ERNIE X1.1 vs Granite Embedding 278m Multilingual
At a Glance
| Compare |  ERNIE X1.1Baidu | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.106IBM watsonx.ai ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 66K | 1K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | ERNIE X1.1 | granite-embedding-278m-multilingual |
|---|---|---|
| Developer | Baidu | IBM |
| Family | Ernie X1 | Granite Embedding 278m Multilingual |
| Model | ERNIE X1.1 | granite-embedding-278m-multilingual |
| Version | ERNIE X1.1 | granite-embedding-278m-multilingual |
| Lifecycle | active | active |
| Released | 2025-09-26 | 2024-12-18 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Embedding |
| Context window | 66K | 1K |
| Total parameters | Unknown | 278M |
| Active parameters | Unknown | Unknown |
| License | Unknown | apache-2.0 |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Baidu Qianfan (Standard) | Hugging Face (Standard), IBM watsonx.ai (Pay as you go) |
| Capabilities | agents, chat, reasoning, search, tools | embeddings |
ERNIE X1.1 Capabilities
Granite Embedding 278m Multilingual Capabilities
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Primary Evidence
Sources and Freshness
Questions
ERNIE X1.1 vs Granite Embedding 278m Multilingual FAQs
Is ERNIE X1.1 or Granite Embedding 278m Multilingual better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both ERNIE X1.1 and Granite Embedding 278m Multilingual, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, ERNIE X1.1 or Granite Embedding 278m Multilingual?+
ERNIE X1.1 is $1.00 and Granite Embedding 278m Multilingual is $0.106 per million tokens, so Granite Embedding 278m Multilingual is cheaper on this metric. Only ERNIE X1.1 has a directly sourced output price: $4.00 per million tokens.
Which has a larger context window, ERNIE X1.1 or Granite Embedding 278m Multilingual?+
ERNIE X1.1 has the larger sourced context window. ERNIE X1.1 supports 66K and Granite Embedding 278m Multilingual supports 1K.
Which performs better in benchmarks, ERNIE X1.1 or Granite Embedding 278m Multilingual?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can ERNIE X1.1 or Granite Embedding 278m Multilingual be self-hosted?+
Granite Embedding 278m Multilingual is the only model in this pair currently marked as self-hostable. ERNIE X1.1 is not marked open weight; Granite Embedding 278m Multilingual is open weight.
Can ERNIE X1.1 and Granite Embedding 278m Multilingual understand images?+
ERNIE X1.1 is not documented with image input; Granite Embedding 278m Multilingual is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, ERNIE X1.1 or Granite Embedding 278m Multilingual?+
Neither has a larger sourced maximum output. ERNIE X1.1 is 66K and Granite Embedding 278m Multilingual is —.
Do ERNIE X1.1 and Granite Embedding 278m Multilingual support reasoning and tool use?+
ERNIE X1.1: reasoning and tool calling. Granite Embedding 278m Multilingual: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, ERNIE X1.1 or Granite Embedding 278m Multilingual?+
ERNIE X1.1 has 1 sourced provider route; Granite Embedding 278m Multilingual has 2, so Granite Embedding 278m Multilingual has broader tracked availability.
Which offers better value, ERNIE X1.1 or Granite Embedding 278m Multilingual?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.