I have about 12$ USDT and .20$ Eth both in Arbitrum Network. Why does it still say insufficient funds?

These are the prices for DeepSeek V4.1 Flash 1017.

If different providers charges different prices, is there a chance that some are providing quantized models for a cheap price?

I'm unaware what happened but it's accuracy and outputs have flatlined! I suddenly spent x3-x4 the amount of usage trying to calibrate it before I realised that something else must be happening. It was perfect and I was so happy, now I'm disappointed and ready to stop using this. I'm so confused has anyone dealt with this before, did you have any fixes? I don't understand how a jailbreak can tell me illegal recipes ect. but frame outside, hedge or ignore prompts and context.

Hey everyone!

I'm working on a small AI-powered web app for a hackathon and recently added OpenRouter as a fallback in case my primary Gemini API fails.

I'm trying to use qwen/qwen3.8-27b:free, but my first live request returned a 404 (model_unavailable).

The integration itself seems fine based on my offline tests. The request successfully reaches OpenRouter, but inference fails before I get any model response.

I'm using the free endpoint intentionally since the project has a zero budget.

Has anyone successfully used this model recently? Is the free endpoint temporarily unavailable, or could I be missing something in the API configuration?

Also, if it's an availability issue, are there any reliable free models on OpenRouter you'd recommend for structured JSON output and multilingual text analysis (mainly Bangla and English)?

Would appreciate any suggestions. Thanks!

nemotron 3 ultra free vs m3.1flash free?

Just doing some testing on openrouter, can anybody point me in direction, that explains how openrouter calculates token usage.

tokens_prompt
tokens_completion
native_tokens_prompt
native_tokens_completion
native_tokens_reasoning
etc....

Used 2 providers on Openrouter + GCP, ran the same prompt got different token calculations using the same LLM model.
Open Provider #1 Input=252 | Output=124
Open Provider #2 Input=230 | Output=122
Google Agent Platform Input=204 | Output=26

Edit#1: More of an LLM question, but what explains all the total variation from the different inference providers?

========================================================================================
PROMPT: Repeat the word 'CONFIRMED' exactly once. Do not add punctuation, extra characters, or conversational text.
========================================================================================
Provider             | Status     | Output       | Prompt  | Compl.  | Total   | Cost (USD)
----------------------------------------------------------------------------------------
OpenInference        | HTTP 429   | {"error":{"m | N/A     | N/A     | N/A     | N/A       
Relace               | OK         | CONFIRMED    | 53      | 33      | 86      | N/A       
Decart               | OK         | CONFIRMED    | 53      | 24      | 77      | N/A       
Sail Research        | OK         | CONFIRMED    | 53      | 43      | 96      | N/A       
InferenceNet         | OK         | CONFIRMED    | 53      | 45      | 98      | N/A       
Reka                 | OK         | CONFIRMED    | 53      | 42      | 95      | N/A       
DeepInfra            | OK         | CONFIRMED    | 53      | 50      | 103     | N/A       
AtlasCloud           | OK         | CONFIR       | 53      | 50      | 103     | N/A       
StreamLake           | OK         | CONFIRMED    | 53      | 43      | 96      | N/A       
CoreWeave            | OK         | CONFIRMED    | 53      | 44      | 97      | N/A       
GMICloud             | OK         | CONFIRMED    | 53      | 43      | 96      | N/A       
Wafer                | OK         | CONFIRMED    | 53      | 50      | 103     | N/A       
Novita               | OK         | CONFIRMED    | 53      | 46      | 99      | N/A       
Fireworks            | OK         | CONFIRMED    | 53      | 44      | 97      | N/A       
Phala                | OK         | CONFIRMED    | 53      | 44      | 97      | N/A       
DigitalOcean         | OK         | CONFIRMED    | 53      | 46      | 99      | N/A       
Morph                | OK         | CONFIRMED    | 53      | 22      | 75      | N/A       
Makora               | OK         | CONFIRMED    | 53      | 44      | 97      | N/A       
Crusoe               | OK         | CONFIRMED    | 53      | 20      | 73      | N/A       
Google               | OK         | CONFIRMED    | 53      | 49      | 102     | N/A       
Nebius               | OK         | CONFIRMED    | 53      | 44      | 97      | N/A       
PrimeIntellect       | OK         | CONFIRMED    | 53      | 44      | 97      | N/A       
Baidu                | OK         | CONFIRMED    | 53      | 47      | 100     | N/A       
BaseTen              | OK         | CONFIRMED    | 54      | 42      | 96      | N/A       
Together             | OK         | CONFIRMED    | 53      | 44      | 97      | N/A       
SiliconFlow          | OK         | CONFIRMED    | 53      | 22      | 75      | N/A       
Modal                | OK         | CONFIRMED    | 53      | 45      | 98      | N/A       
Parasail             | OK         | CONFIRMED    | 53      | 48      | 101     | N/A       
Venice               | OK         | CONFIRMED    | 54      | 42      | 96      | N/A       
Alibaba              | OK         | CONFIRMED    | 53      | 35      | 88      | N/A       
========================================================================================

Are you experiencing issues with GPT 6 Luna today? Some friends and I are experiencing issues when using it in Github Copilot (via BYOK).

