Sinoué Gad

Liquid AI has released d1-ultra, taking the #1 position on the Artificial Analysis Decision Index v1.0, supported by a reduction in hallucinations d1-ultra is the new decision model from @liquidai and follows on from their release of d1 earlier this month. It is priced at $0.042 per 1M input tokens compared to $0.02 per 1M input tokens for d1. Like d1, d1-ultra is billed only on input tokens. Key takeaways: ➤ d1-ultra leads the Artificial Analysis Decision Index v1.0 with a score of 65: d1-ultra scores 65, ahead of the recently released Jev 1.14 at 57 and GLiDE and Microsoft-Decision-1 at 54 ➤ d1-ultra’s score of 65 is driven by a much lower hallucination rate on AA-Omniscience-Decision: d1-ultra’s non-hallucination rate on AA-Omniscience-Decision is 85%, up substantially compared to d1 at 2%. The non-hallucination rate is the share of questions not answered correctly where the model chose "I don't know" and measures how well a decision model can judge its own knowledge in knowing when not to answer. While d1-ultra’s hallucination rate decreases, so does its accuracy (% questions correct). It achieves an accuracy of 32%, down 9 points compared to d1 (41%) ➤ d1-ultra has the third lowest Time per Task of any decision model: d1-ultra achieves a Time per Task of 92 ms, the third lowest of decision models measured, behind Jev 1.14 at 78 ms per task and Celeris-1 Decision at 63 ms per task ➤ d1-ultra offers comparable pricing to other decision models at $32 per 1M tasks: At $32 per 1M tasks, d1-ultra is comparably priced to Celeris-1 Decision ($32 per 1M tasks) and Microsoft-Decision-1 ($31 per 1M tasks), more than double the price of d1 and Mercury Decide at $15 per 1M tasks but less than half the price of GPT-6 Luna (Decisions) at $79 per 1M tasks

Which model should you run on the iPhone 17 Pro? Announcing our new intelligence and inference testing for small models on mobile devices: independent measurement of how capable small models are in typical on-device tasks, and how they perform on popular phones - in partnership with @liquidai We have partnered with @liquidai to deliver mobile device inference benchmarking, covering a range of models in 4-bit or lower precision on the iPhone 17 Pro and Galaxy S26 Ultra We’re publishing our phone-scale intelligence evaluation results in combination with @liquidai's inference performance benchmarks to give users and developers a holistic view of how small models are performing on phones Inference benchmarking is conducted in a controlled environment using Liquid AI’s inference benchmarking software, which Artificial Analysis has examined and is open-sourced on Liquid AI’s GitHub. Results cover end-to-end generation time, output speed, peak memory usage and other metrics. The inference benchmarking app, ‘Pipette’, is available to download for free on iOS and Android, allowing users to test a variety of models on their own devices We are ranking phone-scale model intelligence based on each model’s average score in five evaluations chosen for the task-based work these models do in practice: BFCL, IFBench, AA-Omniscience, GPQA Diamond and MATH-500. These evaluations are run by Artificial Analysis using our independent methodology. By default, we limit models to 16K context on each of these evaluations, representing the lack of memory space for significant KV cache on mobile devices. This leads to some intelligent but verbose models dipping in relative score - they were not designed for the constraints that phone memory imposes on token use We are defining our portable device category as including models that fit within 8 GB of memory after quantization, including KV cache, at 8K context We expect both the intelligence and inference benchmarks to evolve over time, as new models, devices, inference frameworks and quantization techniques are released. Our pages will remain up to date with these new additions Initial results: ➤ Nanbeige4.2-3B and LFM2.5-2.6B share the top average evaluation score at 63 (with a 16K context limit), ahead of Ornith-1.0-9B at 62 and Qwen3.5 9B (Reasoning) at 61. LFM2.5-2.6B achieves its score more efficiently: on an iPhone 17 Pro it answers a standard 1,024-token prompt in 8.0s using 2.3 GB of memory, against 21.4s and 4.0 GB for Nanbeige4.2-3B and 25+ seconds and 6.9 GB for the two 9B models ➤ The 16K context limit shapes the leaderboard: as an example, Qwen3.5 9B (Reasoning) spends 74.5M output tokens across one pass of the benchmark set, hitting the 16K limit on 29% of its generations and landing in fourth place overall. With the limit raised to 64K, Ling 3.0 Tiny takes first place with a score of 66, ahead of Nanbeige4.2-3B (65), Qwen3.5 9B (Reasoning, 64), and LFM2.5 2.6B (64). But a 64K window does not fit in mobile phone memory, and at 55 output tokens/s on an iPhone, generating 64K tokens could mean a 20+ minute wait and a lot of battery use. This is why our primary results are capped at 16K, but we're also publishing a set of results capped at 64K, and another capped at one minute of generation time ➤ The speed-intelligence Pareto frontier is short: six models are unbeaten on both intelligence and speed on an iPhone 17 Pro: LFM2.5-230M (27 at 0.9s), MiniCPM5-1B (45 at 2.9s), LFM2.5-8B-A1B (58 at 5.7s), Ling 3.0 Tiny (59 at 5.7s), LFM2.5-2.6B (63 at 8.0s) and Nanbeige4.2-3B (63 at 21.4s). LFM2.5-8B-A1B and Ling 3.0 Tiny are mixture-of-experts models that activate ~1B parameters per token, which is how they answer in under 6s with 8B-class weights ➤ Leading models have opposite strengths: Nanbeige4.2-3B is the most balanced (76% on BFCL, 96% on MATH-500, 67% on GPQA Diamond); Qwen3.5 9B (Non-reasoning) is the strongest tool caller (77% on BFCL) and scientific reasoner (79% on GPQA Diamond); LFM2.5-2.6B follows instructions best of any model measured on the iPhone (59% on IFBench), clears 90% on MATH-500 and hallucinates far less on AA-Omniscience (79% non-hallucination, against 33% for Nanbeige4.2-3B and 24% for Qwen3.5 9B (Reasoning)) More details below in thread ⬇️