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Index › Marketing › Direct mail & gifting › Reachdesk vs Lob
Direct mail and gifting platforms · October 2026 Edition

Reachdesk vs Lob

One of fourteen models named Reachdesk first on the direct prompt; zero named Lob. Reachdesk was named by fourteen of the fourteen models and Lob by ten and Reachdesk carries 59 labels and Lob 18, so the shares are not directly comparable.

Reachdesk

accepted challenger

Named in three categories this edition.

Lob

accepted challenger

Named in three categories this edition.

First-choice share16%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate19%17%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#6A position in a field of 10; printed, not drawn.
Labels5918A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Reachdesk reading right to left. Rank and label count are printed, not drawn.Sendoso was named alongside these two in twelve of the fourteen direct answers. Sendoso vs Reachdesk · Sendoso vs Lob · Reachdesk vs Postal

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

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
ReachdeskFirst choices, of fourteen modelsLob
Direct101 against Lob
Paraphrase601 against Reachdesk
Comparative34
Budget-constrained026 against Reachdesk
Scale-constrained20
Negative104 against Reachdesk · 2 against Lob
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Across every category in the October 2026 Edition, Reachdesk and Lob were named in the same answer ninety-two times, of the 231 answers naming Reachdesk and the 294 naming Lob. In those answers Lob took the first choice thirty-one times and Reachdesk ten.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Reachdesk and Lob stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Reachdesk Lob first choice named as an alternative argued againstblank: not namedEach cell is one answer, Reachdesk on the left and Lob on the right.

The direct prompt

The plain question, one answer per model, grouped by where Reachdesk and Lob stood in it.

Reachdesk first, Lob not the choice

1 of 14 modelsLob was named in the answer but not as the choice, or not at all.
Mistral SmallReachdesk, Sendoso alternatives: Postal, Postie

Neither was the first choice, one was named

11 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5Postal alternatives: Reachdesk, Sendoso
GPT-5.4 miniSendoso alternatives: Reachdesk
Gemini 3.5 FlashPostal alternatives: Goody, Reachdesk
Perplexity SonarSendoso alternatives: Reachdesk, Stannp
Grok 4.1 FastSendoso alternatives: Giftsenda, Postal, Reachdesk
DeepSeek V4 FlashSendoso alternatives: Postalytics, Reachdesk
Qwen 3.7 FlashSendoso alternatives: Postal, Reachdesk
Kimi K2Postal alternatives: Goody, Reachdesk
GLM 4.7 FlashXPostal alternatives: Postalytics, Reachdesk, Sendoso
MiniMax M2.5Sendoso alternatives: Alyce By Sendoso, Reachdesk
GPT-6 LunaSendoso alternatives: Reachdesk

Neither was named

2 of 14 modelsThe answer made no first choice from these two in this category.
Llama 4 MaverickSendoso
Muse Glimmer 30BPostal alternatives: Alyce, Sendoso

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Lob leads by three points.
Lob5%#5 of 9
Reachdesk2%#8 of 9
The full small business standing →
Mid-marketThe figures above
The order flips: Reachdesk leads at mid-market.
Reachdesk16%#2 of 10
Lob4%#6 of 10
The full mid-market standing →
Enterprise
Reachdesk leads by twenty-two points.
Reachdesk28%#2 of 9
Lob6%#4 of 9
The full enterprise standing →

What the models said about Reachdesk

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of six in this category shown.

“Reachdesk | ~$20,000/year | Enterprise-focused with annual contracts” Kimi K2 · budget prompt · hard negative
“Avoid enterprise tools like Sendoso/Reachdesk/Postal” Grok 4.1 Fast · budget prompt · hard negative
“Reachdesk is excellent, but its $20K+ entry point is typically better suited to companies with more established, high-volume gifting programs.” DeepSeek V4 Flash · paraphrase prompt · soft negative
“Reachdesk: The undisputed leader for global B2B gifting. Excellent "pay on redemption" features and localized international logistics.” Gemini 3.5 Flash · scale prompt · first choice
“Best Overall: Reachdesk leads for strategic gifting at scale, delivering global reach, AI personalization, and real-time ROI tracking” Claude Haiku 4.5 · paraphrase prompt · first choice
“I'd recommend Reachdesk if you want the strongest balance of B2B sales use cases, CRM integration, and scale” Perplexity Sonar · 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
Also compared

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.