Independent
Apodex 1.1Open
Specialized open-weight coding and reasoning model based on optimized architecture.
Intelligence
0.0 #765
Top 100% tier
Output Speed
145 t/s
ITL 6.9 ms/tok
Blended price (3:1)
$0.35 /1M
In $0.2/M · Out $0.8/M
Context
128K
≈ 328 A4 pages · max out 16,384
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Apodex 1.1
Claude Mythos Preview (#1)
Average (68)
Radar axes are approximated from category scores where direct benchmark measurements are missing — see Methodology.
Strengths & Trade-offs
Where it wins
- Open weights — self-host, fine-tune and audit with no vendor lock-in
- 145 tok/s output — fast enough for real-time agents
- Very low blended price — $0.35/1M mixed tokens
- Cheap prompt-cache reads at $0.02/1M
Killer Use Cases
SWE-bench agents
Issue-to-PR resolution across multi-file repos — refactor, test and verify autonomously.
Developer Quickstart & API
Set
HF_TOKEN first — get an API keycurl https://router.huggingface.co/v1/chat/completions \
-H "Authorization: Bearer $HF_TOKEN" \
-H "content-type: application/json" \
-d '{
"model": "apodex-1-1",
"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://router.huggingface.co/v1",
api_key=os.environ["HF_TOKEN"],
)
resp = client.chat.completions.create(
model="apodex-1-1",
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://router.huggingface.co/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.HF_TOKEN}`,
"content-type": "application/json",
},
body: JSON.stringify({
model: "apodex-1-1",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);Official Resources & Links
API deployment
Compare hosts & latency
Throughput, TTFT and cache pricing across Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock and Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Scores by Source
Category Scores
Benchmark Results
Live Specs & Architecture
Under the hood
Dense · 72B
- Prompt caching supported — cached input reads at $0.02/1M tokens.
Release date
Jul 20, 2026
Architecture
Dense · 72B
Parameters
72B
License
Open Weights
Context Window
131,072 tokens
Max Output
16,384 tokens
Modalities
text
Cache Pricing
read $0.02 · write $0.25 /1M
Reasoning
—
Input Price
$0.2/M
Output Price
$0.8/M
Output Speed
145 t/s
Time to First Token
—
Data Updated
Oct 26, 2026
Estimated monthly bill
Estimated monthly spend
$0.00
Calculating savings vs GPT-4o...
Cite & embed
GitHub Markdown badge
Loading badge markdown...BibTeX
@misc{llmpodium2026apodex11,
title = {Benchmark Analysis and Intelligence Evaluation of Apodex 1.1},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/apodex-1-1}
}Embed Score Badge
Add a live Podium Score badge to your GitHub or Hugging Face README — it links back to the live ranking.
Preview
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