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Ramp data: 6.4% pay for open models

In Ramp's spending data, only 6.4% of AI-spending businesses pay for open-source models.

“Businesses are choosing open-weight AI models over expensive frontier models, which are now 90% as good at 10% to 25% of the token cost.”

IBM CEO Arvind Krishna, IBM third quarter 2026 earnings call (21 October 2026); trending on r/artificial

Recorded 25 October 2026. Our ruling means: literally true, materially misleading.

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Receipts pack

The evidence file for this verdict

14 receipts: 5 support the claim, 4 contradict it, 5 add context. 4 figures on the record, 4 traced to a quoted receipt.

The receipts

Each source, the line we quote from it, its date and which way it cuts. Free to read and to cite.

  1. 1Supports the claimconstellationr.com ↗

    open-weight models currently anywhere from 25% to 10% of the token cost of the frontier models

    Look up an archived copy ↗

  2. 2Supports the claimconstellationr.com ↗

    Would you like something that's 10% of the cost if it's 90% as good as the best?

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  3. 3Supports the claimconstellationr.com ↗

    you can get going with maybe 2 or 3 racks, which is not a huge amount of infrastructure

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  4. 4Supports the claimconstellationr.com ↗

    in late, 2025 we began to see the gap shrink

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  5. 5Contextfinance.yahoo.com ↗

    Krishna noted that IBM's on-premises IT infrastructure is well-positioned for the shift

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  6. 6Contextfinance.yahoo.com ↗

    they are also getting worried if some of the AI spending is completely out of control with no real return

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  7. 7Contextnewsroom.ibm.com ↗

    infrastructure revenue up 4 percent, up 5 percent at constant currency

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  8. 8Contextnewsroom.ibm.com ↗

    distributed infrastructure up 31 percent, up 32 percent at constant currency

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  9. 9Contextthenextweb.com ↗

    IBM has decided that the money in artificial intelligence is no longer only in building the cleverest model

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  10. 10Supports the claimrits.shanghai.nyu.edu ↗

    found that open-weight models now handle 34% of enterprise AI token usage, up from 23% a year earlier.

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  11. 11Contradicts the claimrits.shanghai.nyu.edu ↗

    But Ramp says most of the growth is going to cheaper closed models

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  12. 12Contradicts the claimrits.shanghai.nyu.edu ↗

    Only 6.4% of AI-spending businesses in Ramp's data pay for open-source models.

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  13. 13Contradicts the claimdev.to ↗

    the article names no benchmark behind the 70, 50 and 90 figures, so the quality half cannot be checked from the public record.

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  14. 14Contradicts the claimdev.to ↗

    open weights are not uniquely cheap. small models are.

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Members' extras

What each receipt proves, and what would change our mind.

  • What each receipt above proves, in our words
  • The figures table, each number traced to the receipt that states it
  • The trick behind the claim, explained, with the guide to spotting it
  • What would change our verdict
  • Our call on the record, with its due date

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