I have a very extensive fleet/orchestrator setup with a custom harness and i run essentially every single model from all the providers so whenever a new one comes out I have my own tests I like to run them through to see how they will stack up with my work , so I ran Haiku 5.5 vs the other small fast models I use (Luna vs DeepSeek Flash vs Gemini 3.8 Flash Through 86 questions across 11 categories: coding, bug fixes, code review, SQL, pulling data out of messy text, math, long docs, tool calling, customer explanations (English + Spanish), and safety. All made-up data that matches my typical work. I did 3 runs each. Graded automatically where possible; the open-ended stuff was graded blind by Opus. Overall scores out of 100: - DeepSeek Flash 86.6 - Luna 85.2 - Gemini 3.8 Flash 85.1 - Haiku 5.5 84.7 So quality is a wash. The difference is what each one is good at: - Haiku 5.5: fastest of the bunch (3.5s median). Near perfect on code, bug fixes, SQL and math. Weak at tool calling and safety. Great workhorse for high-volume stuff. - DeepSeek Flash: best overall by a hair, perfect on long documents. Slow worst case though, and a few times it thought itself into a hole and returned nothing. - Luna: cheapest of the paid APIs, solid everywhere, weakest on long docs. - Gemini 3.8 Flash: way better at tool calling than the rest (81 vs ~50), but slowest by far (11s median) and worst on safety. Caveats : it's a small test with synthetic data, so a 2 point gap is noise. Haiku ran through a subscription so its cost shows as $0. Whole thing cost about $4 in API fees. Hope the report below helps some people! Full report here - https://claude.ai/artifact/SEHMk78LcN3QNkpLQ6wLLV https://preview.redd.it/re1wxd3bc4uh1.png?width=1456&format=png&auto=webp&s=ab551328cd067e14c7d83a3c52875955a267597c https://preview.redd.it/dp943oxdc4uh1.png?width=1456&format=png&auto=webp&s=cb181e76ec95b11857d5c762f6423e021a1af0d9 https://preview.redd.it/p3asmxekc4uh1.png?width=1456&format=png&auto=webp&s=5bfbaa67d3fd0326d9cdc0fc0a81addee07827bb submitted by /u/NiagaraPeloton to r/ClaudeAI [link] [comments]
First-party 97%Usable today 93%Measured result 90%
Abstract
Claude Haiku 5.5 is the cheapest, fastest, and most capable small model we’ve ever released. On average, it costs around 75% less to run than Claude Haiku 4.5. Haiku 5.5 is designed for high-volume, cost-sensitive tasks. It reliably handles quick, repetitive work like summaries and classification, and pairs well with Claude Opus 5.5 and Sonnet 5.5 as a sub-agent on coding work. It’s also fast enough for live customer support and browser use. It’s a significant step up over Haiku 4.5 across coding, computer use, and knowledge work. It’s also our first Haiku model with an adjustable effort setting, so you can decide whether to optimize for cost or intelligence on each task. Per token, it costs 90% less than Haiku 4.5 on tasks under 100,000 tokens, which make up around 90% of requests to our previous Haiku model. On longer tasks, it’s half the price. Haiku 5.5 is available now on all platforms, including Amazon Web Services, Google Cloud, and Microsoft Azure. We’re also halving the price of cache reads on Claude Sonnet 5.5, which makes it around 20% cheaper to run on most long-running work. Read more: https://www.anthropic.com/claude-haiku-5-5 submitted by /u/ClaudeOfficial to r/ClaudeAI [link] [comments]
Since i saw a post for native support in comfyui for Hunyuan image 3 i got really exited to try it out locally. On Strix Halo (Ryzen ai max 395+) i'm getting around 300 seconds per generation while on rtx 3090 it gets around 60 to 70 seconds for 1 megapixel (crashes if i go above not because of memory issues but something with how it's set up ? ) Anyway, i really enjoy the outputs of this model and if we had it working in comfyui 8 month ago when it released it would've dominated this sub :D **the editing capabilities can be a bit better than qwen2,1 (when it works + it's a 4bit quant, details are a mess) **i'll do a krea2 and qwen2.1 comparison with the same prompts, maybe i'll have them here on reddit *** WORKFLOW LINK: https://pastebin.com/7sZEbSMc ***Custom nodes used: https://github.com/PedroMarinhoDev/ComfyUI-HunyuanImage3 ***post that thought me about this: https://www.reddit.com/r/StableDiffusion/comments/1wzclz5/hunyuanimage_30_80b_running_natively_in_comfyui/ submitted by /u/Skystunt to r/StableDiffusion [link] [comments]
haiku 5.5 just dropped, so I really wanted to see how it stacks up against its direct competitor, which is gpt-6 luna, chose the infamous voxel pagoda test. what I really wanted to see is if anthropic managed to make at least their cheapest model not be a token devourer(spoiler: they didn't) and make an actually cheap model to use model effort tokens in (cached) tokens out API-equivalent cost Claude Haiku 5.5 xhigh 268.8M (262.7M) 4.46M $24.96 GPT-6 Luna xhigh 64.5M (61.1M) 1.18M $1.96 haiku is definitely another token goblin, but the real reason it's that much more expensive is that it's API price quintuples after 100k context, so every request over 100k tokens is billed 5x and in an agent session almost every turn is over 100k. for luna that occurs at 272k context, but with the default config it auto-compacts at that limit anyway setup: both models via their respective subs in atomic.chat submitted by /u/Fun-Meaning-6474 to r/ClaudeAI [link] [comments]
