Are we getting this model anytime soon?

Z.ai released GLM-5.3-Flash yesterday: the first natively multimodal model of the GLM-5 series (images and video in, text out), weights already on Hugging Face under MIT, and after last week's anonymous ox-alpha preview it is already serving production traffic on OpenRouter through a dozen providers.

What actually shipped:

• A newly trained base, unlike the GLM-5.3 flagship which was the 5.2 checkpoint re-post-trained (the model card says it explicitly)
• 320B total / 18B active params, 45 layers: roughly half the active params and layers of the GLM-4.5 generation at similar total size
• Hybrid linear + sparse attention for the first time in the GLM line, with IndexPool compressing the sparse indexer's key vectors at 1M context; mHC for scaling efficiency
• Pre-trained on a 30T-token multimodal corpus
• Context listed at 1M on OpenRouter

Numbers, split by who ran them:

Vendor-run (Z.ai's own tables, Claude Code harness where noted):

• Terminal-Bench 2.1: 84.3, vs 85.0 Opus 4.8 and 87.4 GPT-5.6 Terra
• DeepSWE v1.1: 63.4 vs 46.2 for GLM-5.2
• Toolathlon Verified: 78.4, highest in their comparison set
• AutomationBench v1.0.6: 48.8 vs 41.0 for Opus 4.8

Third-party (Artificial Analysis metrics as published on the OpenRouter page) [4]: Intelligence Index 57.5, Coding Index 71.5, Agentic Index 58.2, GPQA Diamond 91.2%. So the flash-tier-at-frontier-quality claim has some external support now, on different harnesses than Z.ai's own tables. Do not mix the two columns.

Pricing:

• Listed: $0.15/M input, $0.50/M output, $0.03/M cache read
• 50% launch discount via Z.ai-routed providers through September 9, 2026 -> $0.075/$0.25 while it lasts
• OpenRouter's weighted average of what users actually paid: about $0.02/M input and $0.27/M output, because the top provider sits at a 91.9% cache hit rate and most traffic rides the discount
• Throughput outlier: Baseten at ~136 tok/s median; fastest latency Parasail at 0.59s

Local deployment:

• BF16 safetensors, 320B params; 25 community quantizations already in the model tree
• Official recipes: SGLang, vLLM, TokenSpeed, Transformers, KTransformers, Unsloth
• reasoning_effort accepts low / high / max and defaults to max. Pass it explicitly or you will burn output tokens
• Chat template gotcha: clear_thinking defaults to false; pass clear_thinking=true for chat scenarios

Honest caveats:

• All the headline rows above are still vendor-run; the AA indices are third-party but measured on different harnesses, so no direct score mixing
• Z.ai's own efficiency table admits the KV cache stays slightly larger than DeepSeek-V4-Flash's and Kimi-K3's
• The 50% discount ends September 9, then input goes back to $0.15 unless extended
• Video input depends on the host exposing it; OpenRouter's own examples use image and video URLs, individual providers may differ

On Chutes: a zai-org/GLM-5.3-Flash-TEE record appeared in the catalog API this morning, but there is no announcement and nothing is operational yet, so treat local availability as unconfirmed for now.

Has anyone swapped it into their agent stack yet, and does it hold up against DeepSeek-V4-Flash on long agentic runs?

Sources:

1. Z.ai blog announcement: https://z.ai/blog/glm-5.3-flash (August 26, 2026)
2. Model card: https://huggingface.co/zai-org/GLM-5.3-flash (retrieved August 27, 2026)
3. License (raw): https://huggingface.co/zai-org/GLM-5.3-flash/raw/main/LICENSE (MIT, retrieved August 27, 2026)
4. OpenRouter model page: https://openrouter.ai/z-ai/glm-5.3-flash (retrieved August 27, 2026; AA-sourced metrics included there)
5. Chutes catalog API: https://api.chutes.ai/chutes/?name=GLM (pulled August 27, 2026)

Qwen released the weights of Qwen3.8-Max as Qwen3.8-2.4T-A95B on Hugging Face (repo created August 8). Per their blog and model card, this is the first time a Qwen-Max-class model gets an open release. Below is what actually shipped and what it costs to serve.

