Granite Embedding 30m English vs Llama 3.1 8B Instruct
At a Glance
| Compare | ||
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.050Openrouter ↗ · Sep 23, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.080Openrouter ↗ · Sep 23, 2026 |
| 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-30m-english | Llama-3.1-8B-Instruct |
|---|---|---|
| Developer | IBM | Meta |
| Family | Granite Embedding 30m English | Llama 3 1 8b Instruct |
| Model | granite-embedding-30m-english | Llama-3.1-8B-Instruct |
| Version | granite-embedding-30m-english | Llama-3.1-8B-Instruct |
| Lifecycle | active | active |
| Released | 2025-08-29 | 2024-07-23 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Embedding | Text |
| Context window | 1K | 131K |
| Total parameters | 30.3M | 8B |
| 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) | Hugging Face (Standard), Openrouter (Standard) |
| Capabilities | embeddings | chat, generation, tools |
Granite Embedding 30m English Capabilities
Llama 3.1 8B Instruct Capabilities
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Primary Evidence
Sources and Freshness
Questions
Granite Embedding 30m English vs Llama 3.1 8B Instruct FAQs
Is Granite Embedding 30m English or Llama 3.1 8B Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Granite Embedding 30m English and Llama 3.1 8B Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Granite Embedding 30m English or Llama 3.1 8B Instruct?+
Only Llama 3.1 8B Instruct has a directly sourced input price: $0.050 per million tokens. Only Llama 3.1 8B Instruct has a directly sourced output price: $0.080 per million tokens.
Which has a larger context window, Granite Embedding 30m English or Llama 3.1 8B Instruct?+
Llama 3.1 8B Instruct has the larger sourced context window. Granite Embedding 30m English supports 1K and Llama 3.1 8B Instruct supports 131K.
Which performs better in benchmarks, Granite Embedding 30m English or Llama 3.1 8B Instruct?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Granite Embedding 30m English or Llama 3.1 8B Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. Granite Embedding 30m English is open weight; Llama 3.1 8B Instruct is open weight.
Can Granite Embedding 30m English and Llama 3.1 8B Instruct understand images?+
Granite Embedding 30m English is not documented with image input; Llama 3.1 8B Instruct is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Granite Embedding 30m English or Llama 3.1 8B Instruct?+
Neither has a larger sourced maximum output. Granite Embedding 30m English is — and Llama 3.1 8B Instruct is —.
Do Granite Embedding 30m English and Llama 3.1 8B Instruct support reasoning and tool use?+
Granite Embedding 30m English: none of these features are definitively sourced. Llama 3.1 8B Instruct: tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Granite Embedding 30m English or Llama 3.1 8B Instruct?+
Granite Embedding 30m English has 1 sourced provider route; Llama 3.1 8B Instruct has 2, so Llama 3.1 8B Instruct has broader tracked availability.
Which offers better value, Granite Embedding 30m English or Llama 3.1 8B Instruct?+
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.