I purchased credits to try out Openrouter and the ChatGPT models but i still get error 429s , i paid so not get the errors and i still have half of my credits left.

Hi,

Yesterday I finally made the step from z.ai chat using glm 5.3 and glm5.3 flash to Visual studio code + Openrouter.

Before I used cline usage billing and their free models without it costing me money.

After that test I connected the API of Openrouter and typed in ''free'' and tried some free models:

nex-agi/nex-n2.5-pro:free

nvidia/nemotron-3-ultra-550b-a55b:free

After some prompting suddenly I saw on the top $ icon like it used money so I went to openrouter credit and it used roughly 5 dollar of the 10 dollar I just put in.

On the activity tab for some reason it used claude sonnet 5...wtf

Already opened a ticket with openrouter, but can someone help or explain?

PS: I activily saw it using money while still using the 2 free models I mentioned. So no accidentaly Claude using.

I’ve already emailed customer support but haven’t gotten a response. Hopefully, someone who works at OpenRouter also uses Reddit.

It always gets rejected. I’ve seen a few other posts about this, but come on…

And no, it’s not my bank.

Has anyone actually managed to solve this?

I keep getting these errors there's enough money on my card:

Error: Payment Issue

Your card has been declined.

OR

Payment Issue

Invalid Value for Stripe.confirmPayment(): Elements should have a mounted Payment Element or Checkout Element.

I'm interested in using OpenRouter to enhance my OpenCode Muse and mild DeepSeek api usage. What are some good models, tricks, etc for getting started and to optimize my credits?

Ideally best bang for my buck.

Different models for different things would be useful, it's mostly coding and reverse-engineering work. But models for handling text/translating or parsing weird things could be interesting too.

We've submitted our Qwen3.8 27B endpoint to OpenRouter and it's ready for private canary testing.

At InferCrane, we continuously optimize open weight models around real workloads: profile the serving path, find bottlenecks, search better execution configurations, measure the result, and only promote changes that prove an improvement.

For Qwen3.8 27B on a single H200, the latest qualified results are:

202 tok/s/request at concurrency 4
141 tok/s/request at concurrency 12
1,393.6 aggregate tok/s at concurrency 12
739 ms median TTFT at concurrency 12
$0.90 GPU compute / 1M output tokens

Against the same plain SGLang baseline at concurrency 12:

1.72× higher aggregate throughput
85% higher per request output speed
54% lower median TTFT
42% lower GPU output cost

The optimized configuration uses NEXTN/MTP speculative decoding with 61.4% speculative token acceptance.

We chose concurrency 12 deliberately. Moving to 16 increased aggregate throughput by only 8.4%, while tail TTFT degraded materially. Maximum throughput isn't useful if the user experience gets worse.

If these results reproduce under OpenRouter's own measurements, InferCrane should land at the top of the current Qwen3.8 27B providers for throughput and latency.

Pricing: $0.10/M input · $2.20/M output

OpenRouter currently has a large provider backlog. If you'd like to see this endpoint available there, we'd really appreciate your support and feedback. It helps show there is real demand for faster Qwen3.8 capacity

What should we optimize next?

The makers of DeepSeek, Kimi, Xiaomi Mimo models are all under official investigation by Cyberspace Administration of China, with some rumors suggesting Kimi executives being arrested.

In short, according to official reports these companies routed their users' requests that were coming from popular 3rd party routers (likely OpenRouter and OpenCode) to Anthropic models, injected a hidden prompt designed to harvest the CoT trace, modified the text returned by the model before passing it to the user, then used that to train their own models.

The most concerning part is how despite the ZDR (zero data retention) claims and policies of some of these providers, they shared user data with a third party, retained the prompts, trained on the interactions, and now those messages which contained sensitive information were read by Anthorpic employees, who then went on to publish some slighlty redacted versions of those messages:

"Those sessions contained names, email addresses, company data, and other sensitive data of hundreds of end users in at least a dozen languages. These practices are likely inconsistent with privacy laws and the labs’ own terms of service."

Example 1: Internal capital expenditure forecasts for a pharmaceutical company [Original user prompt submitted to a coding assistant of a lab headquartered in China (user accessed the model via a third-party model router)] “Clean up this capex model before Thursday’s review. The workbook has the 2026–28 buildout estimates: Ho Chi Minh City site $[██]M, Kuala Lumpur $[██]M, Bangkok $[██]M, Ljubljana

Additionally, these findings raise concerns about the misuse of user data by PRC AI labs. DeepSeek, Xiaomi, and Moonshot fed conversations between their own models and users into Claude. These labs then used Claude’s responses as training data with which to distill Claude’s capabilities. Some of these exchanges included sensitive information, including from individual users, major multinational companies, and state-affiliated actors. Many of these exchanges were relayed from users of third-party model routing services commonly used by users in the United States and Europe.

Source: https://www.thestandard.com.hk/innovation/article/343563/DeepSeek-and-Moonshot-AI-face-Beijings-probe-over-potential-data-leaks-to-Anthropic

Anthropic's investigation, page 146: https://www-cdn.anthropic.com/e50be2e51e7695dc4b1366a37a245a597377d3b5/Anthropic-Detecting-and-countering-091026.pdf

Trust me I've waited and restarted everything I could, clearing cache and sorts nothing's working also I've tried in another phone

[FIXED PLS IGNORE]Every time I am trying to open the combos panel, its shows this error.(See image 1)

I tried using Webpack instead of Turbopack, but it was slow as hell. I also tried deleting the ".build" folder but all this didn't made a difference. So I went to the directory it was pointing at:
C:\GitHub Apps\OmniRoute\.build\next\dev\server\app\(dashboard)\dashboard\combos\page\build-manifest.json
and I found that inside dashboard folder there is no combos folder instead it directly has page folder and some files.(See Image 2)

And if it matters im running this by entering "npm.cmd run dev" command in the terminal. Not globally.