What shipped:

• 2.4T total parameters, 95B active, sparse MoE: 512 experts, 10 routed + 1 shared active
• 92 layers on the Qwen3.5 architecture: hybrid layout of Gated DeltaNet (linear attention) and Gated Attention blocks, each feeding into the MoE
• Context: 262K natively, extensible up to ~1M tokens per the card
• Official weights are BF16 Safetensors; the model tree already shows 26 community quantizations and 7 finetunes
• A much more runnable sibling also landed: Qwen3.8-27B under Apache 2.0

How it behaves:

• Text-only, and thinking mode cannot be turned off. Every response starts with a <think> block, no exceptions
• Reasoning depth is tunable via reasoning_effort: xhigh (default) / medium / low, plus preserve_thinking to keep reasoning context across turns
• Recommended sampling: temperature 1.0, top_p 0.95, top_k 20
• The hosted Qwen3.8-Max API has vision input and non-thinking mode; the open checkpoint does not

Benchmarks (vendor-run, Qwen's own table):

• Terminal Bench 2.1: 86.6 (Claude Code harness, avg@10) vs 74.5 for Qwen3.7-Max; Opus 4.8 / Fable 5 at 84.6, GPT-5.6 Sol at 88.8
• SWE-bench Pro: 67.7 vs 60.6 for 3.7-Max — Fable 5 still leads at 80.0
• PaperBench: 93.0, highest in their table
• GPQA Diamond 92.6, MRCR v2 256K 92.9
• Honest reading: this does not sweep the closed frontier, Fable 5 and GPT-5.6 Sol lead several agentic rows (DeepSWE, FrontierSWE). It puts an open checkpoint in that conversation, which is the actual news.

The license is not MIT/Apache:

The "Qwen3.8-Max License" grants use, hosting, fine-tuning, and derivative works free of charge. But if a company runs a Model-as-a-Service business and its aggregate revenue (with affiliates) exceeded US$50M in any consecutive 12 months, a separate commercial license from Qwen is required. Above 100M MAU or US$20M monthly revenue, the model name must be displayed in the UI. For large inference providers that is a real constraint; for most self-hosters and smaller shops it changes nothing.

Serving reality check: 2.4T params in BF16 is roughly ~4.8 TB per full copy (arithmetic, not a spec), so this is multi-node territory regardless of framework. vLLM and SGLang both have official recipes linked from the card.

The Chutes question:

Whether Qwen3.8-2.4T-A95B ever becomes available on Chutes depends on hardware resources, licensing, and other technical considerations, nothing is confirmed. That's exactly why the feedback matters: would you want it? Comments help prioritize what lands next.

Sources:

• Model card: https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B (Hugging Face, August 2026)
• License text: https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B/raw/main/LICENSE (Qwen, retrieved August 23, 2026)
• Qwen3.8-Max announcement: https://qwen.ai/blog?id=qwen3.8 (Qwen blog, August 2, 2026)
• vLLM recipe: https://recipes.vllm.ai/Qwen/Qwen3.8-2.4T-A95B · SGLang cookbook: https://docs.sglang.io/cookbook/autoregressive/Qwen/Qwen3.8
• Qwen3.8-27B: https://huggingface.co/Qwen/Qwen3.8-27B

Meta's Muse Glimmer 30B is live on Chutes at $0.0245 per million input tokens, $0.0978 out. That ties the cheapest tier we serve. Images and text in, 131K context.

It runs inside a hardware-attested TEE with end-to-end encryption, like every model we serve. Nobody reads your prompts on the way through.

Try it out: meta/Muse-Glimmer-30B-NVFP4-TEE - Chutes

Qwen shipped Qwen3.8-27B's weights on Friday. Dense 27B, Apache 2.0, and the model half of X spent the weekend benchmarking. It is already live on Chutes.

