Agnes
Agnes 2.5 Pro Beta
Agnes 2.5 Pro Beta frontier model with improved reasoning, speed and instruction following.
Intelligenza
42.5 #428
AAQI 46.5
Velocità Uscita
65 t/s
TTFT 320 ms · ITL 15.4 ms/tok
Prezzo combinato (3:1)
$1.5 /1M
Input $1/M · Output $3/M
Contesto
131K
≈ 328 pagine A4 · output max 8,192
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Agnes 2.5 Pro Beta
Claude Mythos Preview (#1)
Average (68)
Gli assi radar sono approssimati dai punteggi di categoria dove mancano misurazioni dirette — vedi Metodologia.
Punti di forza e compromessi
Dove vince
- Sub-400ms time-to-first-token
- Cheap prompt-cache reads at $0.1/1M
A cosa fare attenzione
- Architecture and weights undisclosed — API access only
Casi d'uso killer
Deep research
Synthesis across broad knowledge domains with citation-grade recall.
Math & proof work
Step-verified reasoning for olympiad math, theorem proving and formal verification.
High-volume batch jobs
Classification, extraction and summarization at minimal cost per call.
Quickstart per sviluppatori e API
Imposta prima
OPENROUTER_API_KEY — ottieni una chiave APIcurl https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "content-type: application/json" \
-d '{
"model": "agnes/agnes-2-5-pro-beta",
"messages": [{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=os.environ["OPENROUTER_API_KEY"],
)
resp = client.chat.completions.create(
model="agnes/agnes-2-5-pro-beta",
messages=[{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}],
)
print(resp.choices[0].message.content)const res = await fetch("https://openrouter.ai/api/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.OPENROUTER_API_KEY}`,
"content-type": "application/json",
},
body: JSON.stringify({
model: "agnes/agnes-2-5-pro-beta",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);OpenRouter-compatible endpoint — one API for 300+ models.
Risorse e link ufficiali
Deployment API
Confronta host e latenza
Throughput, TTFT e prezzi cache su Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock e Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Punteggi per Fonte
Punteggi per Categoria
Risultati dei Benchmark
Specifiche live e architettura
Sotto il cofano
- Prompt caching supported — cached input reads at $0.1/1M tokens.
Data di Rilascio
15 feb 2025
Parametri
Not disclosed
Licenza
Proprietary
Finestra di Contesto
131,000 tokens
Output max
8,192 tokens
Modalità
text
Prezzi cache
lettura $0.1 · scrittura $1.25 /1M
Ragionamento
—
Prezzo Ingresso
$1/M
Prezzo Uscita
$3/M
Velocità Uscita
65 t/s
Tempo al Primo Token
320 ms
Dati aggiornati
28 ago 2026
Stima bolletta mensile
Spesa mensile stimata
$0.00
Calcolo risparmio vs GPT-4o…
Cita e incorpora
Badge Markdown GitHub
Loading badge markdown...BibTeX
@misc{llmpodium2026agnes25probeta,
title = {Benchmark Analysis and Intelligence Evaluation of Agnes 2.5 Pro Beta},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/agnes-2-5-pro-beta}
}Incorpora il badge del punteggio
Aggiungi un badge Podium Score live al tuo README su GitHub o Hugging Face — rimanda alla classifica live.
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