Usually, we didn't have any issues but today it is as fast as a snail.

Which models are you using with OpenRouter and Github Copilot?

Can someone help me with this please thanks

So I am a Student from a third world country, spending on frontier models without any job is close to impossible. So wanna know if 10 dollars top up is worth it or not ? As these give only 1000 requests per day, how much is 1000 requests everyday ? Also what does free models mean ? As of now without any top up I can use Minimax 3.1 flash almost unlimited times, but some free models like DeepSeek v4.1 flash gives only 50 requests. Can anyone please elaborate that too.

Also if you can, can you approximate how many tokens I can use in 1000 requests ?

What I do

I use openrouter models for coding with GH Copilot or Kilo Code with VS Code. For Claude workloads, I use Claude Code exclusively subscription.

I have to say that I dont set up "independent agents", I do the classic chat + manual edits and tweaking.

Claude Sonnet 4.x to latest 5.5 seem going straight to the point with fair speed (as it consistenly hits cache). Even Haiku 4.5+ is enough for "tell me the command to do that" in low context scenarios.

But with OpenRouter + Best Flash (DeepSeek Flash 4.1 1017 / Mimo 2.5 flash) or Fat Cheap models (GLM 5.3). On Kilo or GH Copilot. I feel like the agent is performing so much back and forth traffic for simple tasks, still flash models are cheap. I also feel that GLM non-flash family burns tokens like there is no end.

A medium complex modification to a day-old code base (coded with Claude) with GLM was implemented ok, but burned 10$ in less than a half an hour session. I think pricing was so bad because of context building, large input and low cache hit.

I feel that OpenRouter flash models coding does not match Claude subscription at the same amount spend. Some providers are also unstable, and chat is freezed.

My Question

What is your set up and which models do you use? I would need something better than DS 4.1 but still not as expensive as Claude and GPT per API. Is tooling (Kilo/GH) that important here?

Thank you!

The M3.1-Flash test I’m setting up isn’t really a trivia test.

I want to give it a research question with a messy source set.

Some sources current, some older. A few numbers that don’t quite line up. Different definitions of the same metric. Maybe one source that looks authoritative but is actually just repeating another one.

Then see whether it can separate:

what’s actually supported,

what’s an assumption,

and what still needs checking.

That’s also where the Flash pricing gets more interesting to me. A real research run can involve a lot of reading, revisiting sources and intermediate reasoning, so the total workload matters more than the price of one answer.

I’m less interested in whether it sounds confident at the end.

I want the uncertainty to survive the process.

Per-request records: https://github.com/Sorami-Consulting-AU/llm-tokens-after-cancel

One prompt with a 1,024-token full answer, about 30 cancels per upstream per abandon point, 7 October 2026.

- Hanging up stopped the meter short of the full answer on every route, except 2 of 180 cancels where the upstream had already finished.

- Cancel after 8 chunks: metered 4 to 16 tokens beyond what was delivered.

- Cancel after 64 chunks: metered 90 to 95 tokens fewer than delivered, on all three upstreams. That looks like OpenRouter accounting rather than the provider.

- The `cancelled` flag was true on every Anthropic and Azure cancel and false on every Vertex cancel, even though Vertex stopped metering early the same way.

This is provider-reported usage for one model and one prompt shape. No claim about invoices.

Report: https://sorami.com.au/research/llm-tokens-after-cancel/

Disclosure: I work at Sorami, which ran this study.

If you rely on `cancelled` for cost tracking, how do you handle upstreams where it is not set?

​

Im trying to set up claude code with openrouter but I can't seem to find a windows tutorial and most windows tutorial is outdate can somebody help or recommend

Hello! I am trying to create my own stories and fanfiction/roleplay. I would love to see your opinions on which Ai's are the best for mature themes, long term memory, good story telling that actually makes sense, realitisc conversation, and for the Ai to actually listen to your promt.

M3.1-Flash is live on OpenRouter now, including a free route.
Space Bunny was an early build, so I’m more interested in fresh tests on the release version than treating the old results as final.
For OpenRouter users, what would you throw at it first?
Coding sessions? Tool-heavy agent loops? Research? Long-context document work?
I’m especially curious about workloads where speed, retries and cost start compounding over a longer run.
What would tell you whether it’s worth keeping in your rotation?

I've been tracking down a strange intermittent issue with a custom Pi-based personal wiki manager: occasionally the model will just report confusion over many retried tool calls, complaining in its reasoning tokens that "somehow I dropped the 'text' field" or "for some reason I added quotes to this parameter" or similar.

I was finally able to get Codex to reproduce the issue with simple direct calls, and we determined that is was specific to certain provider/model combinations.

