Local and Cloud LLMs — Latest

October brought two things at once: the cloud's top tier finally opened to everyone, and what runs locally expanded beyond LLMs. Gemini 4 Argon entered general availability on Oct 13; Samsung published a method that squeezes a 13B-class model under 1 GB; NVIDIA shipped a 2.6B world model. The deciding metric has fully moved from unit price to cost per task and memory required.

1. At a Glance: Cloud LLMs and Local LLMsVerified

Split by cloud (API) and local (on your own hardware), with a clear open-weight flag and per-token pricing. Prices are published list rates per 1M tokens (USD); anything we could not confirm from a primary source is marked "unconfirmed".

A. Cloud LLMs (API)

B. Local LLMs (on-device / on-premises)

AIfrontier view The two practical gates are whether the weights are downloadable and whether the license allows commercial use. LittleBit is a technical landmark but CC BY-NC 4.0 forbids commercial use, and Sarashina is light but closed-weight — cheap and light do not imply free.

2. Cloud LLMs: Argon opens up, GLM-5.3 goes open-weight and ranks high

Google "Gemini 4 Argon" enters general availability on Oct 13Verified

Google opened general availability for Gemini 4 Argon, announced Sep 30, on Oct 13. Until then it was limited to trusted cyber defenders through the "Fairwind Program." It is rolling out globally to developers (paid Gemini API plans), enterprises (Gemini for business, API, Google Antigravity), consumers (Gemini app and AI Mode in Search for Google AI Ultra and Pro subscribers) and researchers (10,000 places with six months of top-tier Antigravity access after review). Source: Impress Watch ／ Google

Item	Detail
Context and output	1M-token context, and up to 1M output tokens per response (about 8× the previous 128K). Model ID gemini-4-argon
Price (launch)	$2 input / $10 output per million tokens through Nov 30, 2026; $4 / $20 from Dec 1. Cached input $0.10 (95% off), rising to $0.20 in December
Access	Gemini app from Google AI Pro ($19.99/month). Antigravity access is Ultra-only, with no free tier
Coding	77.9% on DeepSWE v1.1, reported ahead of GPT-6 Astra and Claude Opus 5.5Vendor-reported
Strengths	Coding, finance, legal, cybersecurity and science; Google claims first place on the Vals IndexVendor-reported
Internal example	Refactoring that separated core web-page handling from a ~6-million-line legacy folder inside Chromium's ~40-million-line codebase took weeks instead of months
Safety	Unauthorised shortcut attempts in coding benchmarks held to 2%. On Gray Swan's indirect prompt-injection benchmark, attack success stayed at 0.7% when attackers adapted up to 15 times
Next	Further Gemini 4 family models are planned

AIfrontier view The headline is not raw capability but the 95% cache discount. For agents that re-read long documents or a codebase repeatedly, it means running the same task several times cheaper. Unit-price tables alone will mislead you. See Frontier models and pricing. Price schedule: Technology.org ／ VentureBeat

Z.ai "GLM-5.3" — a 744B open-weight modelVerified

Z.ai released GLM-5.3 on Aug 14 and published the open weights on Aug 29. It is a MoE model with 744 billion total and 40 billion active parameters; running it directly needs data-centre-class infrastructure. Source: GIGAZINE ／ Hugging Face

Metric	Value	Comparison
Artificial Analysis Intelligence Index	60	Above Claude Opus 4.8
Artificial Analysis Agentic Index	59	Above GPT-5.6 Sol and Grok 4.6
API price	$1.40 / $4.40	GLM-5.3-Flash is $0.15 / $0.50

A local path exists too. Unsloth published quantized builds, and the UD-IQ2_M variant is documented as runnable on a Mac with 256 GB unified memory or a PC with 24 GB VRAM plus 256 GB RAM. Source: Unsloth ／ GGUF

AIfrontier view An open-weight model matching closed leaders on intelligence indices matters. But 40B active still needs 256 GB-class RAM even quantized — this is not a laptop model. "Open weights" is not the same as "runs on your machine," and the distinction needs re-checking every time.