I don't think many people know about this yet, so I wanted to share it. VEDA Sparse Attention is now available as a ComfyUI custom node for MiniMax H3. I tested it today on my setup: RTX 4090 Laptop 16GB 32GB RAM, 4-step LoRA, 15 second video, 1344x768 Without VEDA: 8:05 With VEDA at 90% sparsity: 4:32 Same workflow, same LoRA, same settings. The only change was enabling VEDA. I couldn't see any quality loss in the result. VEDA is not a LoRA. It uses a learned predictor to estimate which attention tiles are important and only computes the relevant subset instead of the full attention map. The current predictor works with T2VA, FL2VA and R2VA, and despite the 8NFE name it is not limited to 8 steps. Installation is simple. Custom node: https://github.com/veda-sparse/Veda-on-ComfyUI Predictor: https://huggingface.co/Veda-Sparse/Minimax-H3-T2VA-Veda-8NFE-600Step-Preview Put the predictor here: ComfyUI/models/veda/ Then add: Veda Sparse Attention (MiniMax H3) on the MODEL line after your model / LoRA loader and before the guider or sampler. I tried different sparsity values, but 90% is the one that works properly for me, so I'm keeping the default trained value. On my setup this made a pretty big difference, especially considering I couldn't see any visual quality loss. I'm adding a 15 second example below. Would be interesting to see what results other people get on different GPUs. submitted by /u/robomar_ai_art to r/StableDiffusion [link] [comments]
Measured result 96%Usable today 94%First-party 58%
Abstract
A while ago I posted the first version of Ponytail here. It's an open-source skill that makes coding agents behave like the senior dev who replaces fifty lines with one. Since then I rewrote it from scratch. New rules, a new review, a new audit. Getting there took 5,900+ benchmarked Claude Code sessions, 3,650 of them testing drafts of the new rules against the old ones. What changed - The rules. Rewritten and about half as long. The goal moved from "the smallest code" to "the smallest complete change": before it writes, the agent maps everything the change must reach (callers, tests, config) and what it could break. Every reply ends with what it skipped or did not check, and any risk you should know. - /ponytail-review. It used to look only for code to cut. Now it reviews like the dev who gets paged when it breaks: bugs, security, real load, missing tests, slow paths, and what to cut. It reads the code around the change, not just the diff. Every finding says what the code does, what goes wrong, how to fix it, and what happens if you skip it. - /ponytail-audit. It used to list what to delete. Now it maps the whole repo first, ranks what it finds, and tells you what to fix first. Numbers , same agent with and without the skill (Claude Code, Opus 5.5, 39 tasks including a real FastAPI + React repo, 5 runs each): - code: -53% (previous version: -48%) - time: -41% (previous version: -38%) - cost: -26% (previous version: -16%) - risky logic that ships with a test: 98% (without the skill: 68%) - injected bugs caught by the agent's own tests: 66% (without the skill: 46%) Repo (MIT): https://github.com/DietrichGebert/ponytail Website: https://ponytail.dev If you already use it, update the plugin. I'd like to hear where it gets things wrong. submitted by /u/IT_WAS_ME_DIO__ to r/ClaudeAI [link] [comments]
Z-Image Turbo LoRA training in Fizgig, with a new training adapter (free, works in any trainer) I've added Z-Image Turbo to Fizgig , my free, open-source LoRA trainer and workbench. It trains LoRAs, LoKR, sliders and full fine-tunes, and every workbench tab works with it (Repair Studio, LoRA the Explorer, LoRA Royale, Profiler, Extract). Turbo has always been awkward to train: a LoRA undoes the distillation and the pictures go soft. I built a new training adapter for it. It sits frozen under your LoRA while it trains and is never in the file you save. In my tests it keeps Turbo's look much better than other adapters, especially in hard lighting . LoRAs train from 12 GB cards, fine-tunes from 16 GB LoRAs load in ComfyUI with the normal loader, at 8 steps and CFG 1 The adapter is free on Hugging Face and works in other trainers too Links Fizgig: github.com/shootthesound/Fizgig Adapter: Fizgig Z-Image Turbo Training Adapter Release notes: v7.1.0 Reference photo of me on my github profile pic for comparing to images in this post. Credit where it's due: Ostris built the first Z-Image Turbo training adapter within days of the model coming out, and it's what made Turbo trainable for most of us. He was upfront about it too, calling it "really just a hack" for shorter runs, made by training a LoRA on Turbo's own renders. I've had the advantage of time, and of being able to put together a large set of real photographs from my own work to train on alongside Turbo's renders. That's most of why this one holds up better on longer runs. It builds on his idea rather than replacing his work. submitted by /u/shootthesound to r/StableDiffusion [link] [comments]
