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CostPerPrompt

Mistral Large 3 2512 (batch) API Pricing

Mistral · context window 262K · prices updated 2026-09-15

Input per 1M tokens
$0.25
Output per 1M tokens
$0.75
Cached input per 1M
$0.025
90% cheaper than fresh input

Where Mistral Large 3 2512 (batch) sits on price

At $0.75 per million output tokens, Mistral Large 3 2512 (batch) is a mid tier model — 2.8× cheaper than the median output price of $2.08, which puts it cheaper than 74% and pricier than 26% of the models we track. It sits 14th cheapest of the 25 Mistral models we track. Output is only 3.0× the input price, an unusually flat spread: long prompts hurt about as much as long answers, so prompt size is where the savings are.

Output tokens per $1
1,333,333
One full context fill
$0.0655
Cheaper than
74% of tracked models

Cached input is 90% cheaper. On an input-heavy workload you need roughly a 28% cache-hit rate to take 25% off the input line — reachable for chatbots and agents that resend the same system prompt and history.

What real workloads cost on Mistral Large 3 2512 (batch)

These three workloads are the ones teams actually run on a mid-priced model — a $0.75/1M model is not bought for the same job as one ten times the price.

Workload Per request Per month
Support chatbot — 500 conversations/day (5K in / 1.4K out) Conversation history re-sent each turn — the classic prompt-caching win. $0.0023 $34.50
Document pipeline — 10K docs/day (3K in / 500 out) Batch-friendly: no user waiting, so a ~50% batch discount usually applies. $0.0011 $338
Small app — 1K requests/day (500 in / 150 out) The baseline most side projects actually run at. $0.0002 $7.13

Model your exact traffic in the API cost calculator — it preloads Mistral Large 3 2512 (batch) with caching and batch options.

Mistral Large 3 2512 (batch) price history

Mistral last cut Mistral Large 3 2512 (batch)'s output price on Sep 10, 2026 (-50% per output token), from $0.5/$1.50 to $0.25/$0.75 per 1M in/out. That is the only repricing in the 18 days we have tracked it — worth knowing before you hard-code today's rate into a budget.

Date Input /1M Output /1M Output change
Aug 28, 2026 (tracking began) $0.5 $1.50
Sep 10, 2026 $0.25 $0.75 -50%

Change-points from our daily price snapshots (tracking since Aug 28, 2026; intraday moves between snapshots are not captured).

Mistral Large 3 2512 (batch) vs Gemini 3.1 Flash Lite (batch)

The closest-priced alternative from another vendor is Gemini 3.1 Flash Lite (batch) (Google) — priced within a rounding error on output, with $0.125 less per million input tokens. When two models land this close on price, the decision is quality on your own workload, not the price sheet: run 50 real requests through both and compare.

See Gemini 3.1 Flash Lite (batch) pricing →

Cheaper alternatives

More Mistral models

Frequently asked questions

How much does the Mistral Large 3 2512 (batch) API cost?

Mistral Large 3 2512 (batch) costs $0.25 per million input tokens and $0.75 per million output tokens, with cached input at $0.025 per million (90% cheaper). That works out to roughly 1,333,333 output tokens per dollar.

What does the support chatbot workload cost on Mistral Large 3 2512 (batch)?

Support chatbot — 500 conversations/day (5K in / 1.4K out) costs about $0.0023 per request and $34.50 per month on Mistral Large 3 2512 (batch). Conversation history re-sent each turn — the classic prompt-caching win.

What does it cost to fill Mistral Large 3 2512 (batch)'s 262K context window?

Sending 262K of input in a single request costs $0.0655 at $0.25 per million tokens — before any output. With prompt caching that same fill drops to about $0.0066 on repeat requests. This is why large context windows are cheap to advertise and expensive to actually use.

Is Mistral Large 3 2512 (batch) worth the price?

Mistral Large 3 2512 (batch) sits in the middle of the market (14 of 25 by price within Mistral). The honest test is a routing experiment: send the same 200 real requests to this model and to a tier below, and compare failure rate against the price gap — most teams find a majority of traffic never needed the pricier model.