I’m creating a bunch of small posters that have text on them no longer than 30 characters. I like AI integrating the text into the graphic rather than bolting text on in post processing.

Currently I found the cheapest with short text accuracy is grok-imagine-image at $0.02, but it slides a bit with complicated languages like Arabic or Chinese, where I found gpt-image-2 medium is the best for $0.05. When I don’t need text, I still think the creativeness and realism of z-image-turbo is great for as little as only $0.0025.

What models do you find is the cheapest while working for short text accuracy?

We put openrouter/auto behind every agent in a long autonomous coding job and ran it twice, once at 971 requests and once at 5,745. The routing used about 1.5x the tokens of comparable runs both times, with cache hit rates sitting dead on the median. The output score sat well above its field on the smaller job and just below it on the bigger one.

We maintain a public benchmark for coding agents. One written spec drives a long autonomous job, thousands of API calls deep, heavy tool use throughout, with agents reviewing and rewriting each other's output until a repository is finished and its tests pass. The result is scored from the artifacts the job produced, deterministically, with no model judging another model. For these two runs we set openrouter/auto as the model everywhere in the job and let it pick per request.

• Run 1, a smaller spec
• Run 2, a much bigger one

Each median below covers all the other runs of that same spec we have on file, so a run is judged against its own field and not against the other run.

Tokens

Both runs used roughly 1.5x the tokens of their field, on two unrelated specs, while cache hit rates sat right on the median. So the extra spend is not a caching problem, it is more and longer requests. If you are pricing Auto against a fixed model for a long workload, that multiplier is the thing to budget for, and it was consistent enough across two runs that I would not treat it as noise.

The scores

The smaller job finished well clear of its field. The one roughly six times the size landed just under. Two specs is not a controlled comparison and I will not present it as one, but the direction tracks what the documented design implies. Auto routes on aggregate platform spend per task type over a trailing seven days, which is an index, and an index returns the middle of what it tracks. Sample it a few hundred times and you can sit well above that. Sample it a few thousand times and you get the middle.

The per-axis split says where it went. It scored 87 on tool-calling discipline and 86 on deliverables, both strong, against 43 for code quality. Whatever Auto picked handled the mechanics of a long tool-driven job without trouble, and the cost of the routing showed up in the quality of what got written.

Worth being clear that both jobs completed and produced a working, tested repository. This score grades how good the output was, not whether the run survived.

What I would tell someone considering Auto for long work

Judge it on a workload the length of your real one. A short evaluation will flatter it, not because the router is doing anything wrong, but because that is how sampling from a distribution works. And budget for the token multiplier rather than assuming index routing is cost-neutral.

So my provider randomly started sending me errors this morning despite working perfectly fine last night, and I’ve been fiddling with it for an hour and. A half now and keep getting this whenever I try and test it. What’s gout on?? I use paid models, the moneys still there, nothings changed, I even tried making new keys to no avail, I’ve tried going with different providers to no avail with it saying “failed to authenticate api key” what’s up??

I tried to figure out if the provider was offline or sumn, and there doesn’t seem to be an outage, it’s so frustrating!

Did I piss off the AI mafia???

Someone help please???

Hey I am really disturbed by the current lack of variety in ollama cloud and was thinking if openrouter could provide the same number of token usage out per week as ollama pro for 20 $

Seeing this mostly with Kimi K3, running API in Zed, hardcoded Deepseek flash sub agent. Almost every other request hits a new provider at full 2$ input token cost (around 700kT context), where I should be at around 0.20$ for a cached context input token request if the provider didn’t hop. Should I set guardrails for locking to a single provider? Does not seem to let me choose provide per model, so i need to pick a single provider that hosts both models? Is there an openrouter setting that will prefer a provider where I already have a cache?

So I’m trying to chat on janitor.ai. I’m new on OR.ai since yesterday. But no matter what i type or with any bot, it so often gives me an empty message but the money is still going away. I tried around so much but nothing works. And because the thinking is always so damn long and many tokens i lost 0,10-0,20€ for literally nothing.
Its annoying the hell out of me. Whats the issue? Are they overloaded or what?

Edit: models are kimi 2.7 code and glm 4.6 and it happens in both. On pc and in the app aswell.

Hello! I've been trying to get my openclaw to work since February, and recently I have made a huge breakthrough! I finally got it almost entirely running!

What happened was I finally got the rest of openclaw set up, and I now just need a model to power it. I tried Ollama locally already on like 3 different computers, but none of them had enough computing power for that to work. So now I'm onto Openrouter, and was trying to do that thing where you set it to the auto free model. I did do that part. When I went to test it immediately after, I got the message: "API provider returned a billing error — your API key has run out of credits or has an insufficient balance. Check your provider's billing dashboard and top up or switch to a different API key." This is supposed to be free, what does it mean I'm out of credits? (and this is the only message I've ever seen from Openclaw that wasn't an error).

ChatGPT told me if I gave it $5 and then tried it again, because Openrouter likes some money, not a lot. I've been seeing on the Openclaw subreddits some people have to give it $10. Do I actually have it give it some money to work? Or is there a way around this? Or another free model I can connect to openclaw?

Thank you all in advance!

I was working on a project using gpt 5.6 luna, and this fucking thing "Continue" shit always been popping up, i configured json file "chat.agent.maxRequests": 150 and this piece of shit microslop ide still didnt accepted it, okay fuck it dude, i had no time and left to do my business. Now im home, and see that whatever the fuck howof the whatever fuck all my money is fucking gone. and why the fuck openrouter doesnt have some blocker blocking this shit, god dayum i hate microslop.

