Kimi K3.1 Pro vs Mixtral 8x22B Instruct
Pick up to 3 models · pricing, context and capabilities · simulate your product cost
Data updated on November 14, 2026· 230 models
Kimi K3.1 Pro is Moonshot AI's flagship open-weight multimodal reasoning model and a post-trained successor to Kimi K3, with a 1M-token context window.
- Reasoning estimated
- ▲
- Speed estimated
- Input
- Output
- Context
- ▲1 M tokens
- Max output
- ▲944 K
- Input, $ per 1M tokens
- $2.00
- Output, $ per 1M tokens
- $10.00
- Cache read, $ per 1M
- $0.200
- Batch (input / output)
- $0.700 / $6.00
- Tool calling
- Yes
- Released
- ▲10/23/2026
- Knowledge cutoff
- —
- API id
- moonshotai/kimi-k3.1-pro
Mistral's official instruct fine-tuned version of Mixtral 8x22B.
- Reasoning estimated
- Speed estimated
- ▲
- Input
- Output
- Context
- 66 K tokens
- Max output
- 52 K
- Input, $ per 1M tokens
- $2.00
- Output, $ per 1M tokens
- ▲$6.00
- Cache read, $ per 1M
- $0.200
- Batch (input / output)
- —
- Tool calling
- Yes
- Released
- 04/17/2024
- Knowledge cutoff
- —
- API id
- mistralai/mixtral-8x22b-instruct
Which is the better API choice: Kimi K3.1 Pro vs Mixtral 8x22B Instruct?
- Mixtral 8x22B Instruct: cheapest output
- Kimi K3.1 Pro: largest context (1 M)
- Kimi K3.1 Pro: the newest (10/23/2026)
- Kimi K3.1 Pro: most reasoning (estimated)
- Mixtral 8x22B Instruct: the fastest (estimated)
Pricing is split: Kimi K3.1 Pro wins on input and Mixtral 8x22B Instruct wins on output. Kimi K3.1 Pro accepts more context (1 M). On capabilities, Kimi K3.1 Pro accepts images; Kimi K3.1 Pro has a reasoning mode. Kimi K3.1 Pro offers a batch tier at half price for jobs that do not need an immediate answer.
What would your product cost?
Estimate the monthly cost with your app's real volume. List prices, no cache or batch discounts.
With Mixtral 8x22B Instruct you save $16.00 a month versus Kimi K3.1 Pro (23% less).
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Paste your prompt: tokens and cost per model
Rough estimate (about 4 characters per token). Nothing is sent to any server.
Cost of sending it 1,000 times (input only)
- 1Claude Haiku 5.5 Anthropic$0.008
- 2Kimi K3.1 Pro Moonshot$0.152
- 3Mixtral 8x22B Instruct Mistral$0.152
- 4Claude Sonnet 5.5 Anthropic$0.152
- 5Claude Opus 5.5 Anthropic$0.304
- 6Claude Opus 5 Anthropic$0.380
- 7Claude Fable 5.2 Anthropic$0.608
- 8Claude Fable 5.1 Anthropic$0.760
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See allFrequently asked questions
Which is the better API choice: Kimi K3.1 Pro vs Mixtral 8x22B Instruct?
Pricing is split: Kimi K3.1 Pro wins on input and Mixtral 8x22B Instruct wins on output. Kimi K3.1 Pro accepts more context (1 M). On capabilities, Kimi K3.1 Pro accepts images; Kimi K3.1 Pro has a reasoning mode. Kimi K3.1 Pro offers a batch tier at half price for jobs that do not need an immediate answer.
Which is cheaper: Kimi K3.1 Pro and Mixtral 8x22B Instruct?
Kimi K3.1 Pro is cheaper: $2.00 per million input tokens and $10.00 per million output tokens, versus $2.00 / $6.00 for Mixtral 8x22B Instruct.
Which has more context: Kimi K3.1 Pro and Mixtral 8x22B Instruct?
Kimi K3.1 Pro accepts 1 M tokens of context; Mixtral 8x22B Instruct accepts 66 K.
Which is newer: Kimi K3.1 Pro and Mixtral 8x22B Instruct?
Kimi K3.1 Pro was released on 10/23/2026; Mixtral 8x22B Instruct on 04/17/2024.
What would 10,000 requests a month cost with Kimi K3.1 Pro and Mixtral 8x22B Instruct?
With 1,500 input and 400 output tokens per request: Kimi K3.1 Pro: $70.00; Mixtral 8x22B Instruct: $54.00 a month, at list price.
Where do the prices come from and how often are they updated?
From the public catalogs of OpenRouter, models.dev and LiteLLM, which publish each provider's list prices. They sync once a day and the last update date is shown above the comparison.
What does price per million tokens mean?
It is what the provider charges per million tokens you send (input) or the model generates (output). A token is roughly 4 characters. Output almost always costs more than input.
Sources: OpenRouter, models.dev and LiteLLM. Prices in USD per million tokens, list price, no volume discounts. Prompt tokens are an estimate; each provider's tokenizer may differ.