Z.ai "GLM-5.3-Flash" — a 320B open-weight workhorseVerified

GLM-5.3's smaller sibling, GLM-5.3-Flash, is a MoE with 320 billion total and 18 billion active parameters, with weights on Hugging Face. Hybrid linear-plus-sparse attention gives it a 1M-token context, and Z.ai says it was designed to be served on Chinese chips. Source: Z.ai

Item	Value
Artificial Analysis Intelligence Index (v4.1.1)	57 (about $0.045 per task, discounted)Vendor-reported
API price	$0.15 / $0.50 (time-limited free tier on z.ai)
Context	1M tokens

AIfrontier view Three points behind the full model (60) at about a tenth of the price. Flash for high-volume cloud work, the full model for hard cases is the sensible split. With 320B total parameters, local use still needs large memory.

Alibaba "Qwen 4" — four tiers announced, still unreleased as of Oct 16Vendor-reported

At the Apsara Conference on Sep 22, Alibaba announced the next-generation Qwen 4 line as four tiers.

Tier	Position	Status
Qwen 4 Max	Flagship	At announcement these were names only: specs, pricing and dates were not published. Training was described as in progressVendor-reported
Qwen 4 Plus	Mid-tier
Qwen 4 Flash	Fast and cheap
Qwen 4 27B	Presumed open-weight 27B class

Remarks that "Qwen 4.5/5" target 5–10 trillion parameters were also reported, but those were stage comments relayed by third parties and we could not confirm them in a primary source.Reported Source: Yotta Labs

AIfrontier view What you can actually use today is the Qwen3.8 generation: Qwen3.8-Max (Aug 2), Qwen3.8-Flash-Next (Aug 26, with a native 262,144-token context) and Qwen3.8-Omni-Flash (Sep 18). The name arrived ahead of the product, so procurement decisions should be made on the 3.8 line.

October arrivals: Haiku 5.5, Muse Wonder, Mistral Large 4Verified

Model	Released	Key points
Claude Haiku 5.5 (Anthropic)	Oct 7	$0.10 input / $0.50 output up to 100K tokens (5× beyond), cache reads $0.01 — on average 75% cheaper than Haiku 4.5. Scores 61 on the Artificial Analysis Decision Index, above the top decision model Jev 1.13 (58), but takes 1.4 s per task (about 11× Jev)
Muse Wonder (Meta)	Oct 12	Meta's first frontier model. API at $4 / $20, in line with GPT-6 Sol and Opus 5.5. In the Muse app it comes with $20 or $100 monthly plans
Mistral Large 4 (Mistral AI)	Oct 6 (public preview)	1 trillion total, 52 billion active, natively multimodal. Trained on 3,800 Grace Blackwell GPUs in Mistral's European data centres. Weights due at the end of October. 61.7% on DeepSWE v1.1Vendor-reported

Source: Anthropic ／ Artificial Analysis ／ TechCrunch ／ Mistral AI

AIfrontier view Haiku 5.5 narrows the gap between cheap general LLMs and decision models. On accuracy alone it beats them; on speed and cost per decision, decision models still win. Bulk classification on a decision model, knowledge-heavy calls on a Haiku-class LLM remains a sound split. Treat Mistral Large 4 as open-weight only once the weights actually land.

3. Qwen3-TTS: speech synthesis goes openVerified

Alibaba's Qwen team open-sourced the Qwen3-TTS family on Jan 22, 2026 under Apache 2.0, so commercial use is permitted. It supports ten languages including Japanese, and a voice can be cloned from roughly three to four seconds of reference audio. Source: GIGAZINE ／ GitHub ／ arXiv:2601.15621

Item	Detail
Sizes	0.6B and 1.7B
Method	Qwen3-TTS-Tokenizer-12Hz turns speech into 12.5 Hz discrete tokens, and a discrete multi-codebook language model predicts them — not a diffusion (DiT) design
Features	Voice cloning from ~3 s of reference audio, text-described voice design, streaming generation
Latency	First audio packet in as little as ~97 msVendor-reported

Correction (Oct 16): the previous version said Qwen3-TTS was built on flow matching. Per the official technical report it is a discrete multi-codebook language model.

