IX2-0193
① SA Source
- Source: 開啟完整 SA 文章
- Section:
Unpacking Inference Providers’ Unit Economics - Line hint:
340
Context Before
Subscribed
Of course, these assumptions may not be _exactly _correct and these calculations don’t account for downtime or underutilization, but this gives an idea of some cool math you can do with InferenceX data. More analysis on the economics of inference can be found in the SemiAnalysis Tokenomics Model ↗.
Evidence
The OpenRouter data also shows Nebius AI Studio (Fast) serving DeepSeek FP4 at 167 tok/sec/user at 6/M output tokens
Context After
② Atomic Claim
OpenRouter 資料也顯示，Nebius AI Studio（Fast）以 167 tok/sec/user 提供 DeepSeek FP4，input token 每百萬 2 美元、output token 每百萬 6 美元。
- Epistemic Mode:
ASSERTED - Mapping Status:
COMPLETE
③ Semantic Frame
{
"frame_type": "NARY_RELATION",
"participants": [
{
"node": {
"id": "02_companies/OpenRouter",
"label": "OpenRouter"
},
"role": "user_or_subject"
},
{
"node": {
"id": "02_companies/Nebius",
"label": "Nebius"
},
"role": "used_entity"
},
{
"node": {
"id": "02_companies/DeepSeek",
"label": "DeepSeek"
},
"role": "used_entity"
},
{
"node": {
"id": "04_knowledge_base/FP4",
"label": "FP4"
},
"role": "used_entity"
}
],
"qualifiers": {
"condition_text": null,
"numeric_mentions": [
"167 tok/s",
"2",
"6"
],
"temporal_mentions": []
},
"relation_type": "USES"
}④ Canonical Entity Mapping
| Role | Surface Label | Canonical Target |
|---|---|---|
| user_or_subject | OpenRouter | OpenRouter |
| used_entity | Nebius | Nebius |
| used_entity | DeepSeek | DeepSeek |
| used_entity | FP4 | FP4 |
⑤ Human Review
請在 Properties 逐項確認：
- 原文 → Atomic Claim 是否忠實
- Atomic Claim → Semantic Frame 是否忠實
- Canonical Entity mapping 是否正確
- Epistemic mode 是否保留原文語氣
- 最後選擇
review_action
Review state
Markdown 內文不是正式 approval。只有 Apply bridge 寫入的 Decision Ledger event 才是正式決策。