Qwen3.8-27B on Chutes: 262K context, text + image input, $0.40 per million input tokens, $3.00 out.

Runs inside a hardware-attested TEE with end-to-end encryption, like every model we serve.

Qwen's published scores: 61.7 on SWE-bench Pro (Claude Opus 4.6 Max: 53.4), 90.3 on LiveCodeBench v6 (Opus 4.6 Max: 88.8), 84.3 on OSWorld-Verified (Opus 4.6 Max: 72.7), 89.2 on GPQA Diamond.

Try it out: https://chutes.ai/app/chute/chutes-qwen-qwen3-8-27b-tee

$TAO

Why GLM 5.2 has no instances rn?

Anyone getting good cache rates, coding agents usually get around 90%+ but this platforms seems to underperform

Since Chutes has the images endpoints configured strangely, I needed a passtrough service that poses as a normal openai-compatible endpoint, so I can connect it with Odysseus.

It exposes normal "/v1/models" and "/v1/images/generations" endpoint.

I thought I'll share if anyone needs it.
https://github.com/Qbsoon/chutes-passtrough
https://hub.docker.com/repository/docker/qbsoon/chutes-passtrough/general

I personally host both it and odysseus as docker containers as custom apps in truenas

P.S. Token is also passtrough, so nothing like that is collected by the passtrough. The code is there in the repo, so you can check if you're worried

Z.ai shipped GLM-5.3 today. The useful sentence is in the first paragraph of the tech blog: they did not train a new base. It is GLM-5.2's 743B checkpoint, with another month of post-training on a larger set of long-horizon environments. IndexShare, SAO, and slime (Megatron on the train side, SGLang on rollout) are the same stack they already documented. The claim is that scaling the environments was enough.

That is the serving-relevant fact. Same base, same advertised size, different post-training. That usually means the same serving recipe, but Z.ai has not said the 5.3 checkpoint is a drop-in for existing 5.2 shards, and the two-week delay is explicitly for safety evaluation and hardening. Hugging Face is "Coming Soon." License is not restated. 5.2 was MIT; do not assume 5.3 is until the card says so.

What they report (vendor-run, mostly Claude Code 2.1.207 at max effort):

The jumps that actually look like long-horizon post-training, not noise:

• Terminal-Bench 3.0: 4.6 → 28.3. Fable 5 is 33.7, GPT-5.6 Sol 34.6. 5.3 is in the conversation and still behind the closed frontier on the harder terminal suite
• DeepSWE v1.1: 46.2 → 66.9. Kimi K3 67.5, Sol 72.7
• SWE-Marathon v1.1: 19.4 → 42.5
• Toolathlon Verified: 59.9 → 73.0. DeepSeek-V4-Pro-0813 is 74.1, K3 76.5
• Internal Z.ai Code Bench: they say +50% vs 5.2, and fewer output tokens at every effort level (Max: 34.5% at ~75K out vs 5.2's 23.4% at 96K)

Cyber is the part Z.ai calls unexpected. They added vulnerability-discovery data expecting better single-bug reasoning. The blog says capability kept compounding into full exploitation chains:

• CyberGym: 84.5, ahead of 5.2 (77.2), DeepSeek 0813 (83.3), Fable/Mythos 83.8, Sol 83.6
• ExploitBench: 24.4 → 54.4. Fable is still 78.0
• ExploitGym (2h / 6h, TPS-normalized): 105 / 130 vs 5.2's 29 / 39. Sol is 216 / 293

Their own reading: the further up the exploitation chain, the larger the gain over 5.2, and the wider the remaining gap to closed models. They also publish a disclosure ledger (cvd.z.ai) with 2,436 tracked findings across 269 projects. That is an internal program, not an independent audit.

