Granite Embedding 30m English vs MiMo V2.6 Pro
At a Glance
| Compare |  MiMo V2.6 ProXiaomi | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.435Openrouter ↗ · Sep 23, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.87Openrouter ↗ · Sep 23, 2026 |
| Context windowMaximum documented tokens | 1K | 1,049K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 22, 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 | MiMo-V2.6-Pro |
|---|---|---|
| Developer | IBM | Xiaomi |
| Family | Granite Embedding 30m English | Mimo V2 6 |
| Model | granite-embedding-30m-english | MiMo-V2.6-Pro |
| Version | granite-embedding-30m-english | MiMo-V2.6-Pro |
| Lifecycle | active | active |
| Released | 2025-08-29 | 2026-09-21 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Video, Audio |
| Output modalities | Embedding | Text |
| Context window | 1K | 1,049K |
| Total parameters | 30.3M | 1T |
| Active parameters | Unknown | 42B |
| License | apache-2.0 | MIT |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Hugging Face (Standard) | Openrouter (Standard), Xiaomi MiMo (Standard) |
| Capabilities | embeddings | chat, generation, reasoning, tools |
Granite Embedding 30m English Capabilities
MiMo V2.6 Pro Capabilities
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Primary Evidence
Sources and Freshness
Questions
Granite Embedding 30m English vs MiMo V2.6 Pro FAQs
Is Granite Embedding 30m English or MiMo V2.6 Pro better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Granite Embedding 30m English and MiMo V2.6 Pro, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Granite Embedding 30m English or MiMo V2.6 Pro?+
Only MiMo V2.6 Pro has a directly sourced input price: $0.435 per million tokens. Only MiMo V2.6 Pro has a directly sourced output price: $0.87 per million tokens.
Which has a larger context window, Granite Embedding 30m English or MiMo V2.6 Pro?+
MiMo V2.6 Pro has the larger sourced context window. Granite Embedding 30m English supports 1K and MiMo V2.6 Pro supports 1,049K.
Which performs better in benchmarks, Granite Embedding 30m English or MiMo V2.6 Pro?+
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 MiMo V2.6 Pro be self-hosted?+
Both models have the same recorded self-hosting status: supported. Granite Embedding 30m English is open weight; MiMo V2.6 Pro is open weight.
Can Granite Embedding 30m English and MiMo V2.6 Pro understand images?+
Granite Embedding 30m English is not documented with image input; MiMo V2.6 Pro is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Granite Embedding 30m English or MiMo V2.6 Pro?+
Neither has a larger sourced maximum output. Granite Embedding 30m English is — and MiMo V2.6 Pro is 131K.
Do Granite Embedding 30m English and MiMo V2.6 Pro support reasoning and tool use?+
Granite Embedding 30m English: none of these features are definitively sourced. MiMo V2.6 Pro: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Granite Embedding 30m English or MiMo V2.6 Pro?+
Granite Embedding 30m English has 1 sourced provider route; MiMo V2.6 Pro has 2, so MiMo V2.6 Pro has broader tracked availability.
Which offers better value, Granite Embedding 30m English or MiMo V2.6 Pro?+
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.