Deepseek V4.1 Flash 1017
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Headlines · 14
- Naive-N0.5-Flash open-weights coding model prioritizes speed
NaiveAI released Naive-N0.5-Flash, an open-weights model for AI coding that continues training from Xiaomi’s MiMo-V2.5 and runs with the NaiveRT inference engine. Latent Space found that it answered faster overall than DeepSeek V4.1 Flash 1017 and GLM 5.3 Flash in limited tests, but it took longer on harder questions and had a slightly higher cost per question. NaiveAI reports about 984 tokens per second during thinking and 1,816 tokens per second while producing the answer, with a roughly 2,100-token-per-second peak measured on a single eight-GPU Blackwell node.
Latent Space · 🔥 8 - M5 Ultra Mac Studio beats a single GB10 in agentic test but trails a Spark pair
Tom's Hardware benchmarked an M5 Ultra Mac Studio with a 36-core CPU, 80-core GPU and up to 512GB of unified memory against a single Dell Pro Precision GB10 system, which it described as nearly identical to an Nvidia DGX Spark. Using llama.cpp with Qwen 3.8 27B and its native multi-token prediction, the Mac completed the Artificial Analysis AgentPerf-Local test in just under half the time of the GB10 system. The report also cited 1.2 TB/s of memory bandwidth for the Mac versus 273 GB/s for the GB10. For DeepSeek V4.1 Flash 1017, however, the Mac's llama.cpp setup took 10–25 minutes for the longest tested contexts, while a two-Spark cluster using DSpark speculative decoding and TensorFold took under five minutes. The report said Mac Studio lead times stretched into February 2027.
Tom’s Hardware · 🔥 7 - DeepSeek V4.1 Flash vs Qwen 3.8 FN in One-Prompt V8 Test
Reddit user /u/jesdga95 reported running the same one-shot prompt for an interactive V8 engine with two local models. DeepSeek V4.1 Flash 1017, using UD IQ1_M quantization and a single agent, generated about 250,000 tokens at roughly 94 tokens per second over about 60 minutes, used a 346,000-token context without compaction, and produced an engine the author said ran on the first attempt with working buttons. Qwen 3.8 FN, using NVFP4 quantization with one main agent and five subagents, ran at about 350 tokens per second in aggregate and took around 70 minutes. Two subagents did not complete their assigned work, while the author said the result included more parts and a better cutaway but also floating components and a less attractive appearance.
r/LocalLLaMA · 🔥 2 - Local Models Find Practical Uses in Research, Coding, and Automation
A Reddit user reports using DeepSeek v4.1 Flash 1017 with Fraser Price's BRRR on four RTX 6000 Blackwells, achieving approximately 550 tokens per second at concurrency 1 and a couple thousand at concurrency 16. The user describes practical local-model workflows for web research, coding, news filtering, event crawling, personal summaries, and speech-to-text, while calling frontier models for broader coordination. The poster says these setups are still limited by manual operation and that broader software-factory automation has not yet worked well.
r/LocalLLaMA · 🔥 0 - AI agents breach simulated corporate networks in Vals AI benchmark
Vals AI’s CyberOffense benchmark tests whether AI agents can independently compromise realistic simulated corporate networks, with every action externally logged and automatically scored. The Decoder reports that agents progressed from web applications to Windows servers and administrative privileges, while the harder Lighthouse test covered lateral movement into a second company. Results varied by test: the report presents conflicting Nova leader claims and score figures, while Lighthouse results reportedly favored GPT-6 Sol Daybreak over Deepseek V4.1 Flash 1017.
The Decoder · 🔥 0 - Artificial Analysis Endpoint Accuracy Index v1.1 expands coverage
Artificial Analysis launched Endpoint Accuracy Index v1.1, expanding coverage to six open-weight models, including GLM-5.3 Flash and Muse Spark Open. The index compares provider endpoints with a self-hosted reference using tool calling, scientific reasoning and long-context recall; its updated AA-LCR v1.1 uses 25 repeats and narrows the average 95% confidence interval from ±4.7 to ±2.6 points.
