Kimi K3.1 Pro vs Mistral Large
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
This is Mistral AI's flagship model, Mistral Large 2 (version mistral-large-2407).
- Reasoning estimated
- Speed estimated
- Input
- Output
- Context
- 128 K tokens
- Max output
- 102 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
- 02/26/2024
- Knowledge cutoff
- —
- API id
- mistralai/mistral-large
Which is the better API choice: Kimi K3.1 Pro vs Mistral Large?
- Mistral Large: 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)
Pricing is split: Kimi K3.1 Pro wins on input and Mistral Large 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 Mistral Large you save $16.00 a month versus Kimi K3.1 Pro (23% less).
The 6 cheapest for this scenario (chat, last 12 months)
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- 3Qwen3.7 Flash Qwen$0.97
- 4DeepSeek V4.1 Flash DeepSeek$1.57
- 5DeepSeek V4 Flash 0731 DeepSeek$1.85
- 6Ministral 3 3B 2512 Mistral$1.90
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
- 3Mistral Large 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 Mistral Large?
Pricing is split: Kimi K3.1 Pro wins on input and Mistral Large 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 Mistral Large?
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 Mistral Large.
Which has more context: Kimi K3.1 Pro and Mistral Large?
Kimi K3.1 Pro accepts 1 M tokens of context; Mistral Large accepts 128 K.
Which is newer: Kimi K3.1 Pro and Mistral Large?
Kimi K3.1 Pro was released on 10/23/2026; Mistral Large on 02/26/2024.
What would 10,000 requests a month cost with Kimi K3.1 Pro and Mistral Large?
With 1,500 input and 400 output tokens per request: Kimi K3.1 Pro: $70.00; Mistral Large: $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.