★ Mistral · Mid-tier · Open weights

Mistral Large 4 API Pricing Calculator

$0.70 per 1M input tokens, $2.10 per 1M output. Open-weight multimodal model with a 1M window. A cheap mid-tier option, and you can self-host the same weights later.

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Updated 7 Nov 2026

Price card · Mistral Large 4

Mistral

Input: $0.70 / 1M
Output: $2.10 / 1M
Cached input: $0.07 / 1M
Batch: 50% off
Context window: 1M tokens
Tier: Mid-tier

Workload

Requests per day

Avg input tokens

tok

Avg output tokens

tok

Cached input

Repeated system prompt or docs

Batched requests

Jobs that can wait hours

Models

Anthropic

OpenAI

Google

Mistral

DeepSeek

$ per 1M tokens, input / output

Monthly cost · 30 days

Cheapest: $285; Gemini 3.1 Flash-Lite
Priciest: $788; Gemini 3.8 Flash
Spread: 2.8×; priciest vs cheapest

	Per request		Per 1K users / mo
Gemini 3.1 Flash-LiteCheapest	$0.00095	$285	$143
Mistral Large 4	$0.00203	$609	$305
Gemini 3.8 FlashIntro price	$0.00263	$788	$394

Per 1K users assumesrequests per user per day.

Where the money is

If 60% of your traffic is simple enough for Gemini 3.1 Flash-Lite, routing it there instead of Gemini 3.8 Flash saves about $302/month.

You're looking at $285–$788/month.

Most AI products I audit can cut that 30–60% with routing, caching and shorter prompts.

Get a cost-cutting audit

What Mistral Large 4 costs in practice

List prices per million tokens are hard to reason about. Here is what common request types cost on Mistral Large 4, with no caching or batch discount applied.

Mistral Large 4 cost by request type
Request type	Tokens in / out	Per call	Per 1K calls	Per 1M calls
Classification or routing call	400 / 10	$0.00030	$0.3010	$301
Support chat reply	1,500 / 250	$0.00158	$1.58	$1,575
RAG answer over your docs	6,000 / 400	$0.00504	$5.04	$5,040
Long document summary	40,000 / 1,000	$0.0301	$30.10	$30,100

Pricing notes

Open-weight multimodal model with a 1M window. A cheap mid-tier option, and you can self-host the same weights later.

Listed as Public Preview in the Mistral docs at the time of verification.

Source: docs.mistral.ai, api, mistral-large-4-0. Verified 7 Nov 2026.

Compare

Or compare all 14 models in the full calculator.

How the math works

Every provider bills input tokens (your prompt, system prompt and retrieved documents) and output tokens (the reply) separately, per million. Output is usually 4–5× the input price, so chatty apps are output-heavy and RAG apps are input-heavy.

cost per request = (fresh_in × input_price

+ cached_in × cached_price

+ out × output_price) / 1,000,000

× (1 − batched_share × batch_discount)

monthly = cost per request × requests per day × 30

Two adjustments are applied automatically. Introductory prices (currently on some Gemini models) are used until their end date. Long-prompt surcharges kick in when your average input crosses the provider's threshold: 272K tokens on current OpenAI models, 100K on Claude Haiku 5.5.

Not modelled: cache-write premiums, priority or fast tiers, regional surcharges, image and audio tokens, and reasoning tokens a model spends before answering (they bill as output, so add them to your output estimate if you use extended thinking).

Mistral Large 4 questions

How much does Mistral Large 4 cost per 1M tokens?

Mistral Large 4 costs $0.70 per 1M input tokens and $2.10 per 1M output tokens, with cached input at $0.07 per 1M. Batch requests get 50% off.

What is the Mistral Large 4 context window?

1M tokens. A large window does not mean you should fill it: every token you send is billed, so retrieve only what the request needs.

Is Mistral Large 4 cheaper than Gemini 3.8 Flash?

For a typical request of 2,000 input and 300 output tokens, Mistral Large 4 is about 1.3× cheaper ($0.00203 vs $0.00263 per call for Mistral Large 4 and Gemini 3.8 Flash). Price is only half the decision; test both on your own prompts.

What is prompt caching and should I use it?

When the start of your prompt (system prompt, tool definitions, a long document) repeats across requests, providers can serve it from cache at a fraction of the input price, often 90–99% off. If your requests share a large fixed prefix, caching is usually the single biggest saving available. Put the stable content first and the variable content last.

Why does my real bill differ from this estimate?

Usually because of tokens you are not counting: reasoning or thinking tokens (billed as output), retries, tool-call round trips, and cache-write premiums. Compare the estimate against the usage page in your provider console for a week and adjust the averages.

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Fine print

Tools: on the house.
Engineer: by the hour.

Knowing your AI bill was free. Cutting it in half isn't, but I'm cheaper than the overspend.

I'm Himanshu, an AI and full-stack engineer. I build MVPs, AI agents and the growth plumbing around them for early-stage startups.

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