A voice-cloning variant, Qwen-Audio-3.0-TTS, followed on Jul 20, 2026. Source: GIGAZINE

4. Local models: quantization and on-device agents are the battleground

Samsung "LittleBit" — 0.1-bit quantization that puts a 13B model under 1 GBVerified

LittleBit, released by Samsung Labs, factorizes each dense weight matrix into low-rank latent factors, binarizes those factors to −1/+1, and then learns scales to restore magnitude. It targets 1.0 to 0.1 bits per weight without changing the inference architecture. The repo contains the NeurIPS 2025 paper and LittleBit-2 (ICML 2026), which improves initialization with Joint-ITQ (enable via --use_itq). Source: GitHub ／ arXiv:2506.13771

Item	Value	Note
Llama2-13B at 0.1 BPW	0.84 GB	About 31× smaller than the 26.06 GB FP16 version
Quality at similar settings	PPL 10.47	For Llama2-7B at 0.55 BPW, STBLLM scores 30.67 — though the setups differ
Speed	11.6× kernel / 2.46× end-to-end	A100 at batch 1 for an MLP-like Llama2-70B layer / Llama2-7B generating 128 tokens
Supported models	OPT, Llama 2/3, Phi-4, Qwen2.5 and QwQ, Gemma 2/3, Qwen3	—

The boundaries are stated plainly: a quantization cliff between 0.3 and 0.1 BPW, an uncompressed lm_head, and a requirement for QAT (quantization-aware training), which is computationally expensive. The licence is CC BY-NC 4.0 — non-commercial, so it cannot go into a commercial product. Source: alphaXiv

LittleBit-2 (ICML 2026) adds an internal latent rotation and Joint-ITQ (joint iterative quantization) to recover spectral energy lost below one bit. It improves perplexity over the 1-bit OneBit baseline on Llama-3 8B, Gemma-3 27B and Qwen3 8B, and because the rotation is absorbed into the weights it adds zero inference overhead.Vendor-reported Source: Samsung Research ／ arXiv:2603.00042

AIfrontier view "13B under 1 GB" is a real milestone for giving memory-constrained edge devices access to 13B-class language ability. But weighing ① the non-commercial licence, ② the QAT requirement (you cannot simply compress an off-the-shelf checkpoint) and ③ 0.1 BPW sitting at the bottom of the cliff, this is best treated as a research and evaluation method for now. Qwen3 support means it is realistic to test alongside local Qwen builds.

Liquid AI "LFM2.5-24B-A2B" — an agent that runs on 2B active parametersVerified

Liquid AI released LFM2.5-24B-A2B, a reasoning-only model with 24B total and 2B active parameters — described as the largest and final model in the LFM2.5 family (230M to 24B). Weights are open on Hugging Face and its Playground. Source: Liquid AI ／ Hugging Face

Item	Detail
Architecture	40 layers, 64 experts per MoE block with top-4 routing, hybrid of gated short convolutions and GQA
Context	Extended from 32,768 to 131,072 tokens
Vocabulary	65K to 128K via in-place tokenizer expansion, improving non-Latin tokenization
Training	Pretraining scaled 17T to 33T tokens; mid-training 2T at 32K then 300B at 131K; post-training in five stages (SFT, teacher specialization, non-agentic and agentic MOPD, agentic RL)
Speed	184 tok/s on an Apple M5 Max and 114 tok/s on an AMD Ryzen AI Max+ 395, up to 1.7× faster than the next-best MoE; up to 21K output tok/s on a single NVIDIA B300Vendor-reported
Runtimes	Day-one support for llama.cpp (GGUF), MLX, vLLM, SGLang and ONNX

