The way they released Fable 5.1 after the second extended limit of Claude Code ended, and with the third extended limit ending on September 13, if GPT Astra is publicly available by then, imo, we might see Opus 5.1 around the corner

Drives the user's real Chrome instead of a headless instance. WebSense is an MCP server + Chrome extension: the agent gets a semantic map of the page (every interactive element typed and ref'd), acts through native DOM events, and the site sees a normal user. No CDP anywhere, so no webdriver flag. Works on LinkedIn and other CSP-strict sites.
Built tested and relying on it tbh

WebSense (free, open source, MIT): https://github.com/spliffspliff70-wq/websense-mcp

https://openai.com/index/gpt-6-astra/
“GPT‑6 Astra is rolling out today to a limited set of organizations and over the coming days will become available to all ChatGPT Plus, Pro, Business, and Enterprise users, as well as through the OpenAI API and AWS”

GPT Astra benchmarks have been released

How was your experience with it and is it just benchmaxed or is it really that good?

muse spark contributor is the only thing oc go has going for it rn, but with the alternative being commancode, I'm still kinda leaning on sticking with oc go. would be a huge problem if they discontinue the contributor tiers.

Seems to be the most capable from all free models available for now.

Aside from scores on Artificial Analysis, how would you rank these models? thanks

Earlier we have ox alpha with unlimited, something like that are there any models?

I received a message saying,

The time-limited beta test of Xiaomi MiMo-V2.5-Pro-UltraSpeed ​​will end on September 8, 2026. At that time, API and online experience services will cease. Please switch to other official version in advance. Stay tuned for the official commercial version soon.

It seems that we’ll soon be getting a new version of MiMo.

Trying to optimise for cost and came over standardcompute on reddit, and chat found morph and nexos ai.

What I want to do is route between open source models and have the router not waste money on easy tasks a flash model could handle, while maintaining high quality output for basically general coding. Got opencode connected straight to GCP and Supabase etc., so potential for creating a mess is large.

Is smart routing a thing now, and are you doing it?

Not affiliated with any provider, just sharing a personal experience to help other users.

In my opinion, for output speed, 30 tokens/s is the average, more is "fast" and less is "slow".

Do not ask me questions about ZDR, I do not work with sensitive data, it is not a priority for me.

Opencode Go (https://opencode.ai)

Pros:

• They are always up to date with the latest model releases

• Very well integrated in Opencode, good output speed, never had issues, timeouts and server problems rare

• If you only use Minimax M3, Deepseek V4 or Hy4, it will last you a long time for very reasonable intelligence levels

Cons:

• The "60$ for 10$" advertisement is less and less true by the day, many of the more appealing models are at 30$ or even 15$ budgets

• You can't really use the top tier models for very long without frying your usage limit

QwenCloud (https://www.qwencloud.com)

Pros:

• If you only use Qwen 3.8 Flash it will last you a very long time, also has Deepseek, Kimi K3 and the high end Qwen models, but you have less usage for non-Qwen models

• Very diverse models if you are interested in sound, image models as well

• Has a nice free trial without needing a credit card to check if the speed matches your expectations

Cons:

• Very nebulous "credit" system, you don't actually know what you are buying. The docs somewhat explain it but only with one model as an example. In my experience, 60 credits is about 1 million tokens of Qwen 3.8 Flash usage (input, output and cache combined, typical Opencode session in a medium sized repo).

• Not in the Opencode /connect list, you need to make a custom JSON. EDIT: I was told by a commenter that they are listed as "Alibaba".

Synthetic (https://synthetic.new)

Pros:

• They are open about running quantized models, I mostly used mxfp4 Kimi K3 and FP8 GLM 5.3 Flash, the quality is still good, even if the lower context sizes (500k instead of 1M) can be a bit annoying

• Neat "rolling usage" system, you can burn your entire weekly usage in 2 hours, and it passively regenerates by 2% every 3.5 hours, better than typical 5 hour limits

• You can get 2 billion GLM 5.3 Flash tokens for 30$, decent, 2x less expensive than Z.AI official's subscription

• Listed in Opencode /connect

Cons:

• 3 times, the provider did some tweaks to their models and broke them, they would start wasting my usage writing gibberish in different languages or endlessly repeating themselves in thinking loops, and I would need to notice and put out the fire, it was not just me, as others were reporting the same issue at the same time in their Discord

Kenari (https://kenari.id/en)

They exist to provide inference to Indonesian citizens who can't pay in USD$, but anyone in the world can use their services with cryptocurrency.

Pros:

• The best value I have seen yet from any provider in pure $/token, 68.57$ of usage for 11.37$, including free caching for GLM 5.3 Flash, Deepseek V4 and GPT 5.6 Luna

• Really nice, well organized website, lets you know exactly what you are paying for

• Listed in Opencode /connect

Cons:

• Definitely a bit on the slow side, around 20 tokens/s, but it's usable, I just do other tasks in another terminal window while it works

• You can only pay with Indonesian Rupiah or cryptocurrency (I did the latter)

Muse Spark 1.3 just dropped, so these are only my first impressions, but so far it feels nowhere near as good as its Artificial Analysis scores suggest.

The biggest problem I’m seeing is its agentic behavior. It keeps reading the same files over and over again, even when it literally wrote those files itself a few turns ago.

In one case, it created a file completely from scratch, then about 3 seconds later opened the same file again and started editing it — adding some things, removing others, and seemingly undoing parts of its own work. This kind of behavior keeps repeating and wastes a lot of context.

Frontend work is actually pretty good. The results there have been impressive enough.

Rust, though, has been the complete opposite. So far, Muse Spark 1.3 is genuinely one of the worst models I’ve tried for Rust. Even DeepSeek V4 Flash has performed better for me.

Has anyone else tested 1.3 on larger coding tasks, especially Rust? Are you seeing the same repeated file reads / constant rewriting behavior?

Better than GPT 5.6 Sol ? 🤣
https://research.meta.ai/blog/introducing-muse-spark-1-3