How relevant is SpamAssassin scoring for modern email deliverability?
Matthew Whittaker
Co-founder & CTO, Suped
Published 28 Jul 2025
Updated 18 Aug 2025
6 min read
For years, SpamAssassin has been a cornerstone in the world of email filtering, widely adopted for its open-source nature and customizable rule sets. Many email senders, myself included, have relied on its scoring system to gauge the spamminess of their outgoing messages. A lower score typically indicates a higher likelihood of inbox delivery, while a higher score suggests potential spam classification. However, the email landscape has evolved dramatically.
The question I often hear now is: how relevant is SpamAssassin scoring for modern email deliverability? Is it still the go-to metric for ensuring your emails land in the inbox, or has its influence waned in the face of more sophisticated spam filtering technologies?
In this article, I’ll explore SpamAssassin’s role, its current impact, and what truly drives inbox placement in today’s complex email ecosystem. It’s important to understand that while SpamAssassin has its place, its direct influence on major email providers like Gmail and Yahoo is often misunderstood.
What is SpamAssassin and how it works?
SpamAssassin is an open-source email filter that identifies spam using a variety of heuristic and statistical tests. It assigns a numerical score to each incoming email based on how likely it is to be spam. This score is then compared against a predefined threshold, and if it exceeds that limit, the email is flagged or potentially rejected. The lower the score, the less spammy an email is considered to be.
It analyzes various aspects of an email, including its headers, content, and body. It uses a range of rules, each assigned a specific score, that collectively determine the email’s overall SpamAssassin score. For example, certain phrases, HTML structures, or even incorrect Microsoft 365 configurations can trigger points against an email.
While SpamAssassin is an excellent tool for understanding the technical nuances of email content and structure, it’s crucial to recognize its context. It's often used by smaller organizations or individual server administrators to filter incoming mail, providing a baseline level of spam protection. It can highlight issues like malformed headers or suspicious content patterns that might universally degrade an email's quality.
Understanding SpamAssassin scores
SpamAssassin assigns scores (positive for spam, negative for ham) based on various rules. A common threshold for an email to be marked as spam is 5.0, though this can be configured. If your email scores above this, it's likely to be filtered. Scores range from simple content checks to complex network tests like DNSBL lookups. Achieving a low score is desirable.
Content analysis: Examines text, HTML, and attachments for suspicious keywords, formatting, or hidden content.
Header analysis: Checks for forged headers, unusual routing, or missing standard headers.
Network tests: Queries DNS-based blocklists (DNSBLs) to see if the sending IP is listed.
Bayesian filtering: Learns from previously classified spam and ham to identify patterns.
The shift in email filtering paradigms
The email deliverability landscape has undergone a significant transformation. Major Internet Service Providers (ISPs) like Google and Outlook (and other large inbox providers) have moved beyond simple, rule-based content filtering. Their systems are now far more dynamic, relying heavily on artificial intelligence and machine learning to make filtering decisions.
This shift means that a good SpamAssassin score, while indicating a technically sound email, doesn't guarantee inbox placement with major providers. They prioritize other signals, such as sender reputation, engagement metrics, and authentication standards like SPF, DKIM, and DMARC.
A key reason for this diminished relevance is how quickly spam tactics evolve. SpamAssassin’s rule sets, while regularly updated, can struggle to keep pace with the real-time, adaptive nature of modern spam. As one discussion highlighted, the funny rules of SpamAssassin sometimes become less effective as the spam arms race has moved on. Furthermore, the sheer volume of mail handled by major ISPs demands a more scalable and dynamic approach than static scoring.
Traditional SpamAssassin filtering
Rule-based: Relies on a static set of rules and spam trigger words to assign scores.
Content-focused: Primarily examines email content, headers, and basic technical validity.
Local implementation: Often configured on individual mail servers for specific inbound filtering.
Limited scope: Does not account for broader sending patterns, sender reputation, or user engagement.
Modern ISP filtering
AI/ML driven: Utilizes complex algorithms to learn and adapt to new spam tactics.
Holistic approach: Considers sender reputation, authentication (SPF, DKIM, DMARC), and user engagement data.
Real-time adaptation: Continuously updates filtering decisions based on evolving threats and user feedback.
Large-scale impact: Affects billions of emails daily, influencing global deliverability.
