The control plane for production AI. 600+ models behind one API, with smart routing, spend management, governance & observability.
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Not every Claude Code call needs Opus.
Auto Routing, now in beta: describe the kinds of work your team does, point each one at a model, and Requesty makes the call on the first message.
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Don’t pick models based on someone else’s benchmark.
FrontierCode 2.0 says open models struggle. On our own set of 129 Go backend tasks, Kimi K3.1 Pro solved 81%, sitting right alongside the frontier labs.
Run new models on YOUR workload. Congrats @Kimi_Moonshot team!
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@paperinstr's Ultramarine 1 Flash now available on Requesty via @FireworksAI_HQ !
fireworks/ultramarine-1-flash
@paperinstr’s Ultramarine 1 Flash is now on Fireworks.
It outscored Fable 5 on Paper Instruments’ internal knowledge-work benchmark (59.3 vs 57.8) at ~98% lower inference cost per task.
Start building through our API, no dedicated GPUs needed: fireworks.ai/models/firework…
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Auto Routing in three steps:
1. Write the question to answer
2. List the answers it can choose from
3. Pick a model or policy for each
Unsure? It falls back to your strongest model. Pinned per session, so you keep cache hits.
docs.requesty.ai/features/au…
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Particle-1 is DeepSeek V4.1 Flash, post-trained to finish tasks in fewer tokens. So we ran both through our internal coding benchmark: 88 real Go backend tasks, hidden tests, same harness.
39% fewer tokens per task, 20% lower cost, half the time per turn.
Congrats @Suhail 🧵
ALT Requesty logo. Kicker "Coding benchmark · Particle-1 vs its base". Headline "Same base model. 39% fewer tokens per task." Four panels compare deepseek-v4.1-flash (grey dots) with particle-1 (blue dots). Tokens per task: 2,399k vs 1,463k. Cost per task: $0.15 vs $0.12. Latency per turn: 5.4s vs 2.7s. Pass rate: 67% vs 61%, one dot per task out of 88.
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Speed stood out most.
At 2.7s median per turn, Particle-1 was the fastest of all 19 models we tested, ahead of glm-5.3-flash (3.4s) and gpt-6-sol (3.6s).
ALT Requesty logo. Kicker "Coding benchmark · Latency". Headline "Fastest of 19 models. 2.7s median per turn." A ranked bar chart of median seconds per model turn for 19 models. Particle-1 is first at 2.7s, in blue, followed by glm-5.3-flash 3.4s and gpt-6-sol 3.6s. Its base, deepseek-v4.1-flash, is at 5.4s. The slowest are deepseek-v4-pro at 17.0s and hy4-preview at 22.6s.
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The trade-off: 61% of tasks solved vs 67% for its base, level with DeepSeek V4 Pro among open-weight models. Frontier models still lead, Opus 5.5 at 90%.
For high-volume coding loops where tokens and turn time add up, worth a try:
model="particle/particle-1"
ALT Requesty logo. Kicker "Coding benchmark · Pass rate". Headline "61% of tasks solved, level with DeepSeek V4 Pro." Eight open-weight models, each drawn as 88 dots, one dot per task, with solved tasks filled. hy4-preview 68%, deepseek-v4.1-flash 67%, qwen3.8-flash 64%, kimi-k3 64%, particle-1 61% (in blue), deepseek-v4-pro 61%, glm-5.3 60%, glm-5.3-flash 58%.
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Fewer tokens per task is the kind of progress we love to route.
Congrats @Suhail on Particle-1: 28 to 52% fewer output tokens across Terminal-Bench 2.1, ReactBench, ADE Bench and DeepSWE 1.1, pass rates in line with the base.
On Requesty today as particle/particle-1
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Sensitive project data. Public-sector clients. 5 to 15 models.
LOUPZ points its AI chats, apps and agents at one European endpoint, router.eu.requesty.ai. Data goes to the US only when strictly required, and no app is tied to a provider.
requesty.ai/customers/loupz
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Not every Claude Code call needs Opus.
Auto Routing, now in beta: describe the kinds of work your team does, point each one at a model, and Requesty makes the call on the first message.
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We used it ourselves.
Our Claude Code sessions on Auto Routing: 42% cheaper and 30% faster than everything on Opus 5.5.
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The part we care most about: the choice is pinned for the session.
The model never switches mid-task, so prompt caches keep working. If the classifier isn't sure, it falls back to the model you trust most.
Set up a policy: app.requesty.ai/routing-poli…
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@OpenAI's new GPT-6.1 Terra available now on Requesty!
It’s cheaper than GPT-5.6 Terra:
Input: $0.50/M (was $1.00, 50% less)
Output: $2.50/M (was $6.00, 58% less)
Cached input: $0.025/M (was $0.10, 75% less)
gpt-6.1-terra
GPT-6.1 Terra: delivering fast and capable computer use at a lower price.
GPT-6.1 Terra understands what’s on screen, takes action across apps, writes naturally, and is a more capable model for your whole workday with the efficiency to take on more of it.
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Last week Jev was the #1 model on Requesty by requests.
By tokens it was #12.
A decision model answers with a typed decision, not an essay, so a call uses ~12% of the tokens of an average LLM call.
Token leaderboards barely see them.
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@Kimi_Moonshot's K3.1 Pro and K3.1 Pro Highspeed now available on Requesty!
Call:
kimi-k3.1-pro
kimi-k3.1-pro-highspeed
Introducing Kimi K3.1 Pro: our most capable 3T-class open-weight model.
🔹 1M-token context, 2.8T total / 104B active params (MoE), Multimodal.
🔹 Full-project coding: games in Unreal, Unity & Godot.
🔹 Excels at parametric 3D modeling in Blender & CAD.
🔹 More reliable at finance work: Accurate modeling with hallucination down by 65%.
🔹 Matching the strongest proprietary models across coding, agentic and knowledge benchmarks.
Kimi K3.1 Pro and K3.1 Pro Highspeed (2–3× faster) are now live on Kimi.ai, Kimi Work, Kimi Code, and the Kimi API.
Open weights by Nov 4, 2026.
🔗 API: platform.kimi.ai
🔗 Tech blog: kimi.ai/blog/kimi-k3-1-pro
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If your AI gateway guardrails are taking seconds to respond, you're adding a ridiculous amount of unnecessary latency to every single request.
And if you're running them in production, you're paying that price constantly.
Ours, 3–18 Oct:
4 ms median
15 ms p95
Just 23 ms p95 on 64k+ token prompts.
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Your agent made 300 calls. One of them went wrong.
In a flat log, good luck.
Requesty rebuilds the run as one session: every prompt, reply and tool call in order, and any request open down to the token.
Replay a whole session: requesty.ai/product/observab…
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