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polyBERT

cooperzvegintzov

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Embeddings & similarity
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
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Source / docs ↗

Sizes are MiB (what the file browser above reports). Auxiliary files: `multilingual-e5-small.imatrix` (131 KiB, the importance matrix used for the imatrix/IQ quants) and `multilingual-e5-small-imatrix-ab.txt` (the A/B notes from producing it). **Recommended: Q8_0.** It is within 0.0001 cosine of the F32 reference while being 3.6× smaller, so F32 buys nothing you can measure. Drop to a 4-bit quant only when you are memory-bound: they save a further 11 MiB — about 9% — and Q4_K gives up an order of magnitude more accuracy (0.990) to do it. At this model size the 4-bit tier is rarely the right trade; it matters much more on the larger models in this registry. CrispEmbed server supports four API dialects: - `POST /embed` — native - `POST /v1/embeddings` — OpenAI-compatible - `POST /api/embed` — Ollama-compatible - `POST /api/embeddings` — Ollama legacy

Embeddings & similarity
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
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1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

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How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

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Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

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