Granite Embedding 30m English vs Mistral Large 3
At a Glance
| Compare | Mistral Large 3Mistral AI | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.25Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.75Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 1K | 262K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 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 | Mistral Large 3 |
|---|---|---|
| Developer | IBM | Mistral AI |
| Family | Granite Embedding 30m English | Mistral Large 3 |
| Model | granite-embedding-30m-english | Mistral Large 3 |
| Version | granite-embedding-30m-english | Mistral Large 3 |
| Lifecycle | active | active |
| Released | 2025-08-29 | 2025-12-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Document |
| Output modalities | Embedding | Text |
| Context window | 1K | 262K |
| Total parameters | 30.3M | 675B |
| Active parameters | Unknown | 41B |
| License | apache-2.0 | Apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Hugging Face (Standard) | Mistral AI (Standard), Openrouter (Standard) |
| Capabilities | embeddings | agents, chat, generation, structured_outputs, tools, vision |
Granite Embedding 30m English Capabilities
Mistral Large 3 Capabilities
Primary Evidence
Sources and Freshness
Questions
Granite Embedding 30m English vs Mistral Large 3 FAQs
Is Granite Embedding 30m English or Mistral Large 3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Granite Embedding 30m English and Mistral Large 3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Granite Embedding 30m English or Mistral Large 3?+
Only Mistral Large 3 has a directly sourced input price: $0.25 per million tokens. Only Mistral Large 3 has a directly sourced output price: $0.75 per million tokens.
Which has a larger context window, Granite Embedding 30m English or Mistral Large 3?+
Mistral Large 3 has the larger sourced context window. Granite Embedding 30m English supports 1K and Mistral Large 3 supports 262K.
Which performs better in benchmarks, Granite Embedding 30m English or Mistral Large 3?+
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 Mistral Large 3 be self-hosted?+
Both models have the same recorded self-hosting status: supported. Granite Embedding 30m English is open weight; Mistral Large 3 is open weight.
Can Granite Embedding 30m English and Mistral Large 3 understand images?+
Granite Embedding 30m English is not documented with image input; Mistral Large 3 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Granite Embedding 30m English or Mistral Large 3?+
Neither has a larger sourced maximum output. Granite Embedding 30m English is — and Mistral Large 3 is —.
Do Granite Embedding 30m English and Mistral Large 3 support reasoning and tool use?+
Granite Embedding 30m English: none of these features are definitively sourced. Mistral Large 3: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Granite Embedding 30m English or Mistral Large 3?+
Granite Embedding 30m English has 1 sourced provider route; Mistral Large 3 has 2, so Mistral Large 3 has broader tracked availability.
Which offers better value, Granite Embedding 30m English or Mistral Large 3?+
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.