Juro vs Conga CLM
Seven of fourteen models named Juro first on the direct prompt; zero named Conga CLM. Juro was named by thirteen of the fourteen models and Conga CLM by fourteen and both carry 33 labels, so the shares below are directly comparable.
Named in one category this edition.
Named in one category 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 contract lifecycle management and e-signature page.
By framing
JuroFirst choices, of fourteen modelsConga CLM
Across every category in the October 2026 Edition, Juro and Conga CLM were named in the same answer twenty-two times, of the 64 answers naming Juro and the 82 naming Conga CLM. In those answers Conga CLM took the first choice zero times and Juro five.
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 | |
Juro Conga CLM first choice named as an alternative argued againstblank: not namedEach cell is one answer, Juro on the left and Conga CLM on the right.The direct prompt
Juro first, Conga CLM an alternative
7 of 14 modelsConga CLM was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
4 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
3 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 Juro
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Users have reported frustration with "complex filing structures" and issues with "handling attachments."” GLM 4.7 FlashX · negative prompt · soft negative
“the strongest default pick is Juro if you want a modern, easy-to-roll-out CLM with strong mid-market fit” Perplexity Sonar · direct prompt · first choice
“Choose Juro if you want a balance of powerful legal management and high adoption rates from non-legal users” Qwen 3.7 Flash · direct prompt · first choice
“I'd recommend starting with Concord or Juro if ease of use is priority” MiniMax M2.5 · direct prompt · first choice
What the models said about Conga CLM
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Customer reviews show high levels of dissatisfaction with feature usability, interface design and overall scalability” Claude Haiku 4.5 · negative prompt · hard negative
“Conga is among the most frequently criticized major CLM platforms... The biggest cautions are around Conga CLM” DeepSeek V4 Flash · negative prompt · hard negative
“generally avoid buying "Heavy Enterprise" platforms like Icertis or Conga” Qwen 3.7 Flash · negative prompt · hard negative
“Ironclad/Conga: Workflow-first, Salesforce-heavy, great for sales/legal speed.” Grok 4.1 Fast · scale prompt · first choice
“For teams already in the Salesforce ecosystem or requiring enterprise-grade features, Conga CLM is a strong contender.” Mistral Small · direct prompt · alternative
“a comprehensive CLM solution that helps design and streamline the entire contract lifecycle management process” Llama 4 Maverick · direct prompt · alternative
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.