What's Changed server: proxy cloud usage and balance APIs by @drifkin in #18829 llama: fix clef head reads past 2GiB on windows by @Gigrise in #18777 cmd: remove account step from CLI onboarding by @hoyyeva in #18826 manifest: avoid symlinks on Windows by @dhiltgen in #18852 docs: fix 6 dead links in README community integrations list by @aniketkrs in #18814 docs: fix broken download links in app README by @chenlichao in #18233 mlx: drop carried metal residency patch now that it is upstream by @dhiltgen in #18854 New Contributors @Gigrise made their first contribution in #18777 @aniketkrs made their first contribution in #18814 @chenlichao made their first contribution in #18233 Full Changelog : v0.40.0...v0.40.1-rc0
Patch release v5.14.1 This patch solves a few issues which appeared when integrating Inkling model, most notably an issue affecting models using EncoderDecoderCache during assisted generation. It also fixes an issue that could appear during prefill with StaticCache and sdpa without padding for Inkling which uses a position_bias. It contains the following commits: Fix sdpa prefill with position_bias ( #47359 ) by @Cyrilvallez Fix assisted decoding for models with EncoderDecoder cache & OlmoHybrid ( #47361 ) by @Cyrilvallez [FP8] Bump kernels version ( #47344 ) by @vasqu Fix deepgemm on multiple devices ( #47323 ) by @IlyasMoutawwakil
Release: llm-openai-decisions 0.1a0 OpenAI released their new Jev-style Decisions API , as previously announced at last week's DevDay. Since I already have an llm-typesafe plugin for talking to Jev, I had GPT-6 Astra read the new OpenAI API documentation and build an llm-openai-decisions plugin inspired by llm-typesafe . Unlike Jev, the new gpt-6-luna decision model supports image input in addition to text. Both models charge for input it and not for output: OpenAI's is 10 cents per million input tokens, Jev's is 4.2 cents per million. Otherwise the API shape is very similar to Jev, at least conceptually. Jev supports three question types for yes/no, choices, or scores. OpenAI Decisions supports the same three types. Install the plugin like this: llm install llm-openai-decisions Here's an example query against an image attachment: llm -m openai-decisions/gpt-6-luna \ -a https://static.simonwillison.net/static/2025/two-pelicans.jpg \ -s ' Does this image contain any mammals? ' And example output: { "type" : " predicate " , "name" : " evaluation " , "probability" : 0.0 } Consult the README for full details of how to run the other types of questions. Tags: openai , llm , coding-agents , jev
model : add LiquidAI/d1-omni-600M decision model ( #30114 ) model : add LiquidAI/d1-omni-600M decision model Assisted-by: Claude Opus 5.5 mtmd : keep conformer GLU sigmoid on CUDA Assisted-by: Claude Opus 5.5 server : take d1omni audio through images and input_audio, scope memory-less lfm2 to non-causal Assisted-by: Claude Opus 5.5 common : rename decision type d1omni to lfm2-d1-omni, server : make images an alias of files Assisted-by: Claude Opus 5.5 Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53804555 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI
Watch the video above for a brief summary of the two methods, both possible using my OBVPM Timeline Workflow, which you can get together with the custom node pack here: https://github.com/chanon/comfyui-obvpm-timeline/ And to watch the example video at HD quality you can watch the full YouTube tutorial video: https://www.youtube.com/watch?v=GiJxlWOooyo In the YouTube video I show how both methods are done, including critical tips and tricks and lessons learned to get the right results. With the bridging method, there is practically no limit to how long these clips can be (well maybe except the fact that there might be a VRAM limit to how long an upscaled clip can be). The second method clip above is 1 minute 40 seconds. About the Workflow So if you've never seen my workflow, it is a workflow with a "timeline" node that lets you put clips that you've generated on, and then you can extend them using motion context (latent masks). The workflow automatically saves and handles the saved latent files for you so you don't have to manage them or pick them manually. And it also saves the conditioning which includes all the reference images etc. into a file that is used when upscaling. For more info, here's the original Reddit post about it , which links the original YouTube tutorial video about it. submitted by /u/obvpm to r/StableDiffusion [link] [comments]
Usable today 82%Measured result 57%First-party 43%
Abstract
I am so happy that someone bought my app license. I was so nervous when I created the app, trying to fix everything and validate with bunch of friends. Approached random strangers on discord asking them to try my app, some did, some ignored. But the feedback was real good. Its been exactly 7 days since my app has launched, and the stats are - 3 licenses sold, 12 downloads and 212 website views. Genuinely happy with this! Here's the app - https://tuningmac.com/ (will remove if not allowed to share) submitted by /u/BowlPulp to r/SaaS [link] [comments]