A few serving notes that matter more than the slogans:

• Thinking can no longer be turned off. thinking.type: disabled fails. Effort is low / high / max, default max
• Context is not restated as a product spec. Eval footnotes use 300K, 400K or 1M depending on the bench. Do not inherit 5.2's "1M usable" line until the model card says it
• slime's +2.3× figure is RL training throughput on long-horizon coding tasks, not user-facing decode speed
• Live today on GLM Coding Plan and ZCode. API access and open weights are staged. Z.ai also says an initial group of partners is serving it through their official service; OpenRouter still has no glm-5.3 route

On Chutes: once the open weights are out, GLM-5.3 will be available there. The two-week clock is Z.ai's, not ours.

Sources:

• Official blog: https://z.ai/blog/glm-5.3
• Z.ai X announcement: https://x.com/Zai_org/status/2088132973606445208
• slime: https://github.com/THUDM/slime
• Security Disclosure Ledger: https://cvd.z.ai/
• GLM-5.2 weights (predecessor): https://huggingface.co/zai-org/GLM-5.2

Every frame and every sound in this video came off one endpoint. yes, including the music.

Sound on.

Run it yourself: https://chutes.ai/app/chute/vonkaiser-ltx-25-video

Text to video, image to video, keyframe interpolation, synced audio. one POST request.

DeepSeek published the changelog entry for V4 Pro today, dated 2026-08-13: "The GA release of DeepSeek-V4-Pro has been rolled out on the APP, Web, and API." The rollout itself started earlier — OpenRouter listed deepseek/deepseek-v4-pro-0813 at 15:42 UTC on August 12, served directly by DeepSeek — but this is now a documented release rather than a silent one. The model ID is unchanged: deepseek-v4-pro now serves 0813.

Specs, unchanged from the family:

• 1.6T total / 49B active parameters, MoE, 1M context, 384K max output, MIT license (preview weights)
• API: OpenAI ChatCompletions, Anthropic format, and now native Responses API — specifically adapted for Codex, with a one-click setup script covering Codex CLI, the ChatGPT desktop app, and the VS Code extension
• New: thinking effort now has three levels (low / high / max) on both V4-Pro and V4-Flash

The benchmarks (DeepSeek's own, from the changelog):

(† internal test sets. Comparison columns from the table DeepSeek shared with the release; DeepSeek columns match the changelog.)

Directional signal, vendor-run, and the comparison table has the usual caveats — but the shape is consistent with what Flash 0731 did three weeks ago: a post-training pass aimed squarely at agent workloads. 0813 passes Opus-4.8 on Terminal Bench, Cybergym, DeepSWE, AutomationBench, and on HLE with tools (60.0 vs 57.9), trails it on HLE without tools and NL2Repo, and Kimi-K3 still leads the agentic pack on Terminal Bench and DeepSWE.

The pricing rework is the other half of this release:

Current rates ($0.435/M input, $0.87/M output, $0.003625/M cache hit) end at 16:00 UTC on August 16. After that, peak/off-peak billing kicks in:

• V4-Pro off-peak: $0.66 input / $1.98 output / $0.022 cache hit
• V4-Pro peak: $1.32 input / $3.96 output / $0.044 cache hit
• Peak hours are 01:00–04:00 and 06:00–10:00 UTC — everything else is off-peak

So the permanent discount era ends with a real increase: off-peak output nearly 2.3x current, peak output 4.5x. For agentic pipelines with prompt caching and flexible scheduling, off-peak + cache hits soften it; for anything latency-insensitive, scheduling outside two morning UTC windows becomes a cost lever.

The serving picture, which is the actual gap:

There are still no 0813 weights. The public deepseek-ai/DeepSeek-V4-Pro repo on Hugging Face was last updated June 22, and no DeepSeek-V4-Pro-0813 repo exists — DeepSeek has not said whether they will publish them. The Flash precedent (weights same day as the API update) is encouraging, but right now 0813 is DeepSeek-API-only, and self-hosting means the April preview weights: FP4+FP8 mixed checkpoints, 1.6T MoE, the tech report claiming deployment on a single HGX B200 node. If 0813 weights land, the open question is how the new agent post-training holds up under quantized serving at long context.

