Granite Embedding English r2 vs Qwen3.7 Plus
At a Glance
| Compare | Qwen3.7 PlusQwen | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.32Openrouter ↗ · Sep 23, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $1.28Openrouter ↗ · Sep 23, 2026 |
| Context windowMaximum documented tokens | 8K | 1,000K |
| 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-english-r2 | Qwen3.7-Plus |
|---|---|---|
| Developer | IBM | Qwen |
| Family | Granite Embedding English R2 | Qwen3 7 Plus |
| Model | granite-embedding-english-r2 | Qwen3.7-Plus |
| Version | granite-embedding-english-r2 | Qwen3.7-Plus |
| Lifecycle | active | active |
| Released | 2025-08-15 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Video |
| Output modalities | Embedding | Text |
| Context window | 8K | 1,000K |
| Total parameters | 149M | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | Unknown | Yes |
| Self-hostable | Yes | No |
| Provider access | Unknown | Alibaba Cloud Model Studio (Standard), Fireworks Ai (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | embeddings | agents, chat, computer-use, reasoning, structured_outputs, tools, vision |
Granite Embedding English r2 Capabilities
Qwen3.7 Plus Capabilities
Primary Evidence
Sources and Freshness
Questions
Granite Embedding English r2 vs Qwen3.7 Plus FAQs
Is Granite Embedding English r2 or Qwen3.7 Plus better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Granite Embedding English r2 and Qwen3.7 Plus, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Granite Embedding English r2 or Qwen3.7 Plus?+
Only Qwen3.7 Plus has a directly sourced input price: $0.32 per million tokens. Only Qwen3.7 Plus has a directly sourced output price: $1.28 per million tokens.
Which has a larger context window, Granite Embedding English r2 or Qwen3.7 Plus?+
Qwen3.7 Plus has the larger sourced context window. Granite Embedding English r2 supports 8K and Qwen3.7 Plus supports 1,000K.
Which performs better in benchmarks, Granite Embedding English r2 or Qwen3.7 Plus?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Granite Embedding English r2 or Qwen3.7 Plus be self-hosted?+
Granite Embedding English r2 is the only model in this pair currently marked as self-hostable. Granite Embedding English r2 is open weight; Qwen3.7 Plus is not marked open weight.
Can Granite Embedding English r2 and Qwen3.7 Plus understand images?+
Granite Embedding English r2 is not documented with image input; Qwen3.7 Plus is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Granite Embedding English r2 or Qwen3.7 Plus?+
Neither has a larger sourced maximum output. Granite Embedding English r2 is — and Qwen3.7 Plus is 131K.
Do Granite Embedding English r2 and Qwen3.7 Plus support reasoning and tool use?+
Granite Embedding English r2: none of these features are definitively sourced. Qwen3.7 Plus: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Granite Embedding English r2 or Qwen3.7 Plus?+
Granite Embedding English r2 has 0 sourced provider routes; Qwen3.7 Plus has 4, so Qwen3.7 Plus has broader tracked availability.
Which offers better value, Granite Embedding English r2 or Qwen3.7 Plus?+
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.