Granite Embedding 278m Multilingual vs Llama 3.1 70B
At a Glance
| Compare |  Llama 3.1 70BMeta | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.106IBM watsonx.ai ↗ · Aug 29, 2026 | Not reported |
| Context windowMaximum documented tokens | 1K | 131K |
| Model facts checked | Aug 28, 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 | granite-embedding-278m-multilingual | Llama-3.1-70B |
|---|---|---|
| Developer | IBM | Meta |
| Family | Granite Embedding 278m Multilingual | Llama 3 1 70b |
| Model | granite-embedding-278m-multilingual | Llama-3.1-70B |
| Version | granite-embedding-278m-multilingual | Llama-3.1-70B |
| Lifecycle | active | active |
| Released | 2024-12-18 | 2024-07-23 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Embedding | Text |
| Context window | 1K | 131K |
| Total parameters | 278M | 70.6B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | llama3.1 |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Hugging Face (Standard), IBM watsonx.ai (Pay as you go) | Hugging Face (Standard) |
| Capabilities | embeddings | generation |
Granite Embedding 278m Multilingual Capabilities
Llama 3.1 70B Capabilities
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Primary Evidence
Sources and Freshness
Questions
Granite Embedding 278m Multilingual vs Llama 3.1 70B FAQs
Is Granite Embedding 278m Multilingual or Llama 3.1 70B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Granite Embedding 278m Multilingual and Llama 3.1 70B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Granite Embedding 278m Multilingual or Llama 3.1 70B?+
Only Granite Embedding 278m Multilingual has a directly sourced input price: $0.106 per million tokens. Neither model has a directly sourced output price in this comparison.
Which has a larger context window, Granite Embedding 278m Multilingual or Llama 3.1 70B?+
Llama 3.1 70B has the larger sourced context window. Granite Embedding 278m Multilingual supports 1K and Llama 3.1 70B supports 131K.
Which performs better in benchmarks, Granite Embedding 278m Multilingual or Llama 3.1 70B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Granite Embedding 278m Multilingual or Llama 3.1 70B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Granite Embedding 278m Multilingual is open weight; Llama 3.1 70B is open weight.
Can Granite Embedding 278m Multilingual and Llama 3.1 70B understand images?+
Granite Embedding 278m Multilingual is not documented with image input; Llama 3.1 70B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Granite Embedding 278m Multilingual or Llama 3.1 70B?+
Neither has a larger sourced maximum output. Granite Embedding 278m Multilingual is — and Llama 3.1 70B is —.
Do Granite Embedding 278m Multilingual and Llama 3.1 70B support reasoning and tool use?+
Granite Embedding 278m Multilingual: none of these features are definitively sourced. Llama 3.1 70B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Granite Embedding 278m Multilingual or Llama 3.1 70B?+
Granite Embedding 278m Multilingual has 2 sourced provider routes; Llama 3.1 70B has 1, so Granite Embedding 278m Multilingual has broader tracked availability.
Which offers better value, Granite Embedding 278m Multilingual or Llama 3.1 70B?+
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.