Jina Embeddings v5 ranks #11 of 12 in our embedding models testing. excellent quality per parameter, on weights you may not use commercially.
58/100
a genuinely strong sub-1b multilingual model that most readers of this page cannot legally deploy — and the licence is not what its reputation suggests.
why people look for an alternative
−weights are CC-BY-NC-4.0 — commercial use prohibited without a separate licence
−v4 was non-commercial too, under a qwen research licence
−no per-token price published for the hosted api
−the v4-to-v5 split moved multimodality to a different model line
stay with Jina Embeddings v5 if 677m parameters with 32,768 tokens of context is the thing you care about most — nothing below beats it on that.
#1 in embedding models · apache-2.0, best open multilingual quality, and a tenth of a cent hosted
89/100
verdictthe best combination of open licence, multilingual quality and truncatable dimensions in the category — provided you can afford to serve an 8b model or are happy renting one.
Qwen3-Embedding-8B vs Jina Embeddings v5
Jina Embeddings v5
Qwen3-Embedding-8B
price
not published per token
$0.010 / 1m tokens on DeepInfra
free tier
yes
yes
price / 1m
not published
$0.010 hosted, free self-hosted
dimensions
1,024, truncatable to 32
4,096, truncatable to 32
context
32,768 tokens
32,768 tokens
licence
CC-BY-NC-4.0 — non-commercial
Apache-2.0
multimodal
no — v5-omni is a separate model
no — text only
switch formultilingual retrieval where the licence has to be clean and the bill has to be small
pros
+apache-2.0 on code and weights — commercially safe without conditions
+32,768-token context, four times OpenAI's or Gemini's
+matryoshka truncation from 4,096 down to 32 dimensions
+publishes MTEB scores with the board version named
+matching apache-2.0 reranker family
cons
−8b parameters — the heaviest model here to self-host
−4,096 native dimensions is a large index if you do not truncate
#2 in embedding models · one vector space for text, images, audio and video
86/100
verdictthe only model here that puts text, images, audio and video in one shared space — at the highest text price in the ranking, and it just invalidated its own predecessor's vectors.
gemini-embedding-2 vs Jina Embeddings v5
Jina Embeddings v5
gemini-embedding-2
price
not published per token
$0.20 / 1m text tokens
free tier
yes
yes
price / 1m
not published
$0.20 text, $0.10 batch
dimensions
1,024, truncatable to 32
128 to 3,072
context
32,768 tokens
8,192 tokens
licence
CC-BY-NC-4.0 — non-commercial
proprietary api
multimodal
no — v5-omni is a separate model
yes — text, image, audio, video
switch forretrieval across mixed media where text-only embeddings cannot answer the question
pros
+text, images, audio, video and documents in one shared vector space
+100+ languages, with matryoshka dimensions from 128 to 3,072
+batch mode halves the text rate to $0.10 per million
+8,192-token context, four times its own predecessor
cons
−$0.20 per million is the highest text rate in the ranking
−audio costs 32x and video 60x the text rate
−vectors are incompatible with gemini-embedding-001 — upgrading means re-embedding everything
#3 in embedding models · the default everyone reaches for, and it is showing its age
83/100
verdictstill a solid general-purpose embedding with the most flexible dimension control here — on an 8k context and a model that has not been updated since january 2024.
text-embedding-3-large vs Jina Embeddings v5
Jina Embeddings v5
text-embedding-3-large
price
not published per token
$0.13 / 1m tokens
free tier
yes
no
price / 1m
not published
$0.13
dimensions
1,024, truncatable to 32
3,072, truncatable to any size
context
32,768 tokens
8,192 tokens
licence
CC-BY-NC-4.0 — non-commercial
proprietary api
multimodal
no — v5-omni is a separate model
no — text only
switch forteams already on the OpenAI api who want a known quantity and no new vendor
pros
+arbitrary dimension truncation via the dimensions parameter, not a fixed list
+no new vendor if you are already on the OpenAI api
+extremely well documented and widely supported by every framework
+the cheap sibling, text-embedding-3-small, is $0.02 per million
cons
−8,192-token context is joint smallest in the ranking
−no replacement shipped since january 2024
−no multilingual claim, no multimodal support, no reranker sibling
−quotes an MTEB score without naming the board version
#5 in embedding models · 128k of context, text and pdfs in one space, and no published price
78/100
verdictby far the longest context here and genuine text-plus-pdf embedding — from the only vendor in this ranking that will not tell you what an api call costs.
Cohere Embed v4 vs Jina Embeddings v5
Jina Embeddings v5
Cohere Embed v4
price
not published per token
not published per token
free tier
yes
yes
price / 1m
not published
not published
dimensions
1,024, truncatable to 32
256 / 512 / 1,024 / 1,536
context
32,768 tokens
128,000 tokens
licence
CC-BY-NC-4.0 — non-commercial
proprietary api
multimodal
no — v5-omni is a separate model
yes — text, images, pdfs
switch forlong-document and pdf retrieval where a 128k window removes the chunking problem
pros
+128,000-token context — fifteen times OpenAI's, the longest here by far
+text, images and pdfs embedded into one space
+matching Rerank 3.5 and Rerank 4 models
+four selectable output dimensions from 256 to 1,536
cons
−no per-token api price published anywhere on cohere.com
−only dedicated-instance pricing is public, from $2,500 a month
−no MTEB score on its own pages, and third-party figures disagree
#10 in embedding models · a flat price, a tidy api, and almost no published specification
62/100
verdictcheaper than OpenAI and easy to adopt if you are already a Mistral customer — from a model that publishes neither its dimensions nor its benchmark scores.
Mistral Embed vs Jina Embeddings v5
Jina Embeddings v5
Mistral Embed
price
not published per token
$0.10 / 1m tokens
free tier
yes
yes
price / 1m
not published
$0.10
dimensions
1,024, truncatable to 32
not published by vendor
context
32,768 tokens
8,000 tokens
licence
CC-BY-NC-4.0 — non-commercial
proprietary api
multimodal
no — v5-omni is a separate model
no — text and code
switch forteams already building on Mistral who want embeddings on the same bill
pros
+$0.10 per million, cheaper than OpenAI's flagship
+no new vendor if you are already using the Mistral api
+flat pricing with no tiers or dimension-based surcharges
+a separate Codestral Embed exists for code retrieval
cons
−output dimensions are not published by the vendor
−no parameter count, language count or MTEB score published
−dates to december 2023 — the oldest model in this ranking
−no weights, no self-host path, no reranker sibling
every tool on this page went through the same test as Jina Embeddings v5 — same tasks, same order, scored the same way. the comparison tables are the figures from that testing, not vendor spec sheets.