Qwen3-Embedding-8B alternatives

11 tools we tested head to head against Qwen3-Embedding-8B, ranked — and what each one actually does differently.

last reviewed 21 aug 2026 · from our best 12 embedding models ·list curated by Onur Ozcanx in

first — what you'd be leaving

Qwen3-Embedding-8B ranks #1 of 12 in our embedding models testing. apache-2.0, best open multilingual quality, and a tenth of a cent hosted.

89/100

the 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.

why people look for an alternative

−8b parameters — the heaviest model here to self-host
−4,096 native dimensions is a large index if you do not truncate
−text only, no multimodal path

stay with Qwen3-Embedding-8B if apache-2.0 on code and weights — commercially safe without conditions is the thing you care about most — nothing below beats it on that.

the short version

best alternativegemini-embedding-2retrieval across mixed media where text-only embeddings cannot answer the question86/100 best free optionStellaenglish-only retrieval where you want mit weights and full control of index size68/100

gemini-embedding-2

#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 Qwen3-Embedding-8B

	Qwen3-Embedding-8B	gemini-embedding-2
price	$0.010 / 1m tokens on DeepInfra	$0.20 / 1m text tokens
free tier	yes	yes
price / 1m	$0.010 hosted, free self-hosted	$0.20 text, $0.10 batch
dimensions	4,096, truncatable to 32	128 to 3,072
context	32,768 tokens	8,192 tokens
licence	Apache-2.0	proprietary api
multimodal	no — text only	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
−no MTEB score published for this version yet

visit gemini-embedding-2 →full gemini-embedding-2 review →

text-embedding-3-large

#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 Qwen3-Embedding-8B

	Qwen3-Embedding-8B	text-embedding-3-large
price	$0.010 / 1m tokens on DeepInfra	$0.13 / 1m tokens
free tier	yes	no
price / 1m	$0.010 hosted, free self-hosted	$0.13
dimensions	4,096, truncatable to 32	3,072, truncatable to any size
context	32,768 tokens	8,192 tokens
licence	Apache-2.0	proprietary api
multimodal	no — text only	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

visit text-embedding-3-large →full text-embedding-3-large review →

voyage-4-large

#4 in embedding models · cheaper than OpenAI, four times the context, and a reranker to match

82/100

verdictbeats the default on price, context and reranking, from a smaller vendor that publishes less about how it performs.

voyage-4-large vs Qwen3-Embedding-8B

	Qwen3-Embedding-8B	voyage-4-large
price	$0.010 / 1m tokens on DeepInfra	$0.12 / 1m tokens
free tier	yes	yes
price / 1m	$0.010 hosted, free self-hosted	$0.12
dimensions	4,096, truncatable to 32	1,024 default; 256 to 2,048
context	32,768 tokens	32,000 tokens
licence	Apache-2.0	proprietary api
multimodal	no — text only	unconfirmed for this model

switch forretrieval-focused teams who want a matched embedding and reranking pair from one vendor

pros

+$0.12 per million — cheaper than OpenAI and it cut price on the version bump
+32,000-token context, four times OpenAI's
+matched rerankers at $0.05 and $0.02 per million
+dimension options are cross-compatible within the 4 series

cons

−no MTEB score published on its own pages
−no language count or parameter count disclosed
−multimodal support for this specific model is unconfirmed
−smaller vendor than the three above it

visit voyage-4-large →full voyage-4-large review →

Cohere Embed v4

#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 Qwen3-Embedding-8B

	Qwen3-Embedding-8B	Cohere Embed v4
price	$0.010 / 1m tokens on DeepInfra	not published per token
free tier	yes	yes
price / 1m	$0.010 hosted, free self-hosted	not published
dimensions	4,096, truncatable to 32	256 / 512 / 1,024 / 1,536
context	32,768 tokens	128,000 tokens
licence	Apache-2.0	proprietary api
multimodal	no — text only	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
−no language count stated for v4.0 specifically

visit Cohere Embed v4 →full Cohere Embed v4 review →

EmbeddingGemma

#6 in embedding models · the cheapest way to embed anything, if the gemma terms suit you

75/100

verdict300m parameters that run on a laptop at a fiftieth of Gemini's price — held back by a licence that is commercially usable but not open source.

