| What it does | Compare LLMs with evidence, not vibes
| Turn Reddit into a growth channel by finding buyer threads, writing posts and replies, and getting mentioned by AI
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| Stage | Not given
| Startup
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| Founded | Not given
| Not given
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| Based in | Not given
| IN
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| Pricing model | Free
| Freemium
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| Pricing | Not given
| Starter: $69/month (was $89/month); Autopilot: $499/month; Custom: contact for pricing. Free 3-day trial available.
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| Key features | - Compare up to 1,080 language, vision and embedding models
- Track 279 benchmarks with 7,082 results
- Side-by-side model comparison tool
- Model Leaderboard ranked by benchmark performance
- API Cost Calculator for workload estimation
- Coding-specific model guides
- Benchmark explorer with evaluation conditions
- Traceable source links for all data
- Community notes and ratings on model pages
- API price verification with dates
| - Track mentions and replies across Reddit
- Score conversations for buying intent (0-100)
- Monitor which threads Google and AI cite
- View competitor share of voice in AI answers
- Auto-generate reply drafts for threads
- Content Engine builds weekly post calendar
- Post from OpenScout's warmed-up Reddit accounts
- Analyze competitor recommendations
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| What makes it different | - Every figure traceable to published source with verification dates
- Evaluation modes labeled separately, not blended into single scores
- Missing data left blank instead of guessed
- Real workload API cost estimation tool
- Powered by DataLearner open AI data platform
| - AI scores every thread for buying intent so you only engage conversations that move the needle
- Tracks AI visibility - shows if Google and AI recommend you or competitors
- Posts from OpenScout's own Reddit accounts for higher authority
- Anti-ban playbook ensures compliance with Reddit rules
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 11 ▲ +1 (up) since last check | 28 ▲ +1 (up) since last check |
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| Trust Flow (Majestic) | 17 | 5 |
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| Citation Flow (Majestic) | 40 | 24 |
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| Referring domains (Majestic) | 33 | 47 |
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LLMLearner
OpenScout