Foyton API | Multi-Model AI

Foyton API: one endpoint for DeepSeek Flash, GPT, Claude & Gemini. Engineering notes, cost experiments and honest model takes. Built for developers and teams.

[Foyton API | Multi-Model AI @FUYITONG111]

Cheap tokens get expensive when an agent keeps retrying.

When comparing models, track cost per accepted result, p95 latency, and retries—not just $/1M tokens.

At Foyton API, we make multi-model access simpler. Bring your own workload to the comparison.
https://t.co/pX1dQwitcZ

[Foyton API | Multi-Model AI @FUYITONG111]

@freeCodeCamp The most underrated MCP control is not prompt filtering; it’s least privilege at the tool boundary. A malicious instruction can only become an incident if the tool was allowed to do something useful.

[Foyton API | Multi-Model AI @FUYITONG111]

@AndroidDev Android MCP is a huge distribution unlock, but the app-function boundary becomes the product. Capability discovery, scoped auth and a user-visible audit trail will matter more than the demo prompt.

[Foyton API | Multi-Model AI @FUYITONG111]

@JasonYanowitz A thread written entirely by Claude through MCP is a great capability demo and a great test of citation discipline. Did you keep the raw tool outputs somewhere so a human can reproduce the claims?

[Foyton API | Multi-Model AI @FUYITONG111]

@ArtificialAnlys @CAIS @scale_AI New eval, same product question: which model is best at the task you actually pay for? I’d love this leaderboard joined to failure rate and accepted-task cost, not just a single intelligence number.

[Foyton API | Multi-Model AI @FUYITONG111]

@cognition The useful unit here is cost per accepted task, not raw code generation. A model that spends slightly more tokens but saves one human debugging pass can win the weekly bill. Does FrontierCode track retries or only final task cost?

[Foyton API | Multi-Model AI @FUYITONG111]

@MatthewBerman The subscription-to-token bill comparison is real, but the hidden line item is maintenance. If the agent keeps rebuilding the same internal app every month, eventually you’ve hired a very confident SaaS company with no support desk.

[Foyton API | Multi-Model AI @FUYITONG111]

@deepseek_ai Open-sourcing the inference stack is underrated distribution. Model quality gets the launch; kernels, batching and speculative decoding decide whether that quality survives at 3am with 10k concurrent users.

[Foyton API | Multi-Model AI @FUYITONG111]

@GoogleDeepMind “Works for days without taking shortcuts” is exactly the claim I’d test with deliberate interruptions: process restart, tool outage, stale context and a hard budget cap. Long-horizon reliability is mostly a recovery benchmark.

[Foyton API | Multi-Model AI @FUYITONG111]

@AnthropicAI A model grading its own conversation is useful, but I’d want the grader and the policy to be observable too. Self-assessment is a smoke detector; it still needs an independent test that the alarm actually works.

[Foyton API | Multi-Model AI @FUYITONG111]

@OpenAI An offline world is a solid sandbox, but the hard part is making it boring enough that a model can’t find the seams. What gets logged when an agent tries to escape versus when it succeeds? The attempt signal is probably the useful early-warning dataset.

[Foyton API | Multi-Model AI @FUYITONG111]

DeepSeek Flash is brilliant at unglamorous work: log triage, test-failure clustering, CSV archaeology, renaming the 47th microservice…

What’s the weirdest production task where a cheap, fast model surprised you? I’m collecting the boring jobs—the useful wins usually hide there.

[Foyton API | Multi-Model AI @FUYITONG111]

An API benchmark is not a provider decision. Replay 50 real requests:

• cold and warm first-token latency
• p95 total latency
• JSON and tool-call validity
• retry and failure rate
• billed cost per accepted result

Pin model versions, reasoning settings and concurrency. The right provider is the one that wins your trace set.

[Foyton API | Multi-Model AI @FUYITONG111]

@thdxr The missing product is the one that turns 10× cheaper tokens into 10× more attempts. Agents that verify, retry and compare are how inference capacity becomes actual demand.

[Foyton API | Multi-Model AI @FUYITONG111]

@theo One MCP server for the whole fleet sounds like a great demo and a terrifying single point of “why is production on my laptop?” Have you added per-target permission scopes yet?

[Foyton API | Multi-Model AI @FUYITONG111]

@ClaudeDevs Long-running tasks sound great until day two: who owns the retry, where is the state, and what happens when the client closes the laptop? The completion notification is the easy part; resumability is the product.

[Foyton API | Multi-Model AI @FUYITONG111]

@dair_ai The study’s sharpest result is that stack choice beat model size. A bigger brain won’t save a bad tool contract; it’ll just fail with more confidence.

[Foyton API | Multi-Model AI @FUYITONG111]

@colemurray Story points measured in dollars would finally satisfy both product and finance. At Foyton API we track accepted-job cost for the same reason: a failed $2 task is more expensive than a successful $5 one.

[Foyton API | Multi-Model AI @FUYITONG111]

@a16z The scary stat is the 34% with write access. Read-only agents can embarrass you; write-enabled agents can file the incident report and approve the remediation.

[Foyton API | Multi-Model AI @FUYITONG111]

@a16z Translation: the agent doesn’t need more IQ; it needs the corporate equivalent of “read the room.” Permissions, fresh context and a clear source of truth beat another half-point on a benchmark.

[Foyton API | Multi-Model AI @FUYITONG111]

@pratikg The token math says rent; the trust math often says own. The decision changes when the API takes three weeks off and your agent still has a deadline. I’d add failure-domain cost, not only resale value.