Postal vs Lob
Five of fourteen models named Postal first on the direct prompt; zero named Lob. Postal was named by thirteen of the fourteen models and Lob by ten and Postal carries 46 labels and Lob 18, so the shares are not directly comparable.
Named in two categories this edition.
Named in three categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the direct mail and gifting platforms page.
By framing
PostalFirst choices, of fourteen modelsLob
Across every category in the October 2026 Edition, Postal and Lob were named in the same answer sixty-four times, of the 160 answers naming Postal and the 294 naming Lob. In those answers Lob took the first choice thirteen times and Postal two.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | | | ||
| GPT-5.4 mini | | |||||
| Gemini 3.5 Flash | | | | | | |
| Perplexity Sonar | | |||||
| Grok 4.1 Fast | | | | | | |
| Mistral Small | | | | |||
| DeepSeek V4 Flash | | | | |||
| Llama 4 Maverick | | |||||
| Qwen 3.7 Flash | | | | | | |
| Kimi K2 | | | | | | |
| GLM 4.7 FlashX | | | | |||
| MiniMax M2.5 | | | | |||
| GPT-6 Luna | | | | | ||
| Muse Glimmer 30B | | |
Postal Lob first choice named as an alternative argued againstblank: not namedEach cell is one answer, Postal on the left and Lob on the right.The direct prompt
Postal first, Lob not the choice
5 of 14 modelsLob was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
3 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
6 of 14 modelsThe answer made no first choice from these two in this category.Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
What the models said about Postal
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“HubSpot integration is underdeveloped — cannot report Postal activity back into HubSpot” Kimi K2 · negative prompt · hard negative
“Postal is no longer an independent alternative—Sendoso acquired it in May 2025.” GPT-6 Luna · direct prompt · hard negative
“Postal is widely recognized as the best direct mail and gifting platform for mid-market B2B companies” Claude Haiku 4.5 · direct prompt · first choice
“Postal.io - a simple and user-friendly platform that is favoured by small to mid-sized businesses.” Llama 4 Maverick · paraphrase prompt · first choice
“Postal: The Best All-Rounder for Mid-Market B2B ... Postal is the strongest fit” Gemini 3.5 Flash · paraphrase prompt · first choice
What the models said about Lob
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“Avoid pure mail-only (e.g., Lob, PostGrid) if gifting is key.” Grok 4.1 Fast · direct prompt · hard negative
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.