MiMo-V2.6-Flash
- Input / 1M tokens
- $0.14
- Output / 1M tokens
- $0.28
- Providers
- 8
- Uptime, 24 h
- 100%
API models · Side by side
MiMo-V2.6-Flash vs DeepSeek V4.1 Flash
MiMo-V2.6-Flash and DeepSeek V4.1 Flash offer two ways to build a low-cost AI feature. MiMo accepts more media formats through the compared endpoint, including audio and video. For text and images, the more useful question is how the complete request is billed and whether its answer does the job.
DeepSeek V4.1 Flash
- Input / 1M tokens
- $0.30
- Output / 1M tokens
- $1.20
- Providers
- 28
- Uptime, 24 h
- 100%
The short answer
Choose the input formats first. MiMo is the relevant option here for direct audio or video input; both can be considered for text and images. For a text application, compare the input and output bill together and include the cost of unsuccessful attempts.
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:
MiMo-V2.6-Flash$12.60per month
DeepSeek V4.1 Flash$36.00per 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 | MiMo-V2.6-Flash | DeepSeek V4.1 Flash |
|---|---|---|
| Input formats | audio, image, text, video | image, text |
| Output formats | text | text |
| Context / position limit | 1,050,000 | 1,048,576 |
| Endpoint output limit (tokens) | 131,072 | 943,718 |
| Developer features | frequency penalty, Reasoning output, logit bias, logprobs, Output limit, min p, parallel tool calls, presence penalty, Reasoning controls, repetition penalty, Response format, Seed, Stop sequences, Structured output, Temperature, Tool selection, Tool calling, top k, top logprobs, Sampling controls | frequency penalty, Reasoning output, logit bias, logprobs, Output limit, min p, parallel tool calls, presence penalty, Reasoning controls, Thinking effort, repetition penalty, Response format, Seed, Stop sequences, Structured output, Temperature, Tool selection, Tool calling, top a, top k, top logprobs, Sampling controls |
| Cached input / 1M tokens | $0.002800 | $0.006000 |
| USD input / 1M tokens | $0.14 | $0.30 |
| USD output / 1M tokens | $0.28 | $1.20 |
| Available API providers | 8 | 28 |
| Best provider uptime, last 24 h | 100% | 100% |
| Price range across providers (per 1M tokens) | Input $0.100–$0.18, output $0.22–$0.36 across 8 providers | Input $0.040–$3.00, output $0.42–$12.00 across 28 providers |
| Model identifier | xiaomi/mimo-v2.6-flash | deepseek/deepseek-v4.1-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 | MiMo-V2.6-Flash | DeepSeek V4.1 Flash |
|---|---|---|
| 1,000 short requestsPer request: 2,000 input + 500 output tokens | $0.42 | $1.20 |
| 100 document summariesPer request: 20,000 input + 1,000 output tokens | $0.31 | $0.72 |
| 10 long-document requestsPer request: 300,000 input + 2,000 output tokens | $0.43 | $0.92 |
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
MiMo covers more input formats in this pair
MiMo’s endpoint accepts audio, images, text and video. DeepSeek’s accepts images and text. For a meeting assistant or a tool that reviews screen recordings, this is a concrete distinction. Sending DeepSeek a transcript is an alternative workflow, with transcription costs and information loss to consider. Neither text-output endpoint should be mistaken for a service that generates finished video or speech.
A cheap prompt is only half of the bill
Separate the text you send from the text you receive. A search query that returns one label has a very different cost shape from a request for a lengthy code explanation. Use the short-request and document examples below, then enter the counts from your own application. Rates can differ between hosting endpoints, so keep the endpoint consistent when comparing a quoted bill with the calculator.
Large context helps only when the material belongs there
Both offer roughly a million tokens of context. That can make room for a long conversation or a collection of documents, but it does not make every document relevant. For a support assistant, retrieve the sections that answer the customer’s question. This can reduce token use and gives a reviewer a smaller set of evidence to check.
A coding assistant needs a completion criterion
For a practical comparison, give both models the same small change and define what counts as done: the expected behaviour, files that may change and checks that must pass. Track whether another prompt is needed and whether the explanation matches the actual edit. A low per-token rate is useful, but repeated incomplete changes can consume the saving.
Audio and video need their own estimate
The calculator compares token spending; it does not price a recording from its duration. Media tokenization, frame selection and any separate audio processing depend on the service. For an upload feature, estimate one representative recording through that service’s billing rules before multiplying by your expected volume. Keep storage and transcription charges separate so the monthly total remains understandable.
Common questions
Can either model generate images?
The compared endpoints return text. Image input means the model can receive an image; it does not mean the endpoint creates images.
Which is better for everyday chat?
There is no measured overall winner on this page. Compare the response style, factual mistakes and follow-up corrections on the conversations your users are likely to have, then weigh those results against the bill.