Olmo 3 7B Think vs Granite Embedding 278m Multilingual
At a Glance
| Compare | ||
|---|---|---|
| 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 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 | Olmo-3-7B-Think | granite-embedding-278m-multilingual |
|---|---|---|
| Developer | Ai2 | IBM |
| Family | Olmo 3 7b Think | Granite Embedding 278m Multilingual |
| Model | Olmo-3-7B-Think | granite-embedding-278m-multilingual |
| Version | Olmo-3-7B-Think | granite-embedding-278m-multilingual |
| Lifecycle | active | active |
| Released | Unknown | 2024-12-18 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Embedding |
| Context window | 66K | 1K |
| Total parameters | 7.3B | 278M |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Unknown | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Hugging Face (Standard), IBM watsonx.ai (Pay as you go) |
| Capabilities | chat, generation, reasoning | embeddings |
Olmo 3 7B Think Capabilities
Granite Embedding 278m Multilingual Capabilities
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Primary Evidence
Sources and Freshness
Questions
Olmo 3 7B Think vs Granite Embedding 278m Multilingual FAQs
Is Olmo 3 7B Think or Granite Embedding 278m Multilingual better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Olmo 3 7B Think and Granite Embedding 278m Multilingual, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Olmo 3 7B Think or Granite Embedding 278m Multilingual?+
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, Olmo 3 7B Think or Granite Embedding 278m Multilingual?+
Olmo 3 7B Think has the larger sourced context window. Olmo 3 7B Think supports 66K and Granite Embedding 278m Multilingual supports 1K.
Which performs better in benchmarks, Olmo 3 7B Think 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 Olmo 3 7B Think or Granite Embedding 278m Multilingual be self-hosted?+
Both models have the same recorded self-hosting status: supported. Olmo 3 7B Think is open weight; Granite Embedding 278m Multilingual is open weight.
Can Olmo 3 7B Think and Granite Embedding 278m Multilingual understand images?+
Olmo 3 7B Think 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, Olmo 3 7B Think or Granite Embedding 278m Multilingual?+
Neither has a larger sourced maximum output. Olmo 3 7B Think is 33K and Granite Embedding 278m Multilingual is —.
Do Olmo 3 7B Think and Granite Embedding 278m Multilingual support reasoning and tool use?+
Olmo 3 7B Think: reasoning. 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, Olmo 3 7B Think or Granite Embedding 278m Multilingual?+
Olmo 3 7B Think has 0 sourced provider routes; Granite Embedding 278m Multilingual has 2, so Granite Embedding 278m Multilingual has broader tracked availability.
Which offers better value, Olmo 3 7B Think 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.