Factors that truly drive inbox placement today
So, if SpamAssassin isn't the ultimate arbiter of inbox placement, what is? Modern email deliverability hinges on a combination of factors, with sender reputation taking center stage. Your domain and IP's reputation, built over time through consistent sending practices, is paramount.
Key elements include email authentication (SPF, DKIM, and DMARC), which verify your identity and prevent spoofing. Engagement metrics – opens, clicks, replies – are also critical indicators of whether recipients value your emails. High engagement signals legitimacy, while low engagement or high complaint rates can trigger spam filters and lead to your emails being directed to the junk folder or even being blacklisted.
It's a holistic assessment. ISPs look at everything from your sending infrastructure to your list hygiene, the content of your emails, and how subscribers interact with them. For a deeper dive into improving your chances, consider how to fix common deliverability issues.
Factor
Description
Impact on deliverability
Sender reputation
History of sending quality, spam complaints, and blocklist presence.
High impact: Directly influences whether emails reach the inbox or spam folder.
Crucial: Determines if ISPs trust your sending domain. Failures lead to rejection or spam folder placement.
Engagement metrics
Opens, clicks, replies, unsubscribes, and spam complaints from recipients.
Very high impact: ISPs use this to gauge email relevance and desirability.
List hygiene
Regularly removing inactive or invalid email addresses, avoiding spam traps.
Significant impact: Reduces bounces, complaints, and boosts sender reputation.
When SpamAssassin scores still matter (and when they don't)
While major ISPs have largely moved past relying solely on SpamAssassin, it still holds a degree of relevance for certain senders and recipient environments. If you’re sending to smaller businesses, academic institutions, or individual mail server owners, there’s a higher chance they might be running SpamAssassin as their primary or supplementary spam filter. In these cases, a good score can still contribute to successful delivery.
However, for anyone aiming for broad inbox placement with major consumer mail providers, optimizing purely for SpamAssassin scores is a misdirection. As one Emailmarketing Reddit thread pointed out, it doesn't affect email deliverability much, if at all, with these large providers. Your focus should instead be on the holistic factors mentioned earlier.
Navigating the modern deliverability landscape
Ultimately, while SpamAssassin remains a valuable diagnostic tool for evaluating the technical aspects of an email and can still be relevant for smaller, self-hosted mail servers, its direct impact on deliverability to major ISPs has significantly diminished. The modern email ecosystem prioritizes a sender's overall reputation, adherence to authentication standards, and recipient engagement.
To achieve consistent inbox placement, I urge senders to look beyond isolated content scores and adopt a comprehensive strategy. Focus on building and maintaining a strong sender reputation, ensuring robust authentication, nurturing a healthy subscriber list, and consistently providing valuable content. These are the true pillars of modern email deliverability.
Views from the trenches
Best practices
Prioritize a holistic approach to email deliverability over focusing solely on content scores.
Implement and monitor strong email authentication protocols, including SPF, DKIM, and DMARC, as these are critical trust signals for ISPs.
Maintain excellent list hygiene by regularly cleaning your subscriber lists and removing inactive or invalid addresses.
Common pitfalls
Over-optimizing email content based on outdated SpamAssassin rules, which can lead to unnatural or ineffective messaging.
Neglecting broader sender reputation signals, like complaint rates and bounce rates, while only looking at content scores.
Failing to adapt to the dynamic and AI-driven filtering systems used by major email providers.
Expert tips
Understand that major ISPs use complex, dynamic filtering systems that go far beyond simple content scanning.
Focus on a holistic approach to email deliverability, encompassing reputation, authentication, and engagement.
Regularly test actual inbox placement with various providers, as this reflects real-world filtering decisions.
Expert view
Expert from Email Geeks says: SpamAssassin scores can vary significantly between different versions, highlighting that relying on a single score is unreliable.
2020-04-21 - Email Geeks
Expert view
Expert from Email Geeks says: For major mailing lists, SpamAssassin's impact on delivery is minimal due to the small percentage of recipients on domains still using it as a primary filter.
Gmail and 
Yahoo is often misunderstood.
Microsoft 365 configurations can trigger points against an email.
Google and 
Outlook (and other large inbox providers) have moved beyond simple, rule-based content filtering. Their systems are now far more dynamic, relying heavily on artificial intelligence and machine learning to make filtering decisions.
