Running DeepSeek-V4.1-Flash locally shouldn't mean sacrificing quality. Meet the most faithful MLX quantization of DeepSeek-V4.1-Flash-1017. We built the ultimate version for 256GB+ Macs, maximizing both quality and speed. 👑 The New Standard for MLX: ✅ Highest Quality: 35–44% lower Mean KL. Crushes the leading 2-bit MLX quant (85.1% vs 79.3% top-1). ✅ No Compromises: ZERO experts pruned. All 40x384 kept. ✅ Advanced Tech: Sequential quantization, sensitivity-driven mixed precision, and Engram tables streamed from SSD. ✅ Fastest Path: A major high-speed inference release is imminent. The best local DeepSeek-V4.1-Flash-1017 on Mac is ready. 🔗 Weights & comparisons: huggingface.co/rapid-mlx/Dee…

DeepSeek V4.1 Flash 1017 scores 45 on the Artificial Analysis Intelligence Index, improving on DeepSeek V4.1 Flash by 6 points, and is the equal #2 open-weights model @deepseek_ai has released DeepSeek V4.1 Flash 1017, an update on their flagship model DeepSeek V4.1 Flash. On the first-party API, DeepSeek V4.1 Flash 1017 is priced at $0.30 per 1M input tokens and $1.20 per 1M output tokens during peak hours, with off-peak pricing half of peak rates, the same pricing as DeepSeek V4.1 Flash. Key results: ➤ DeepSeek V4.1 Flash 1017 makes gains in agentic capabilities, knowledge and scientific reasoning. DeepSeek V4.1 Flash 1017’s Elo rating on GDPval-AA v2.1 is 1746, an increase of 146 points from DeepSeek V4.1 Flash’s rating of 1600. On AA-Briefcase v1.1, its Elo rating increases by 150 points from 1420 to 1570. On AA-Omniscience, its non-hallucination rate is 33%, a 29-point improvement from DeepSeek V4.1 Flash’s 4%, while seeing an improvement in accuracy from 43% to 46%, pushing its overall AA-Omniscience Index score from -5 to +5. On CritPt, DeepSeek V4.1 Flash 1017 scores 24%, up from 14% for DeepSeek V4.1 Flash. ➤ DeepSeek V4.1 Flash 1017 is now the #2 open-weights model alongside GLM-5.3. With a score of 45 on the Artificial Analysis Intelligence Index, DeepSeek V4.1 Flash 1017 is behind MiMo-V2.6-Pro by only 1 point and ahead of other open-source models like Step 5 Preview and Kimi K3 (both at 44). ➤ With a Cost per Task of $0.26, DeepSeek V4.1 Flash 1017 sits in the most attractive quadrant of Capability vs. Cost. With a Cost per Task of $0.26 and Intelligence Index score of 45, DeepSeek V4.1 Flash 1017 compares favorably to GLM-5.3-Flash at $0.28 per task and a score of 42. At the same time, MiMo-V2.6-Pro (46) at $0.13 per task and GPT-6.1 Sol (medium, 48) at $0.21 per task both offer higher capability at a lower Cost per Task. Additional model details: ➤ Context window: 1M tokens (equivalent to DeepSeek V4.1 Flash) ➤ Pricing: $0.30 per 1M input tokens and $1.20 per 1M output tokens, unchanged from DeepSeek V4.1 Flash. Cache hit price of $0.006 per 1M tokens, a 98% discount ➤ Size: 552B total parameters (16B active) ➤ License: MIT ➤ Providers: Available through DeepSeek’s first-party API, @FireworksAI_HQ , @baseten, @DeepInfra , @wafer_ai , @Cloudflare and others