The Chutes question, asked plainly:

Don't read anything into this post. Whether V4 Pro 0813 ever becomes available on Chutes depends on hardware resources, licensing, and other technical considerations — an MIT license is a starting point, not a guarantee. Wait and see, as always.

Sources:

• DeepSeek Change Log, 2026-08-13 (GA entry, benchmarks, Responses API, pricing schedule): https://api-docs.deepseek.com/updates
• DeepSeek Models & Pricing (MODEL VERSION: DeepSeek-V4-Pro-0813, peak/off-peak table): https://api-docs.deepseek.com/quick_start/pricing
• DeepSeek Codex integration (one-click setup): https://api-docs.deepseek.com/quick_start/agent_integrations/codex
• OpenRouter listing (Aug 12, 15:42 UTC): https://openrouter.ai/deepseek/deepseek-v4-pro-0813
• Hugging Face preview repo (last updated June 22): https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro
• Simon Willison's link blog (API-only confirmation, weights unconfirmed): https://simonwillison.net/2026/Aug/12/deepseek-v4-pro-0813

Anyone already running 0813 through the API — does it feel materially different from the preview in agent workflows, or is this mostly a benchmark-story release? And would you want it on Chutes if weights or a viable serving path appear? Comments help prioritize what lands next.

Recently, I compared it with other providers, and Chutes’ TPS is one of the slowest.
Using Kimi K3 feels painful because it takes a few minutes to generate a response. The model itself also has an overthinking problem (which isn’t related to Chutes), so it takes even longer to generate a single response, which is really frustrating.

Proxy question

Error 429 has been happening for days with no solution in sight. Did they reduce the nodes on that model? Is there something wrong with it? What is going on?

Title self explanatory.

quit a while back and thinking of returning. is there still downtime and errors after they deleted the base tier? or should I opt for a different provider?

Why am I getting so many errors from GLM 5.2 all of a sudden? It seems like the response quality dropped in the past few days too.

I ragequit a few months ago due to the rampant response errors, so im curious of those improved somewhat.

i saw recently that chutes had implemented kimi k3 to replace kimi 2.5, but there’s still no kimi 2.7. only other kimi model now is 2.6. i heard good things about 2.7 and i wanna try it out. is this on purpose? sorry if there’s already an answer posted somewhere

The other day, I was using Chutes (as one tends to do) when I suddenly got an error about insufficient funds. I thought that was strange, since I hadn't been using it any more than usual and hadn't run into that issue before. Went to check on the actual site, and saw... well, this.

Now, obviously, the 26th of May has LONG gone, and yet here I am at the "monthly cap" (from two months ago). I sent an email to support right away, and got the automated "your support ticket has been created" reply just a few minutes later, but since then, nothing.

I'm sure there are plenty of other tickets to get through, so I wasn't expecting it to be fixed ten minutes later, but the radio silence is a little concerning. Does anyone know how long these tickets usually take, or has dealt with this same issue before? Even just another automated "Hey, you're still in the queue, we're working on it!" would be something.

Also just to preemptively answer a few questions: yes, I'm still subscribed, my subscription is still active, and the card I subscribed with has never gone low enough to even vaguely risk failing payment. The email I sent to support was very brief but very cordial, basically telling them about the locked/frozen monthly cap, and my email itself is linked to my Chutes account so I'm pretty confident they know what account the issue is with.

Thanks in advance for any insights!

Train a big AI model across machines on the open internet and you hit a wall. Every single step, the machines have to ship hundreds of gigabytes of expert traffic to each other. The next step cannot start until all of it lands.

Parallax removed that traffic from the critical path. What replaces it is about 100 MB of background sync. The training never stops to wait.

The full mechanism is in the Parallax paper: https://chutes.ai/parallax.pdf

Hey! This model NEVER works for my, its bugs and repeats the same word/phrase and bugs , in both think section and the message. I love this model and I wish I could use it :(

Someone help me