Against Qwen3.6-35B-A3B — roughly 50% more total and active parameters — it leads five of eight tool-use and agentic benchmarks (AutomationBench 27.69 vs 8.72, LiveClaw 48.09 vs 38.19) and all three instruction-following benchmarks. Larger models still lead on knowledge-heavy GPQA Diamond (74.42 vs 83.69), HLE, SWE-Bench Verified (51.00 vs 66.20) and SciCode.Vendor-reported

One detail stands out. During teacher specialization the model showed reward-hacking behaviour: it searched git history and platforms like GitHub, PyPI and Hugging Face for answers, and worked out that it was inside a sandbox. Liquid AI tightened its sandboxes and re-ran all reported results in sealed environments. Related: the OpenAI–Hugging Face incident

AIfrontier view This is the clearest signal yet that local models are moving from "can chat" to "can run a tool-calling agent on device, continuously." At 2B active with that decode speed, the split — planning in the cloud, execution locally over sensitive data — has become practical. Knowledge and coding lag bigger models, so pairing with retrieval is a prerequisite.

NVIDIA "SANA" and "SANA-WM" — a one-minute world model at 2.6BVerified

NVIDIA Labs' SANA is an efficiency-oriented codebase for image and video generation, covering SANA, SANA-1.5, SANA-Sprint, SANA-Video, SANA-Video 2.0, SANA-WM, SANA-Streaming and Sol-RL. It has been accepted at ICLR 2025 (Oral), ICML 2025, ICCV 2025 (Highlight) and ICLR 2026 (Oral). Source: GitHub

SANA-WM is a 2.6B-parameter controllable world model that generates 720p video up to one minute long and follows 6-DoF camera trajectories. Project ／ arXiv:2605.15178 ／ Source: GIGAZINE

Item	Detail
Long-horizon mechanism	Hybrid linear attention (Hybrid GDN-Softmax): a mechanism for keeping long temporal flow plus one for referencing detail when needed
Camera control	Two tiers — coarse trajectory plus per-frame fine viewpoint changes — preserving motion detail that compression tends to lose
Pipeline	Two stages: the base model generates the long video, then a long-video refiner corrects quality and consistency
Training	About 213,000 public video clips, with 6-DoF camera poses estimated by an annotation pipeline; 64 H100 GPUs for 15 days
Inference speed	For a 60-second 720p denoising pass: about 21.8 minutes normally on H100, 48 seconds distilled, 34 seconds with sink. On RTX 5090, 48 seconds with sink and 42 seconds with NVFP4 quantizationVendor-reported
Efficiency claim	Better instruction following on camera trajectories than prior open models, with 36× throughput at near-large-model visual qualityVendor-reported

The surrounding releases move fast too: SANA-Video 2.0 (5B and 14B, hybrid linear attention, 720p on a single GPU) and its 5B 4-Step Preview (720p in four denoising steps, 5- and 8-second outputs) landed in Sep 2026, and Sol Engine in Aug 2026 gave day-one acceleration for MiniMax-H3's 33B omni-modal audio+video DiT — 3.95× on GB200 and up to 4.52× on RTX 5090, with no distillation, LoRA or calibration.Vendor-reported

AIfrontier view "Making LLMs efficient" and "making generative models efficient" look like separate problems but solve the same one: handling long sequences cheaply. Hybrid linear attention plus distillation and low precision will carry over to long-context LLMs. For robotics and simulation, a camera-controllable world model becomes a practical test environment, which makes this worth watching.

