ClawRouter

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Every AI infrastructure decision is a bet on who wins the model race. Nobody knows who wins. The teams shipping fastest are the ones who stopped betting. They route. They switch when the numbers change. That's the actual strategy.

claude-opus-4-7 is now available on ClawRouter. One line to access it: model = "claude-opus-4-7" base_url = "clawrouter.com/v1" No SDK changes. No new API keys. Just update the model string. Try it: clawrouter.com/v1

"Just use LiteLLM" works until you're managing a self-hosted service at 3am. Routing infrastructure is operational overhead. The question isn't whether to unify your model access. It's whether to own that infra yourself.

@socialwithaayan The scariest part isn't the 9 malicious ones. It's how easy it was to get into production without anyone asking what the router does with the data it sees.

In 2026, researchers bought 428 LLM routers and let them run against real wallets and real AWS credentials. one router drained $500,000 in crypto before they could stop it. here is the part nobody wants to say out loud. every developer building AI agents right now is almost certainly routing traffic through at least one intermediary they did not build, cannot audit, and are contractually trusting with everything. every API key. every tool call. every user query. passed in plaintext. no encryption. no integrity check. the router reads it all. the researchers formalized this into four attack types. payload injection: the router quietly rewrites your tool calls mid-flight. your agent thinks it called one function. it called another. secret exfiltration: the router copies your credentials while forwarding your request normally. you never see an error. the theft is silent. dependency-targeted injection: the router stays clean during testing. then activates when it detects a specific dependency in your stack. conditional delivery: the router watches for a trigger condition. behaves perfectly until it fires. they did not just theorize these attacks. they ran them. 17 routers silently touched researcher-owned AWS canary credentials. 1 drained a live crypto wallet. 2 were running adaptive evasion, quiet until triggered. then they poisoned routers that looked completely safe. within hours: 99 credentials harvested across 440 coding sessions. 2 billion billed tokens extracted from accounts that showed no alerts. 401 autonomous agent sessions already running with zero human oversight. this is not a future threat. this is infrastructure that is live, trusted, and currently broken. most developers will not find out until their wallet is empty or their AWS bill hits a number that does not make sense.

@adxtyahq Full access to your prompts, your keys, and your spend — and most devs never think twice about who's on the other end. This is the accountability gap the router space needs to fix.

Researchers from university of california bought 28 paid and 400 free LLM API routers. 9 were actively injecting malicious code, 17 stole AWS credentials, 1 drained a crypto wallet. your AI agent might already be compromised and you wouldn’t even know

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One line to switch: base_url = "clawrouter.com/v1" If you're prototyping: OpenRouter is fine. If fallbacks are liability and latency is cost: you need routing you control. Try it: clawrouter.com/v1 Docs: clawrouter-api.gitbook.io/clawrouter

Same applies to region and cost. You pick the endpoint region. Locked routing rules mean the bill looks the same on Monday as it does on Friday. No mid-month surprises.

That's fine for prototyping. It's a liability in production. When fallback = wrong model = wrong output, you need to know which model caught the failure. OpenRouter doesn't show you.

You're running 10k daily requests on OpenRouter. Mid-month, they swap the model. Your latency baseline is gone. Your cost forecast is off. You find out in the bill. This isn't a complaint about OpenRouter. It's how the product works. Auto-routing is the feature. Black-box fallback is part of the deal.

The model that wins the benchmark is rarely the model in production. Cost, latency, rate limits, context window. Evaluation is multi-dimensional. Locking your stack to one provider is a bet that one company stays optimal on all dimensions indefinitely. That's not a bet I'd take.

this is what happens when the AI stack grows faster than the security practices around it. LLM routers sit in the most sensitive position in the entire inference pipeline - they see every prompt, every tool call, every credential. and most teams add them as a convenience layer without any security audit. 26 compromised routers injecting malicious tool calls and one draining a $500K wallet is bad but predictable. the agentic era needs zero-trust for every hop in the inference chain, not just the model endpoint @Fried_rice

Standard pricing on M2.5 / M2.7: $1.96/1M input, $7.85/1M output Highspeed variants: $3.93/1M input, $15.7/1M output One endpoint. Same format you already use. base_url = "clawrouter.com/v1"

MiniMax models are now on ClawRouter. 4 variants live: - MiniMax-M2.5 - MiniMax-M2.5-highspeed - MiniMax-M2.7 - MiniMax-M2.7-highspeed

Self-hosted routing means you see every call, every model, every cost — before it hits your invoice. No session fees. No bundled pricing you didn't agree to. Just clean per-token costs you actually control. clawrouter.com

That's $800/day — before you've shipped a single feature.

Anthropic just started charging $0.08/session-hour for managed agents. Not per token. Per hour. You're running 10K sessions/day. Some finish in 2 minutes. Some take 40. You don't control the clock. You don't control the bill.

Not all the time. But enough of the time that sending every task to the top tier is just burning money. The real edge isn't picking the best model. It's knowing which task actually needs it. That's routing. That's ClawRouter.

Recent routing research put numbers to what most engineers already suspect: task complexity doesn't map to model tier as cleanly as vendors want you to think. Cheaper models are now solving problems that used to require the most expensive ones.