Qwen2.5 7B Instruct pricing.

Prices checked

Qwen2.5 7B Instruct costs $0.10 in and $0.20 out per million tokens from OpenRouter; the cheapest price is $0.05 at SiliconFlow, checked .

OpenRouter's own price

OpenRouter

$0.13/M tokens

$0.10 in · $0.20 out

86% below the typical price of the 392 text models we track.

Cheapest price

SiliconFlow

$0.05/M tokens

$0.05 in · $0.05 out

60% less than OpenRouter, and the provider says why.

tool callingstructured outputopen weights33k contextreleased 16 Oct 2024

Every provider selling Qwen2.5 7B Instruct

  1. 1

    SiliconFlow

    Runs it themselves

    $0.05 in · $0.05 out

    $0.05/M tokens
  2. 2

    Eden AI

    Regional price

    $0.04 in · $0.10 out

    $0.06/M tokens
  3. 3

    DeepInfra

    Runs it themselves

    $0.04 in · $0.10 out

    $0.06/M tokens
  4. 4

    Novita

    Runs it themselves

    $0.07 in · $0.07 out · 32k context here

    $0.07/M tokens
  5. 5

    Jiekou

    Resells

    $0.07 in · $0.07 out · 32k context here

    $0.07/M tokens
  6. 6

    OpenRouter

    Same as maker

    $0.10 in · $0.20 out

    $0.13/M tokens
  7. 7

    Kilo Gateway

    Same as maker

    $0.10 in · $0.20 out

    $0.13/M tokens
  8. 8

    Phala (RedPill)

    Same as maker

    $0.10 in · $0.20 out

    $0.13/M tokens
  9. 9

    Nous Portal

    Same as maker

    $0.10 in · $0.20 out

    $0.13/M tokens
  10. 10

    Together AI

    Costs more

    $0.30 in · $0.30 out

    $0.30/M tokens

Not ranked

Below the maker's own price with nothing published to account for it, from a catalog we could not read reliably, or a free tier, which is a limit rather than a price. Why

  1. given away with limits

    free
  2. Featherless

    Not ranked

    $0.40 in · $0.80 out

    $0.50/M tokens

Something else wrong?

What it costs for your work
pages a month
$0.07

A month at OpenRouter's own price

$0.03

A month at SiliconFlow, the cheapest

$0.04

What you save

A page is about 600 tokens in and 60 out, a support reply 200 in and 300 out, an email 150 in and 250 out. Nothing else is counted: no cache discount, no batch rate, no free tier.

How we count

Prices are per million tokens. The big number on each row is blended 3 in : 1 out, the way most work uses a model. A price below OpenRouter's own is listed as cheapest only when the provider says why. Alibaba Cloud trained Qwen2.5 7B Instruct and sells it on no catalog we read, so the price every row is measured against is OpenRouter's.

Runs it themselves
The provider runs the open weights on hardware of its own, so the price is its own rather than a resale.
Regional price
A price list for one region, lower than the international one. A region, not a deal.
Resells
The provider sells another company's endpoint, at a margin of its own or at the maker's price.
Same as maker
Sold by someone else at the price the maker charges.
Costs more
Sold by someone else for more than the maker charges.
Free tier
Given away inside a quota. A limit is not a price, so it is never called cheapest.
Not ranked
Below the maker's price on a closed model with nothing to account for it, or from a catalog we could not read reliably. Listed, and never a pick.

Similar models

Models from other labs that do what Qwen2.5 7B Instruct does, cheapest first. Each percentage is against Qwen2.5 7B Instruct's cheapest price, $0.05 per M tokens.

Frequently asked

Who sells Qwen2.5 7B Instruct cheapest?
SiliconFlow, at $0.05 in and $0.05 out per million tokens, checked 31 Oct 2026. The provider runs the open weights on hardware of its own, so the price is its own rather than a resale.
Is Qwen2.5 7B Instruct open weights?
Yes. Alibaba Cloud publishes the weights, so anyone may host Qwen2.5 7B Instruct, which is why 11 providers sell it and why their prices differ so much.
How much context does Qwen2.5 7B Instruct hold?
32,768 tokens in one call, which is about 33k, and it can call your own functions.
How does Qwen2.5 7B Instruct compare with the rest of the market?
At $0.13 per million tokens, OpenRouter's own price is 86% below the typical $0.88 across the 392 text models we track.

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