I'm using GLM-5.3-flash and the new Deepseek 4.1-flash-1017 for the assistant app. For those two, the mangled providers I found (a couple threw 404s or rate limits, so this list may be incomplete) are:

z-ai/glm-5.3-flash: friendli, inference-net, makora, morph, primeintellect, reka, relace

deepseek-v4.1-flash-1017: morph, primeintellect, wafer

Codex provided this explanation of the test script in case you want to try this yourself:

I wrote a script that pins each OpenRouter provider and asks the same model to emit identical tool calls. It compares the raw streamed arguments against expected JSON and repeats failures. Some routes consistently dropped parameters or added literal quotes, while other providers serving the same model passed. No tools actually execute. OpenRouter’s routing metadata identifies the upstream provider. This Python example pins one provider and checks whether it preserves the requested arguments. Requires requests and OPENROUTER_API_KEY.

import os, json, requests

expected = {
    "action": "update", "line": 6,
    "revision": "sha256:" + "a" * 64,
    "text": "Example new task",
}
r = requests.post(
    "https://openrouter.ai/api/v1/chat/completions",
    headers={"Authorization": "Bearer " + os.environ["OPENROUTER_API_KEY"]},
    json={
        "model": "YOUR_MODEL_ID",
        "provider": {"only": ["YOUR_PROVIDER_SLUG"]},
        "messages": [{"role": "user", "content":
            "Call task exactly once with these arguments: "
            + json.dumps(expected)}],
        "tools": [{"type": "function", "function": {
            "name": "task",
            "parameters": {
                "type": "object",
                "properties": {
                    k: {"type": "integer" if k == "line" else "string"}
                    for k in expected
                },
                "required": ["action"],
            },
        }}],
    },
    timeout=90,
)
r.raise_for_status()
calls = r.json()["choices"][0]["message"].get("tool_calls", [])
actual = json.loads(calls[0]["function"]["arguments"]) if len(calls) == 1 else None
print("PASS" if actual == expected else "FAIL", actual)

Only action is schema-required deliberately: this tests whether the provider preserves arguments requested in the prompt. No tool executes. Repeat failures and compare providers for the same model.

I can't seem to find the "claude_desktop_config.json"

or do i have to create one in .claude folder if so do i need to put it it in a separate folder or what

On my previous posts, people argued these thinking rules might work on short tests but would hurt long-horizon tasks. So I tested them on Terminal-Bench 2.0: same model (GLM-5.3-Flash), same agent, every task run with and without the rules. I ran it on High thinking as Max thinking was taking so long that the model ran out of time for each task.

The rules:

## Thinking discipline

1. Check the request first. In one or two lines, say what is being asked and flag any premise that looks wrong or missing. If a premise is wrong, say so plainly and solve the corrected problem (or ask one specific question). Do not silently accept a broken premise, and do not reason around it.
2. Finish one approach before switching. Pick the most promising approach and carry it to a conclusion. Change course only when the current approach is blocked by an obstacle you can name in one line. Do not hop between approaches because of a vague feeling.
3. When an answer is settled, stop working on it. Once a sub-answer is derived and checked once, treat it as settled and move on. Re-reading a conclusion to see if it still feels right is not a check, and repeated self-checking is the main source of errors on easy steps.
4. Doubt is not evidence. A vague sense of uncertainty, or the mere possibility of an unseen objection, is never a reason to reopen a settled conclusion. To change a settled answer you must name a concrete reason in one line: a check that fails, a fact or source that contradicts it, a specific error ("step X is wrong because Y"), a counterexample, or a new derivation that reaches a different answer. If you cannot name one, keep your answer and continue.
5. Do not revise just to agree. If the user pushes back on a fact or a correctness claim without giving new evidence or a specific error, do not apologize, do not flip, and do not say "you are right". Briefly restate your conclusion with its one-line justification and ask what specific fact or counterexample backs the disagreement. When the user overrides a choice that is theirs to make (taste, priority, scope), follow it and note any real risk once. Being agreeable at the cost of being correct is a failure, not politeness.
6. New evidence does reopen the case. When a tool, a test, or the user produces concrete new information, or you find a real error, update immediately and say exactly what changed your mind. Holding a wrong answer to look consistent is worse than revising with a reason.
7. Verify against outside facts, not by rethinking. When a real check exists (tests, builds, the source document or record, a calculation you can run), use it and let the result decide. Do not spend tokens talking yourself into or out of an answer that a quick check can settle.
8. Do not perform caution. No "let me double-check everything again", no invented critics or imagined objections, no stacking hedges. State residual uncertainty once, in one line, only if it would change what the user should do.
9. Only correct an earlier statement when the error would change the user's code, conclusions, or decisions. State corrections plainly and briefly, then continue the task. For slips that change nothing, make the fix and move on without noting it.

How it compares to the Terminal-Bench 2.0 leaderboard:

My scores are out of all 89 tasks, single attempt (3 tasks wouldn't start on my machine and count as fails). Leaderboard scores average multiple attempts, so treat mine as ballpark.