The AI Traffic Infrastructure for Optimized Model Access
Joined February 2025
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Running AI shouldn't lock you into one model just to save money.
ClawRouter gives you one endpoint for OpenAI, Claude, and Gemini — native formats, no code changes — and every model is guaranteed 30%+ cheaper than going direct.
This week: which model for which job, and exactly how much you save. → clawrouter.com/
A 3-5 word status line, refreshed every 30 seconds per active background agent. Each refresh re-sent that agent's whole conversation.
One developer found this in their own request captures.
Agents spend where nobody's watching. Give each one its own key with a hard spend cap.
Your agents ran all night. Which one spent the money?
One model on the big codebase, another on quick checks. Same invoice.
Give each agent its own key. Usage shows cost per key and per model, live.
Cap each key, and a runaway loop hits the ceiling, not your card.
GPT-6.1 Terra is new, so we gave it the same toy-train prompt as Claude Sonnet 5.5.
Measured in our runs: Terra built it in 5 min, Sonnet 5.5 in 16.
Then we pressed "Station closed" on both. You judge.
Both are on ClawRouter. Switch with one parameter: clawrouter.com/v1
GPT-6.1 Terra is now live on ClawRouter. 1M context.
Per 1M, as of Oct 28 (ours / OpenAI list):
Input $0.60 / $1
Output $3 / $5
Cache read $0.03 / $0.05
For agents re-reading long context, cache read is the line to watch.
Base URL clawrouter.com/v1
model: gpt-6.1-terra
The "Codex or Opus?" debate assumes you have to pick one.
Hard refactor → the strongest model.
PR checks, tests, boilerplate → whatever is good enough.
Spend the expensive tokens where they change the outcome.
Which task in your stack is still on your priciest model?
Reported this weekend by @PeterJ_Walker : 7-day average token volume for Anthropic models is up 3.6x since Opus 5.5 launched.
New flagships do this. Usage jumps before budgets do.
See spend per key. Give each key a spend cap. Hit it, and that key stops.
clawrouter.com
Two elevated-error incidents on @claudeai 's status page Oct 25: 30 min on Sonnet 5.5 and others, 21 min on Fable 5.1. Both resolved.
Short outages hurt most when the only plan is a retry loop.
Back off with jitter. Pick a fallback model. On ClawRouter, it's one parameter.
Tens of new inference providers pop up almost every week, per Deedy Das. Low barrier to entry, low-ish margins.
Great for token prices. Risky if your app is wired to one of them.
One OpenAI-compatible endpoint, failover where a backup route exists, a hard spend cap per key.
Same 50,000-token contract, sent to Claude Opus 5.5 ten times.
Full price every call: $1.40.
With prompt caching: $0.24.
83% less in our test. Each cached read was 95% cheaper.
The change is one line:
cache_control={"type": "ephemeral"}
clawrouter.com
If LiteLLM 1.107.0 touched a machine or image, "we never imported it" isn't a defense.
The payload runs at Python startup, before the package is even imported.
Rotate credentials from a clean environment and pin 1.106.2 by digest.
How many of your images pulled it?
🚨 LiteLLM 1.107.0 compromised in a supply chain attack.
Malicious PyPI packages and Docker images delivered a credential stealer and backdoor.
⚠️ The malware runs at Python startup, targeting cloud credentials, private keys, and AI tool credentials.
🔗 Full story: thehackernews.com/2026/10/li…
Anthropic's first new Claude API version since 2023 is out: 2026-10-21. 2023-06-01 isn't deprecated, so nothing breaks until you move.
When you do, check:
1. temperature, top_p, top_k: any value now returns a 400, even the default. Remove them, and retest prompts that relied on a lower temperature.
2. document blocks in assistant messages, and citations on text blocks in user messages, also return a 400.
3. The new SDK majors (Python 2.0.0, TypeScript 1.0.0) send 2026-10-21 by default. Treat the SDK upgrade as a migration, and replay real payloads in CI first.
Requests that still need non-default sampling can stay on 2023-06-01.
Same prompt, two models: a campfire in a forest at night.
Claude Opus 5.5: a 3D scene with real lighting, where the fire lights, burns and goes out.
DeepSeek V4.1 Flash: a clean 2D loop.
Pick by the job. On ClawRouter, both run on one key; switching is a model-name change.
Open-weight models handled 71.5% of the tokens on Vercel's AI Gateway in September. They took 23% of the spend.
From Vercel's AI Gateway Production Index (Oct 15; September traffic, spend estimated at list prices):
→ open-weight models: 71.5% of tokens, 23% of spend
→ 68% of teams that spent more on open-weight models spent more on closed ones too
→ teams with 100M+ tokens since December used 26 models on average
So it's not a fork. It's a mix: open-weight where the volume is, a frontier model where the task needs it.
A mix needs one place to see it. On ClawRouter, open-weight models like DeepSeek, Kimi and GLM run through the same OpenAI-compatible API, and usage shows cost per model.
Two kinds of model. One usage view.
How many people on your team share one API key?
If the answer is "all of us":
→ the bill is one number
→ nobody can tell whose script ran all night
→ someone leaves, the key gets rotated, every service breaks at once
Split the keys. Share the wallet.
Gemini 4 Argon is Google's new flagship.
Output limit went from 64K to 1M tokens.
Launch pricing: $2 in / $10 out per 1M.
Right now only cyber defenders get access. Paid API customers are next.
When it opens up, want it on ClawRouter?
Same endpoint. OpenAI format or Gemini native. No new SDK.
Tell us what you'd run on it.