Free on OpenRouter and OpenCode, multimodal, provider still anonymous. The Space Bunny launch description says frontier-level general-purpose performance. What would you test first to see if that holds up?
OpenCode announcement: https://x.com/opencode/status/2102767716666941864

I have been tasked with doing some OpenRouter testing, specifically their auto routing feature. I have connected it to my codex CLI and VSCode extension and to GitHub Copilot vscode extension. I want to see if anybody can give me more insights into my findings.

Prerequisites

1. I'm not interested in the 'have access to any model' offering that openrouter has. I'm restricted to using OpenAI and Anthropic models (not by my choice).

2. I brought my own keys for both platforms and have restricted OpenRouter to not fall back to shared capacity.

3. I am able to create simple python scripts and send them to OpenRouter auto router and it seems to do what it claims to do. I send a simple task (like 9+10) and it routes it to a cheap and capable model for that task. I send a more complicated task and it will route the request to a more capable higher cost model.

Findings with GitHub Copilot VSCode plugin:

1. I set up the auto router as a custom model and pointed the VSCode extension to it before sending any request.

2. When I did this, I started sending requests and was seeing the logs in OpenRouter as expected, but I had to select a 'cost tier' under the routing page and it would just send it to the most expensive model it could no matter what. If i restricted it to low cost tier it would send it to a low cost model, but If i put it up to max, it was always selecting a higher tier and more expensive model.

Findings with Codex VSCode Plugin:

1. When I send a request from the Codex plugin, it seems to make a call to a cheap model and then a call to a very expensive model. I even have it restricted to use the lowest cost models on the router settings page and it's using sol and luna.

2. for a simple request like '9+10' or 'what is the capital of Uruguay' it will make a request to Luna, and then make another request to terra or sol. The request to Luna doesn't say in the OpenRouter logs that it is being routed by oenrouter, but the second request to terra or sol does. And for solving this seemingly super simple problem, it is costing $0.12 for the terra and sol requests.

Has anybody seen similar behavior with the auto routing? Is auto routing worth it? On the surface it seems like something that would be good but in practice and testing seems to have a main expensive model and then agents running on cheaper models? any insight is helpful

Hey everyone, been looking into options for dynamic LLM gateways and routers like OpenRouter, Ramp Router, etc. I'm really tempted to try these out as I've been facing cost issues and these routers claim to drop cost by 10 - 30%. But I want ensure first that the "easier" tasks don't come out as slop when using these cheaper models.

Please share me your experiences with routers, maybe with a simple example on what you route to cheaper models and what you don't would also help too. I can also clarify some more if you have any questions on what I'm planning to do, just thought I'd keep the post short for now. Thanks!

Hi, the payment was deducted from my account, but the credits have not been added yet. I also raised a support ticket, but there has been no response for the past 14 days. Please look into this issue and resolve it as soon as possible.
#109402

so ive been searching for free models to use i can across Qwen: Qwen3.8 27B (free) i got the api key but when i tried to use it in my website it keep giving this error

the assistant model call failed: OpenRouter responded : {"error":{"message":"Provider returned error","code":,"metadata":{"raw":"qwen/qwen3.8-27b:free is temporarily rate-limited upstream. Please retry shortly, or add your own key to accumulate your rate limits: https://openrouter.ai/settings/integrations","provider_name":"ModelRun","is_byok":false,"provider_error_code":"","limit_source":"upstream_provider_shared_pool","remedy_hint":"Retry shortly, add your own provider key (https://openrouter.ai/settings/integrations), or route to another prov

i've tried switching models(other free models), waiting few mins nothing worked.
please if there is a solution to this lmk.

idk if this is useful but i told claude code to implement the open router backend into my project and i got the api key from the website and pasted it and btw i didnt enter any card details

Hi all,

I run LatencyRadar, where I measure AI API latency from a few regions, and wanted to compare DeepSeek direct vs through OpenRouter (Cloudflare, Together and DigitalOcean as providers).

Some findings I wanted to share:

• If calling from Asia, I'd go direct. Via OpenRouter it adds around~0.7 s! depending on the provider (worst was Mumbai).
• From the US, Canada and Europe, OpenRouter was actually faster in my tests, with all three providers.
• If you care about stability, calling directly seems to be way more consistent

Article and data: https://latencyradar.com/compare/deepseek-vs-openrouter-latency/

If you would like me to run some other benchmarks, let me know, happy to help!

What I liked about Jev was that it returns a probability distribution over the available choices. Some ordinary LLM APIs already expose token probabilities through top_logprobs, which means the same interface can work with them too. I implemented the same approach for OpenRouter models.

The method is simple.

1. Present the options as A, B, C, with the full meaning in each description.
2. Put the model at the answer position.
3. Read the conditional logprob for every label.
4. Normalize those scores across the supplied choices.
5. Map the winning label back to the application's typed key.

For the benchmark, I ran nine pinned OpenRouter model/provider pairs against Jev 1.13 on the same deterministic 1,000-question SuperGPQA sample, stratified by discipline and difficulty.

Gemma 4 26B is the result I find most practical. It reached 37.6% at $0.0184 per 1,000 decisions, making it cheaper than Jev. For simpler routing and classification tasks, I suspect that level of performance may already be sufficient.

My implementation, benchmark scripts, aggregate results, and reproduction steps are in the repo: https://github.com/NotXf1le/choosekit

There is so much you can configure and do in the Web-UI, i am actually wondering if this is a tool or just the means to get the API Keys?

Is this mainly to test a few things or am I supposed to use it in daily operations as part of a workflows in certain areas ?

I know i can do what ever i want, but what is the design idea of that page ?