Work out how much of the request becomes an answer
At these uncached rates, Kimi becomes cheaper when input exceeds roughly 24 times the billable output. A 50,000-token brief with a 1,000-token answer fits that pattern; a 2,000-token instruction followed by a 2,000-token report does not. For the latter, 1,000 requests cost about $40.30 on Kimi and $9.90 on Gemini. Use the ratio as a budgeting shortcut, then replace the assumptions with your application’s usage. The same prompt need not produce the same token count on both models.
Gemini can work with the audio in a recording
Both models accept images and video, but only Gemini accepts audio through the compared endpoint. That matters for an interview, support call or product demonstration where spoken details carry the answer. Sending a silent clip to Kimi would not preserve those details; you would need a transcript or a separate audio-processing step. For visual-only work, such as reading an error on screen, both remain candidates. Gemini 3.8 Flash returns text here, so audio input support should not be mistaken for a voice-response service.
Repeated reference material changes the price again
Kimi has the lower uncached input price, but Gemini has the lower cache-read rate: $0.0825 per million tokens against $0.142. A support assistant that repeatedly sends the same manual should therefore budget cache hits separately from new input. Do not apply the cache rate to every request or count it as a discount on generated output. Cache creation, storage, eligibility and expiry depend on the service. A useful cost estimate separates the reusable manual from each customer’s new question and the answer.
Long context does not remove the need to choose material
Both compared endpoints allow 1,048,576 tokens of context, and neither rate record adds a prompt-length surcharge. That is useful for large reference packs, but it does not make sending every file the cheapest approach. A 300,000-token input with a 2,000-token reply costs about $0.086 on Kimi and $0.256 on Gemini before other charges. Retrieve the relevant sections when you can. It reduces repeated input and makes it easier to trace an answer back to the material that supports it.
Use output limits as boundaries, not writing targets
Gemini’s documented output limit is 65,536 tokens. The Kimi endpoint has a much larger generation ceiling, shown in the table, but that is a service limit rather than a promise of a complete or useful answer at that length. For reports or generated code, ask for reviewable sections and leave room for instructions, source material and reasoning. A generous output allowance can also permit a larger bill. Set the limit around the deliverable you need instead of the maximum the endpoint accepts.
Kimi gives you an operational choice Gemini does not
Kimi K3 has released weights and deployment guidance for serving engines including vLLM and SGLang. Gemini is accessed as a hosted service. Running Kimi yourself means taking responsibility for GPU capacity, compatible serving software, updates and access controls. Its 104-billion active parameter count is not the size of the complete model: the full mixture-of-experts model has 2.8 trillion parameters. Plan from the actual checkpoint and runtime rather than treating the active count as a memory estimate. Renting API access avoids that infrastructure work.
Keep the API route and reasoning settings explicit
The calculator uses the selected hosting rates, which can differ from buying directly. Google’s standard direct API charges $0.75 input and $3.75 output per million tokens through December 2026, with higher rates scheduled for January 2027. That is distinct from the $0.825 and $4.125 route used here. Thinking controls also differ: Gemini supports low, medium and high; Kimi’s own API supports low, high and max and requires preserved reasoning history for ongoing tool conversations. An integration needs to handle those differences before switching models.
Common questions
Which should I choose for a document assistant?
Consider how much it reads, how much it writes and whether reference material is cached. Kimi can cost less for large uncached inputs and short replies. Gemini’s lower output and cache-read rates can favour longer answers or repeated reference packs. Check that the finished answer identifies the right passages and preserves important qualifications.
Can either model generate images or speak its answers?
The endpoints compared here return text. Accepting an image, video or audio input does not mean generating that format. Use a suitable image-generation or voice model when the response itself must be visual or spoken.
Is Gemini Flash faster than Kimi K3?
This comparison does not include measured latency or token-generation speed. Reply length, reasoning settings, tools and the hosting service affect waiting time. Measure a representative request with the service and configuration you intend to use before setting response-time expectations.