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DeepSeek R1 Distill Qwen 7B vs DeepSeek-R1
Stavite dva do četiri modela jedan pored drugoga na poljima koja odlučuju o implementaciji: kontekst, cijena, regija, licenca, jezici, hosting i što je verificirano.
| DeepSeek R1 Distill Qwen 7B | DeepSeek-R1 | |
|---|---|---|
| Pružatelj | DeepSeek | DeepSeek |
| Parametri | 7.6B | 684.49B |
| Prozor konteksta | Nije provjereno: pružatelj nije dokumentirao ovu tvrdnju. | 131k |
| Licenca | mit | mit |
| Modaliteti | text | text |
| Datum izdanja | 20. sij 2025. | 20. sij 2025. |
| Jezici | Nije provjereno: pružatelj nije dokumentirao ovu tvrdnju. | Jako: en, zh Zadovoljavajuće: de, fr, es, it, pt, ru, ja, ko |
| Otvoreni utezi | da | da |
| Zadaci | Zaključivanje, Niska cijena | Zaključivanje, Kodiranje, Sažimanje |
Kada se dva modela razlikuju, ova tablica pokazuje razliku i tu staje. LLM EU ne objavljuje značku pobjednika bez dokumentiranog rezultata za taj zadatak, a brojka koja nedostaje ostaje nedostajuća umjesto da postane nula.
Kada ga koristiti
DeepSeek R1 Distill Qwen 7B
Prednosti
- open weights are published on Hugging Face and can be downloaded without a vendor agreement
- mit licence, so commercial self-hosting is permitted without a separate agreement
- at 7.6B parameters it fits on a single accelerator
Ograničenja
- catalogue only — LLM EU does not run this model, and nothing on this card implies an LLM EU endpoint
- no evaluation has been run by LLM EU
- the published configuration records no context window, so the card carries none
- at 7.6B parameters, the weights fit on a single modest accelerator
Zadaci
DeepSeek-R1
Prednosti
- MIT licence for a 671B-parameter reasoning model
- published reasoning traces in the base training recipe
- strong maths scores for a January 2025 release
Ograničenja
- 671B parameters, so self-hosting is expensive
- reasoning output is verbose and raises token cost
- superseded by DeepSeek-V3.2 and V4-Flash for most workloads
Zadaci
Zadaci: Zaključivanje
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