ArtificialAnlys (X) · 🔥 0 - DeepSeek V4.1 Flash 1017 Tested on Weight-Space Programming
A Reddit user reported that DeepSeek V4.1 Flash 1017 inferred the behavior of four small reinforcement-learning networks from rendered weights, an architecture outline and four candidate tasks. The model succeeded on CartPole, Flappy and Catch but confused BallBeam with CartPole; the author stressed that each checkpoint received only one trial, so the results do not establish reliability.
r/LocalLLaMA · 🔥 0 - Reddit User Praises DeepSeek V4.1 Flash 1017
A Reddit user says DeepSeek V4.1 Flash 1017 is fast, inexpensive, and reliable enough for everyday tasks, contrasting it with earlier open-weight models they found less dependable. The post is a personal appreciation, not an independent benchmark or reproducible evaluation, and it does not cite an official DeepSeek announcement.
r/LocalLLaMA · 🔥 0 - User seeks tuning for Crawl4AI and SearXNG research
A user reports that research with local models through Crawl4AI and SearXNG often yields missing, outdated or less relevant results. They subjectively compare the results unfavorably with ChatGPT and Codex, citing GLM 5.3 Flash and DeepSeek V4.1 Flash 1017. The user says they have used ZCode, DeepSeek Harness and OpenCode, and asks whether Crawl4AI and SearXNG can be tuned or paired with another approach to improve research results.
r/LocalLLaMA · 🔥 0 - Rivermark benchmark finds tuned open models competitive on design
Rivermark developer JannVanDam reported preliminary results from 112 social and marketing design requests evaluated through more than 4,700 blind pairwise human judgments. He said DeepSeek v4.1 Flash 1017 and GLM 5.3 Flash, using Rivermark, matched or beat plain Claude Code with Sonnet 5.5 and /design, while Sonnet 5.5 achieved the strongest result with Rivermark. The design-specific harness has been tuned for some models used in production, though not for the benchmark test cases. The author is validating the findings with additional judges.
r/LocalLLaMA · 🔥 0 - Artificial Analysis overhauls Intelligence Index with private evaluations
Artificial Analysis launched Intelligence Index v5, with 60% of its evaluations now based on private test sets or held-out answers. The update adds FrontierCode 2.0 and Terminal-Bench-Science, upgrades three existing benchmarks, and introduces Progress Index v1 to track model capability over time; Claude Opus 5.5 leads the initial v5 ranking with 58 points.
cognition (X) · 🔥 0 - GPT-6.1 Terra writing evaluation favors lower cost
A Towards AI evaluation tested GPT-6.1 Terra on 10 YouTube script-writing tasks, generating five drafts per task and scoring them blindly out of 100 with four LLM judges. GPT-6.1 Terra ranked 23rd overall in an evaluation of 84 models, while GPT-5.6 Terra ranked 15th. Its low-effort setting achieved overlapping Elo scores for less than half the cost of GPT-5.6 Terra at medium effort. On maximum effort, GPT-6.1 Terra used about 9,500 thinking tokens per draft and cost roughly three times as much without improving the writing score. Only one of the four judges, OpenAI's GPT-6.1 Sol, clearly preferred GPT-6.1 Terra; the other three scored it lower.
r/OpenAI · 🔥 0 - OpenAI and Anthropic Cut LLM Prices by Up to 80%
Max Woolf’s analysis charts how OpenAI and Anthropic reduced API prices for major models between June and September, including an 80% cut for GPT-5.6 Luna and lower prices for GPT-6.1 Sol and Claude Opus 5.5. The reports say competition from lower-cost open-weight models, including DeepSeek and Kimi offerings, created pricing pressure, while improvements in inference efficiency may also have enabled lower prices. Price comparisons in the analysis cover input, output and cache-read charges per million tokens.
Hacker News · AI（100+ 分） · 🔥 0 - DeepSeek V4.1 Flash 1017 debuts as open-weight value leader
Latent Space's AINews roundup reports that DeepSeek V4.1 Flash 1017 is a 552B-total, 16B-active MoE model with a 1M-token context window under an MIT license, priced unchanged at $0.30/$1.20 per million input/output tokens at peak, with off-peak pricing half that. Artificial Analysis scores it 45 on its Intelligence Index, up six points and tied with GLM-5.3 for second among open-weight models, while noting MiMo-V2.6-Pro and GPT-6.1 Sol (medium) score higher at lower cost per task. The model is available through DeepSeek's API, Fireworks, Baseten, DeepInfra, Cloudflare and Together, and ranks first among open models in Code Arena WebDev at No. 12 overall with 1,670 points; Together says it was trained with self-feedback RL, and release figures cite Terminal-Bench 4.0 rising from 31.2 to 42.3.
Latent Space · 🔥 0
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