DeepSeek V4.1 Flash 1017 scores 45 on the Artificial Analysis Intelligence Index, improving on DeepSeek V4.1 Flash by 6 points, and is the equal #2 open-weights model @deepseek_ai has released DeepSeek V4.1 Flash 1017, an update on their flagship model DeepSeek V4.1 Flash. On the first-party API, DeepSeek V4.1 Flash 1017 is priced at $0.30 per 1M input tokens and $1.20 per 1M output tokens during peak hours, with off-peak pricing half of peak rates, the same pricing as DeepSeek V4.1 Flash. Key results: ➤ DeepSeek V4.1 Flash 1017 makes gains in agentic capabilities, knowledge and scientific reasoning. DeepSeek V4.1 Flash 1017’s Elo rating on GDPval-AA v2.1 is 1746, an increase of 146 points from DeepSeek V4.1 Flash’s rating of 1600. On AA-Briefcase v1.1, its Elo rating increases by 150 points from 1420 to 1570. On AA-Omniscience, its non-hallucination rate is 33%, a 29-point improvement from DeepSeek V4.1 Flash’s 4%, while seeing an improvement in accuracy from 43% to 46%, pushing its overall AA-Omniscience Index score from -5 to +5. On CritPt, DeepSeek V4.1 Flash 1017 scores 24%, up from 14% for DeepSeek V4.1 Flash. ➤ DeepSeek V4.1 Flash 1017 is now the #2 open-weights model alongside GLM-5.3. With a score of 45 on the Artificial Analysis Intelligence Index, DeepSeek V4.1 Flash 1017 is behind MiMo-V2.6-Pro by only 1 point and ahead of other open-source models like Step 5 Preview and Kimi K3 (both at 44). ➤ With a Cost per Task of $0.26, DeepSeek V4.1 Flash 1017 sits in the most attractive quadrant of Capability vs. Cost. With a Cost per Task of $0.26 and Intelligence Index score of 45, DeepSeek V4.1 Flash 1017 compares favorably to GLM-5.3-Flash at $0.28 per task and a score of 42. At the same time, MiMo-V2.6-Pro (46) at $0.13 per task and GPT-6.1 Sol (medium, 48) at $0.21 per task both offer higher capability at a lower Cost per Task. Additional model details: ➤ Context window: 1M tokens (equivalent to DeepSeek V4.1 Flash) ➤ Pricing: $0.30 per 1M input tokens and $1.20 per 1M output tokens, unchanged from DeepSeek V4.1 Flash. Cache hit price of $0.006 per 1M tokens, a 98% discount ➤ Size: 552B total parameters (16B active) ➤ License: MIT ➤ Providers: Available through DeepSeek’s first-party API, @FireworksAI_HQ , @baseten, @DeepInfra , @wafer_ai , @Cloudflare and others

Six months ago, a friend at @seeedstudio handed me this $50 ESP32 AI gadget. I tossed it in a drawer and forgot about it. @Meta dropped the open-source Muse SDK last week. I dusted the device off, but it still felt restricted to just one "brain." I wanted total control, better privacy, and the freedom to use Claude, Astra, or my own local models. So I gutted the software and rebuilt it. The result works so well that I decided to open-source the project—turning this dusty gadget into a lightning-fast, multi-model personal assistant. Here is why it actually feels alive: 🗣️ The Ultimate Polyglot: Speak to it in English, Español, 中文, 日本語, or 한국어. It instantly auto-detects your language and replies in the exact same tongue, picking the best voice model automatically. 🧠 Bring Your Own Brain: Plug in ANY OpenAI-compatible API. You aren't locked in anymore. Swap between Claude, GPT, or local models with a 1-line preset. ⚡️ No More Awkward Pauses: We slashed the latency from a clunky 10s+ down to ~3s. It literally starts talking to you while the LLM is still generating the text. 🛑 True Conversationalist: You can interrupt it mid-sentence without breaking its brain (true barge-in), and we added silence guards so it never hallucinates from background room noise. All the heavy lifting (TLS optimizations, audio compression, crash-recovery) is handled under the hood. Grab your dusty ESP32 toys and give them a real brain: 🔗 github.com/raullenchai/esp32…

Nvidia’s Nemotron 3.5 Super VL is now the leading US open weight model, scoring 26 on the Artificial Analysis Intelligence Index Nemotron 3.5 Super VL achieves twice the intelligence of its predecessor, Nemotron 3 Super (13), and does so using ~60% fewer output tokens per task It launches alongside Nemotron 3.5 Bolt 74B-A9B 24GB, the first model in its new Bolt family, which scores 21 on the Intelligence Index Congratulations to @NVIDIAAI on the releases! Key Takeaways: ➤ Leading US open weight model: Nemotron 3.5 Super VL scores 26.5, edging out Thinking Machines' Inkling Small (25.7) and ahead of NVIDIA's Nemotron 3 Ultra (23) and Meta's Muse Glimmer (17) ➤ Bolt comes within 2 points of Ultra: Nemotron 3.5 Bolt 74B-A9B 24GB scores 21, 1.5 points behind Nemotron 3 Ultra 550B A55B (23). The 2 bit memory has about a seventh of Ultra's parameters (74B vs 550B) and a sixth of its active parameters (9B vs 55B) ➤ Super 3.5 VL near lossless in NVFP4: Nemotron 3.5 Super VL releases in NVFP4, scoring within 0.1 points of its BF16 weights on the Intelligence Index, with no component moving by more than 2.6 points Nemotron 3.5 Super VL details Context window: 1M tokens Modalities: Vision and text input; text output Parameters: 120B / 12B Nemotron 3.5 Bolt 74B-A9B 24GB details Context window: 256k tokens Modalities: Vision and text input; text output Parameters: 74B / 9B