Granite Embedding 30m English vs Kimi K2 Instruct
At a Glance
| Compare |  Kimi K2 InstructMoonshot AI | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.57Openrouter ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $2.30Openrouter ↗ · Aug 29, 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 | Kimi-K2-Instruct |
|---|---|---|
| Developer | IBM | Moonshot AI |
| Family | Granite Embedding 30m English | Kimi K2 Instruct |
| Model | granite-embedding-30m-english | Kimi-K2-Instruct |
| Version | granite-embedding-30m-english | Kimi-K2-Instruct |
| Lifecycle | active | active |
| Released | 2025-08-29 | 2025-07-11 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Embedding | Text |
| Context window | 1K | 131K |
| Total parameters | 30.3M | 1T |
| Active parameters | Unknown | 32B |
| License | apache-2.0 | other |
| 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
Kimi K2 Instruct Capabilities
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Primary Evidence
Sources and Freshness
Questions
Granite Embedding 30m English vs Kimi K2 Instruct FAQs
Is Granite Embedding 30m English or Kimi K2 Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Granite Embedding 30m English and Kimi K2 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 Kimi K2 Instruct?+
Only Kimi K2 Instruct has a directly sourced input price: $0.57 per million tokens. Only Kimi K2 Instruct has a directly sourced output price: $2.30 per million tokens.
Which has a larger context window, Granite Embedding 30m English or Kimi K2 Instruct?+
Kimi K2 Instruct has the larger sourced context window. Granite Embedding 30m English supports 1K and Kimi K2 Instruct supports 131K.
Which performs better in benchmarks, Granite Embedding 30m English or Kimi K2 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 Kimi K2 Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. Granite Embedding 30m English is open weight; Kimi K2 Instruct is open weight.
Can Granite Embedding 30m English and Kimi K2 Instruct understand images?+
Granite Embedding 30m English is not documented with image input; Kimi K2 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 Kimi K2 Instruct?+
Neither has a larger sourced maximum output. Granite Embedding 30m English is — and Kimi K2 Instruct is —.
Do Granite Embedding 30m English and Kimi K2 Instruct support reasoning and tool use?+
Granite Embedding 30m English: none of these features are definitively sourced. Kimi K2 Instruct: tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Granite Embedding 30m English or Kimi K2 Instruct?+
Granite Embedding 30m English has 1 sourced provider route; Kimi K2 Instruct has 2, so Kimi K2 Instruct has broader tracked availability.
Which offers better value, Granite Embedding 30m English or Kimi K2 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.