buildConfirmed3 publishers Liquid AI says its LFM2.5-24B-A2B scores 48.09 on LiveClaw against 38.19 for the larger Qwen3.6-35B-A3B. The 184 tok/s it reports on an Apple M5 Max is a decode rate, so agent latency depends on how many tokens the model spends thinking.
Perspective Coverage
3 publishers
- Builder
- Builder 50%
- Operator
- Operator 25%
- Investor
- Investor 25%
Reality
- Evidence40
- Adoption
- Insufficient
- Hype gap+30
- Incentives70
- Confidence58
Microsoft released Decision-1, a model that scores fixed answer options and, by its own benchmarks, runs 35 times faster than GPT-6 Sol. Whether routing work moves off general-purpose models now depends on independent tests and on pricing that confirm those self-reported figures.
Reality
- Evidence30
- Adoption15
- Hype gap+35
- Incentives65
- Confidence35
buildConfirmed2 publishers Liquid AI's open-weight d1-3B answers classification questions in a single forward pass, taking 16 ms on a Jetson AGX Thor and 50 ms on an Orin Nano. That puts routing and intent tagging on edge boards without a large model, on figures Liquid measured itself.
Perspective Coverage
3 publishers
- Builder
- Builder 55%
- Operator
- Operator 30%
- Investor
- Investor 15%
Reality
- Evidence50
- Adoption
- Insufficient
- Hype gap+20
- Incentives70
- Confidence60
buildOne report1 publisher Liquid AI shipped a 279.5-million-parameter draft model for LFM2.5-VL-3B on September 24, reporting 2.30x to 3.13x faster decoding on an Apple M5 Max. Image encoding and prompt prefill are unchanged, and the company's tests did not cover quantized deployments.
Reality
- Evidence55
- Adoption
- Insufficient
- Hype gap+15
- Incentives70
- Confidence60
buildOne report1 publisher Online-SDFT fine-tunes a 230M model on an Android phone from delayed, unlabeled interactions. The teacher is the same frozen base weights with the adapter off, and the write-up reports no measurements.
Reality
- Evidence38
- Adoption12
- Hype gap+15
- Incentives65
- Confidence35