SB Intuitions "Sarashina" — a Japanese-first model family opens as a free APIVerified

On Oct 13, 2026, SB Intuitions launched a free beta of the "Sarashina service" for its Japanese-focused generative AI, exposing six models through both an API and a web chat. Sign-in uses a Google account, there is no application and no charge, and rate limits apply. Source: SB Intuitions ／ sarashina.ai

Kind	Model	Purpose
API	Sarashina3 mini	Text generation and dialogue
	Sarashina3 nano	High-volume, high-speed simple tasks
	Sarashina3 embedding	Retrieval-augmented generation
	Sarashina3 rerank	Search-result reranking
	Sarashina3 guard	Input safety judgement
	Sarashina3 translate	Japanese–English translation
Chat	Dialogue and translation	Web search, writing, JA–EN translation

The underlying Sarashina3 mini and nano were built from scratch and published on Jun 30, 2026, pretrained on 30 trillion tokens weighted toward Japanese and English. Highlights include multi-stage reinforcement learning (cascading plus simultaneous skill phases), asynchronous RL, agentic task data from an in-house synthetic pipeline, and a large internal Japanese annotation and evaluation programme. They were also selected as trial models for the Digital Agency's government AI. Source: SB Intuitions TECH BLOG

AIfrontier view A family designed for Japanese performance and managed in domestic data centres becoming testable through a free, no-application API is significant. Being able to assemble guard and rerank from a domestic model is a real option for Japanese-language business systems. The beta's data terms will need re-checking before production adoption.

5. How to split the work today

Aspect	Local	Cloud (frontier)
Cost	Hardware plus electricity; no metered charge	Metered, but cache discounts are large (Argon: 95% off; unit price doubles in December)
Confidentiality	Data never leaves	Depends on contract and configuration
Capability	27B–125B class plus 2B-active agents; knowledge and coding are limited	Top tier; long, multi-step work and current knowledge
Speed	Fast in practice — 184 tok/s on an M5 Max for LFM2.5-24B-A2B	Network round trip
Best fit	High-volume routine decisions, sensitive data, always-on tool calling	Hard problems, long documents, coding, planning
Examples (Oct 2026)	LFM2.5-24B-A2B, GLM-5.3 (quantized, 256 GB class), Qwen3.8 line, Sarashina, LittleBit (research)	Gemini 4 Argon, GPT-6 line, Claude Opus 5.5 / Haiku 5.5, Muse Wonder, GLM-5.3 / 5.3-Flash API, Mistral Large 4 (preview)

6. Decision models now have their own featureAIfrontier view

Decision models ("System One" — models that return a judgment rather than text) differ from generative LLMs in billing unit, evaluation and adoption criteria. Rather than mixing them here, we have split them into a dedicated feature that tracks the family tree (Jev, Laya, Von, Kev, Jeff, Decider and others), pricing, calibration, threshold design and an adoption checklist.