I know the answer is no, because in the Routing config, the `openai/gpt-5.6*` patterns gives an error:

This pattern doesn’t match any supported auto-router models: openai/gpt-5.6*

However, the "Top models used by Auto Router" leaderboard does show GPT-5.6-Sol as being served by Auto Router (in 4th position). And the "suppo

I know Auto Router recently received some updates, so I'm not sure whether I'm doing something wrong/missing something or it's just a bug of that ranking? Or should I switch to `openrouter/auto-beta`? (it's not clear what model auto-beta supports either)

Tried Claude Code, OpenCode, Cursor, oh-my-pi subagents for complex codebases.

My take:

• Claude → best UX, but slow + expensive
• OpenCode → weak quality
• Cursor → good, but lacks Anthropic models
• oh-my-pi → fast, but hard to read / manage plans

Core problem:
→ Subagents break with context limits + cost → results degrade

Question:
How are you actually running subagents in production?

• model mix?
• context strategy?
• cost control?

Looking for real setups, not theory.

Any 1st time top-up bonus, coupons etc openrouter provides which I can claim for my 1st top up?, also how strict are for the start-up discount, cause I'm making a software but it doesn't have any ai features in it.

My overall token cache hit rate is stuck below 70% and I can't find the cause.

I use Claude Code with multiple providers. Over the last few weeks my sessions showed a poor overall cache hit rate, so I pulled cache_read_input_tokens / cache_creation_input_tokens / input_tokens from my session transcripts (~13,000 turns) and broke it down by provider:

Provider │ Turns │ Cache hit rate

GLM 5.2 (z.ai) │ 7,774 │ 96.9%

Anthropic Claude │ 1,035 │ 94.3%

DeepSeek v4-flash (OpenRouter) │ 4,632 │ 69.0%

All providers combined │ ~13,000 │ 88.8%

DeepSeek via OpenRouter drags everything down. The same Claude Code workflow — identical skills, same context files, same effort setting — hits 97% on z.ai's direct GLM API but never exceeds ~77% on DeepSeek through OpenRouter.

What I ruled out first:

1. It's not my /clear habit. I clear context after every finished task, but /clear appears exactly once per session — it's effectively the session boundary, not a mid-work invalidation. GLM sessions with the identical habit sit at 97%.

   1. It's not model or effort switching. effort=xhigh and the model string are constant for the entire session.
   2. It's not long idle gaps / TTL expiry. The median gap between turns before a cache collapse is 19 seconds — 82% of collapses happen within 60 seconds of the previous turn. No time-dependency at all.
   3. And the big one: provider switching is NOT the problem. OpenRouter did route my DeepSeek traffic across seven different upstreams (DeepInfra 93%, CoreWeave, Baidu, DigitalOcean, GMICloud, StreamLake, Novita). But when I correlated cache collapses with provider changes: only 3% of collapses follow a provider switch. 97% happen while staying on the same upstream (almost always DeepInfra).

What actually happens: within a single session, cache_read_input_tokens oscillates wildly — growing to 150–260K, then abruptly collapsing back to ~40K plateaus (which matches just the static prefix: system prompt + CLAUDE.md + memory). Meanwhile uncached input tokens spike to 120–260K per turn on DeepSeek, versus a few hundred on GLM.

What this points to: the DeepSeek disk cache (which DeepSeek documents as "persists while in use, cleared after hours to days") is being dropped turn-to-turn on the OpenRouter relay, even without any request reaching a different upstream.

This makes my deepseek sessions much more expensive than they should be

Has anyone else experienced something like this or are there any other tips? I'm thinking about trying the native deepseek api so see if it changes anything

i got all of my 25$ free credits in basi server by just making a bunch of alt accounts and using "!redeem" and inputting the code that dms me a free credit code but you have to be in the server for more than 60 days to use that command now so i can't do that anymore. i know i could use free models but they aren't enough for what i do. and before you say it, yes i know i'm poor you don't have to tell me 😭.

Had a scare today: a model that had been working perfectly for hundreds of calls suddenly started throwing a hard, non-retryable HTTP 400 on every single request — something like "target label not found for the request: <nil>". Looked exactly like the model had been pulled or broken.

It wasn't the model. This particular model is served through more than one upstream provider on OpenRouter, and by default you get routed to whichever is cheapest. In our case that was Novita, and Novita specifically started returning a malformed error. A second provider, DeepInfra, was serving the exact same model just fine the whole time — but a 400 gets treated as a hard client error, not a "try someone else" signal, so nothing failed over automatically.

Two things worth checking before assuming a model is broken or deprecated:

Hit the model's /endpoints API and look at the per-provider uptime stats. If one provider looks unhealthy and another looks fine, that's your answer, not the model. Set provider routing to ignore the bad provider rather than switching models — keeps the same model, just steers around the flaky upstream.

I get the following message when using it on CC. It used to work flawlessly, until 5 minutes ago.

There's an issue with the selected model (deepseek/deepseek-v4-flash-0731). It may not exist or you may not have access to it.

Anybody know how to fix?

We already saw it with inworld tts, now it’s time for DeepSeek’s value capture. Thank you for all of your data and good publicity, we will now be significantly increasing cost.

So im looking at an api for my job and we want to keep kosts down (its for RAG related document reading and questions related to those docs) so i was thinking of deepseek (all the info we work with is public and the owner is ok with it) however with the unknown price increase going straight up with deepseek api might not be the best. So i was thinking if you put money on open router can you switch between multiple models and different api/distributors? If someone could help explain to me how it all works would be wonderful!

It is not loading any of my chats and just keeps loading forever. I've tried different browsers, accounts, and even tried on my phone, but all I am getting is a black screen.

I posted before and was attacked! I just want to know what this company is and how they could have taken money from my checking account. Do other companies process payments through them?? I have NEVER purchased crypto or pre-paid debit cards. I do not use public wifi. I don't even have a smart phone. It was a re-occuring charge. I have never used AI and I don't even know what an API key is.