Qwen3.5 397B A17B vs Granite Embedding English r2
At a Glance
| Compare | ||
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.45Deepinfra ↗ · Sep 23, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $3.00Deepinfra ↗ · Sep 23, 2026 | Not reported |
| Context windowMaximum documented tokens | 262K | 8K |
| 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 | Qwen3.5-397B-A17B | granite-embedding-english-r2 |
|---|---|---|
| Developer | Qwen | IBM |
| Family | Qwen3 5 397b A17b | Granite Embedding English R2 |
| Model | Qwen3.5-397B-A17B | granite-embedding-english-r2 |
| Version | Qwen3.5-397B-A17B | granite-embedding-english-r2 |
| Lifecycle | active | active |
| Released | 2026-02-15 | 2025-08-15 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Embedding |
| Context window | 262K | 8K |
| Total parameters | 403.4B | 149M |
| Active parameters | 17B | Unknown |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | Unknown |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, tools | embeddings |
Qwen3.5 397B A17B Capabilities
Granite Embedding English r2 Capabilities
Primary Evidence
Sources and Freshness
Questions
Qwen3.5 397B A17B vs Granite Embedding English r2 FAQs
Is Qwen3.5 397B A17B or Granite Embedding English r2 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5 397B A17B and Granite Embedding English r2, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.5 397B A17B or Granite Embedding English r2?+
Only Qwen3.5 397B A17B has a directly sourced input price: $0.45 per million tokens. Only Qwen3.5 397B A17B has a directly sourced output price: $3.00 per million tokens.
Which has a larger context window, Qwen3.5 397B A17B or Granite Embedding English r2?+
Qwen3.5 397B A17B has the larger sourced context window. Qwen3.5 397B A17B supports 262K and Granite Embedding English r2 supports 8K.
Which performs better in benchmarks, Qwen3.5 397B A17B or Granite Embedding English r2?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Qwen3.5 397B A17B or Granite Embedding English r2 be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3.5 397B A17B is open weight; Granite Embedding English r2 is open weight.
Can Qwen3.5 397B A17B and Granite Embedding English r2 understand images?+
Qwen3.5 397B A17B is documented with image input; Granite Embedding English r2 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.5 397B A17B or Granite Embedding English r2?+
Neither has a larger sourced maximum output. Qwen3.5 397B A17B is — and Granite Embedding English r2 is —.
Do Qwen3.5 397B A17B and Granite Embedding English r2 support reasoning and tool use?+
Qwen3.5 397B A17B: reasoning, tool calling, and image input. Granite Embedding English r2: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Qwen3.5 397B A17B or Granite Embedding English r2?+
Qwen3.5 397B A17B has 4 sourced provider routes; Granite Embedding English r2 has 0, so Qwen3.5 397B A17B has broader tracked availability.
Which offers better value, Qwen3.5 397B A17B or Granite Embedding English r2?+
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.