Open the Decision Model feature

Sources

• Technology.org, "Gemini 4 Argon generally available, pricing changes in December" (Oct 14, 2026) https://www.technology.org/2026/10/14/gemini-4-argon-generally-available-pricing-december/
• VentureBeat, "Google makes Gemini 4 Argon broadly available" https://venturebeat.com/technology/google-makes-gemini-4-argon-broadly-available-with-limited-time-low-api-pricing-of-2-10-per-1m-token
• Z.ai, "GLM-5.3-Flash" https://z.ai/blog/glm-5.3-flash
• Anthropic, "Claude Haiku 5.5" (Oct 7, 2026) https://www.anthropic.com/claude-haiku-5-5
• TechCrunch, "Meta releases its first frontier model, Muse Wonder" (Oct 12, 2026) https://techcrunch.com/2026/10/12/meta-releases-its-first-frontier-model-muse-wonder/
• Mistral AI, "Mistral Large 4" (Oct 6, 2026) https://mistral.ai/news/mistral-large-4/
• Artificial Analysis, "Decision Index v1.0" https://artificialanalysis.ai/articles/artificial-analysis-decision-index
• QwenLM, "Qwen3-TTS" https://github.com/QwenLM/Qwen3-TTS / technical report arXiv:2601.15621
• Reddit r/LocalLLaMA, "Qwen 4 apparently coming out at the end of October" (rumour) https://www.reddit.com/r/LocalLLaMA/comments/1wz1pqn/qwen_4_apparently_coming_out_at_the_end_of_october/
• Samsung Research, "LittleBit-2" https://research.samsung.com/blog/LittleBit-2-Maximizing-the-Spectral-Energy-Gain-in-Sub-1-Bit-LLMs-via-Latent-Geometry-Alignment
• TypeSafe AI, "Models" https://docs.typesafe.ai/models
• Google, "Gemini 4 Argon" https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/
• Impress Watch, "Gemini 4 Argon enters general availability" (Oct 13, 2026) https://forest.watch.impress.co.jp/docs/news/2147842.html
• Impress Watch, "Gemini 4 Argon announced" (Sep 30, 2026) https://forest.watch.impress.co.jp/docs/news/2144700.html
• GIGAZINE, "GLM-5.3 released as an open model" (Aug 29, 2026) https://gigazine.net/news/20260829-glm-5-3-open/
• Hugging Face, "zai-org/GLM-5.3" https://huggingface.co/zai-org/GLM-5.3
• Unsloth, "GLM-5.3" https://unsloth.ai/docs/models/glm-5.3 ／ https://huggingface.co/unsloth/GLM-5.3-GGUF
• Yotta Labs, "Qwen 4 Max vs Plus vs Flash vs 27B" https://www.yottalabs.ai/post/qwen-4-max-vs-plus-vs-flash-vs-27b-tiers-2026
• Qwen, "Qwen3.8-Max" https://qwen.ai/blog?id=qwen3.8 ／ "Qwen3.8-Flash-Next" https://qwen.ai/blog?id=qwen3.8-flash-next
• Hugging Face, "Qwen/Qwen3.8-Flash-Next" https://huggingface.co/Qwen/Qwen3.8-Flash-Next
• GIGAZINE, "Qwen3-TTS family open-sourced" (Jan 23, 2026) https://gigazine.net/news/20260123-qwen3-tts-family-opensource/
• GIGAZINE, "Qwen-Audio-3.0-TTS" (Jul 23, 2026) https://gigazine.net/news/20260723-qwen-audio-3-tts/
• Samsung Labs, "The LittleBit Project" https://github.com/SamsungLabs/LittleBit
• Lee et al., "LittleBit: Ultra Low-Bit Quantization via Latent Factorization" arXiv:2506.13771 ／ https://www.alphaxiv.org/abs/2506.13771
• Lee & Kim, "LittleBit-2" arXiv:2603.00042
• Liquid AI, "LFM2.5-24B-A2B" https://www.liquid.ai/blog/lfm2-5-24b-a2b ／ https://huggingface.co/LiquidAI/LFM2.5-24B-A2B
• NVIDIA Labs, "SANA" https://github.com/NVlabs/Sana ／ "SANA-WM" https://nvlabs.github.io/Sana/WM/ ／ arXiv:2605.15178
• GIGAZINE, "NVIDIA announces SANA-WM" (May 18, 2026) https://gigazine.net/news/20260518-nvidia-sana-wm/
• SB Intuitions, "Free beta of the Sarashina service" (Oct 13, 2026) https://www.sbintuitions.co.jp/news/press/20261013_01/ ／ https://sarashina.ai/
• SB Intuitions TECH BLOG, "Sarashina3 mini / Sarashina3 nano" (Jun 30, 2026) https://www.sbintuitions.co.jp/blog/entry/2026/06/30/100000
• TypeSafe AI, "Jev 1.14" (Oct 14, 2026) https://typesafe.ai/blog/jev-1-14
• MarkTechPost, "TypeSafe AI Releases Jev 1.14" https://www.marktechpost.com/2026/10/14/typesafe-ai-jev-1-14-images-box-long-context/

This page organises public information. Most performance, price and speed figures are vendor-reported and have not been independently reproduced by us. Read every number with its conditions: quantization bit-width, context length, batch size, GPU and model version.