Ministral 3 14B

mistralai/ministral-3-14b-instruct-2512

Mistral AI· 100K ctx

open weightstool callstructured quant: unknown

spread (output, p90 ÷ p10)

— too few providers

$0.200 – $0.500 · 2 providers · max÷min 2.5×

2 providers priced · 2 offerings

Including routers & resellers

Cheapest / median / spread use providers that serve the model on their own capacity: first-party lab, hyperscaler, inference host.

Provider classes in the stats:

Blended $/M assumptionsInput : output: 1Cache-hit share of input%

blended = 90.9% input × [(1 − 70% cache) × input + 70% cache × cache-read] + 9.1% × output + cache writes (30% of uncached input) where a write price is published

A provider that publishes no cache price is blended with cache share 0 (all input at the input price) and labelled “no cache price published” — never penalised against identical in/out prices.

Provider	Input	Output	Cache rd	Blended
DigitalOcean inference host	$0.200	$0.200	—	$0.200no cache price published
NanoGPT router / aggregator	$0.100	$0.400	$0.0500	$0.0955
Eden AI router / aggregator	$0.344	$0.458	—	$0.354no cache price published
Infomaniak inference host	$0.370	$0.500	—	$0.382no cache price published

Connect

Provider (default: cheapest output among direct providers)

$0.200 /M · inference host

opencode.json

{
  "provider": {
    "digitalocean": {
      "npm": "@ai-sdk/openai-compatible",
      "options": {
        "baseURL": "https://inference.do-ai.run/v1"
      },
      "models": {
        "mistral-3-14B": {}
      }
    }
  }
}

Set DIGITALOCEAN_ACCESS_TOKEN in your environment. Generated — verify against the OpenCode docs.

Output $/M over time7-day preview

Median line, min–max band. Pre-2026-09 points are catalog-recorded dates from models.dev git history, not provider effective dates.

Data: models.dev (MIT), snapshot 2026-09-20. Prices are vendor-published; verify before purchase. Methodology.