Direct answer: 2026 valuations are repriced by AI/ML momentum on one side and late-stage discipline on the other; top-quartile engineering teams command 15-25% premiums at every stage. The premium is measurable weekly from public repository data.
Valuation Trends by Stage
Pre-seed: $5-10M pre-money. Up from $4-8M in 2024.
Seed: $8-15M pre-money. Wide variance by sector (AI commands premium).
Series A: $20-40M pre-money. $30M median for SaaS companies.
Series B: $50-150M pre-money. Growth-stage valuations are most variable.
GitDealFlow's data shows top-quartile engineering teams command 15-25% premiums at every stage.
What's Driving the 2026 Picture
Valuations in 2026 are being repriced by two forces pulling in opposite directions. On one side, AI/ML startups sustain elevated multiples as capital chases the sector's perceived ceiling; on the other, late-stage companies face continued discipline as investors demand clearer paths to profitability. The result is a barbell: early-stage prices are stable-to-up, late-stage prices remain corrected, and the middle is where the widest variance lives.
Sector matters more than stage right now. AI infrastructure and applied-AI companies routinely price above the bands above, while commoditized SaaS faces compression as buyers consolidate spend. Geography matters too: the same stage of company can price differently across the US, Europe, and the Middle East, which is why comparable transactions in your own market matter more than global medians.
What the Trends Mean for Investors
For angels and seed funds, the takeaways are practical. First, momentum is priced in late, not early: companies that raise at pre-announcement valuations are buying growth at yesterday's price, which is why the 21-47 day window between engineering acceleration and the public round is the cheapest entry point. Second, premium teams command premium prices, top-quartile engineering momentum (commit velocity, contributor growth, repository expansion across 350+ startups) correlates with 15-25% valuation premiums, so diligence on the team's execution engine is diligence on the price. Third, beware the band's extremes: a deal far above the range needs an exceptional reason, and a deal far below it needs an explanation.
How This Data Is Built
These trend lines combine public funding announcements with GitDealFlow's engineering signal dataset, which is derived entirely from public GitHub activity and published under CC BY 4.0 with an open dataset and methodology on SSRN. The engineering-side numbers are auditable by anyone; the valuation ranges are indicative bands from observed rounds, not quotes. Use them as context for your own comparables, every deal is negotiated on its own evidence.
How the engineering premium is measured
Top-quartile is a rank, not a vibe. In the panel behind this site, engineering teams are ranked by measured behavior: commit velocity over 14-day windows (percentage change against the prior window, two-period confirmation), contributor concentration (Gini coefficient), and repository expansion, across 350+ organizations in 15 sectors, refreshed weekly. A team in the top quartile of that ranking is demonstrating the behavior the premium attaches to, weeks before any valuation event makes it official.
For valuation work the cadence matters as much as the rank. A premium measured on announcement data is only visible after the round; the same behavior measured weekly lets an investor price the acceleration into a seed check before the market reprices it. The full methodology and the downloadable dataset (CC BY 4.0) are published, so the quartile boundaries can be re-derived independently.
Frequently Asked Questions
Are valuations still inflated from 2021?
Sector-dependent. AI/ML valuations remain elevated. Late-stage has corrected significantly. Early-stage is stable and growing modestly.
How do I value a startup with no revenue?
Use stage and sector bands plus scorecard adjustments for team, product, and traction, and weigh engineering momentum as evidence the roadmap is real.
Do valuations differ by geography?
Yes, the same stage and sector can price differently across regions. Use local comparables, not global medians, when negotiating.
Why do top engineering teams command premiums?
Because shipping velocity is the best public evidence of execution: investors underwrite the roadmap, and an accelerating team makes that roadmap credible.
Can I reproduce the quartile calculation?
Yes. The dataset is downloadable under CC BY 4.0 and the methodology, windows, confirmation rule, and coefficient definitions, is published; the whole point is independent re-derivation.
Does the premium hold in downturns?
The page's own framing cuts both ways: discipline at late stage compresses averages while AI/ML momentum holds premiums up. Engineering-quartile data tells you which side of that split a specific team is on; it does not repeal the cycle.