EmbeddingGemma vs Qwen3-Embedding-8B

	Qwen3-Embedding-8B	EmbeddingGemma
price	$0.010 / 1m tokens on DeepInfra	$0.002 / 1m tokens on DeepInfra
free tier	yes	yes
price / 1m	$0.010 hosted, free self-hosted	$0.002 hosted, free self-hosted
dimensions	4,096, truncatable to 32	768, truncatable to 128
context	32,768 tokens	2,048 tokens
licence	Apache-2.0	Gemma Terms of Use
multimodal	no — text only	no — text only

switch foron-device and high-volume embedding where size and cost dominate everything else

pros

+$0.002 per million hosted — the cheapest rate in the ranking
+300m parameters, genuinely runs on a laptop or phone
+matryoshka truncation to 512, 256 or 128 dimensions
+publishes MTEB scores with the board version named

cons

−Gemma Terms of Use, not an osi licence — prohibited-use policy attached
−redistribution obliges you to pass the same restrictions downstream
−2,048-token context is among the shortest here
−quality sits below the larger open models, as expected at 300m

visit EmbeddingGemma →full EmbeddingGemma review →

BGE-M3

#7 in embedding models · mit, three retrieval modes in one model, and quietly ageing

73/100

verdictthe most versatile open model here — dense, sparse and multi-vector output under mit — on a card that has barely moved since 2024.

BGE-M3 vs Qwen3-Embedding-8B

	Qwen3-Embedding-8B	BGE-M3
price	$0.010 / 1m tokens on DeepInfra	$0.010 / 1m tokens on DeepInfra
free tier	yes	yes
price / 1m	$0.010 hosted, free self-hosted	$0.010 hosted, free self-hosted
dimensions	4,096, truncatable to 32	1,024, fixed
context	32,768 tokens	8,192 tokens
licence	Apache-2.0	MIT
multimodal	no — text only	no — text only

switch forhybrid retrieval where you want dense and sparse vectors from a single model

pros

+mit licensed — the most permissive terms in the ranking
+dense, sparse and multi-vector retrieval from one model
+100+ languages at 8,192 tokens of context
+matching bge-reranker-v2 family under the same licence

cons

−fixed 1,024 dimensions with no matryoshka truncation
−card essentially unchanged since july 2024
−NVIDIA NIM is deprecating its hosted endpoint in august 2026
−no MTEB score with an identifiable board version

visit BGE-M3 →full BGE-M3 review →

Stella

#8 in embedding models · mit weights with dimensions from 256 to 8,192, english only

68/100

verdictthe widest dimension range in the category under an unrestricted mit licence — english-only, short-context, and with nobody hosting it for you.

Stella vs Qwen3-Embedding-8B

	Qwen3-Embedding-8B	Stella
price	$0.010 / 1m tokens on DeepInfra	free — self-host only
free tier	yes	yes
price / 1m	$0.010 hosted, free self-hosted	free — self-host only
dimensions	4,096, truncatable to 32	256 to 8,192
context	32,768 tokens	512 tokens recommended
licence	Apache-2.0	MIT
multimodal	no — text only	no — text only

switch forenglish-only retrieval where you want mit weights and full control of index size

pros

+mit licensed with no usage conditions whatsoever
+eight matryoshka dimension options from 256 to 8,192
+the authors state where quality plateaus, rather than leaving you to test
+1.5b parameters — far lighter to serve than Qwen3-8B

cons

−english only
−512-token recommended input, the shortest here
−no hosted option we could price — self-host or nothing
−licence comes from card metadata; the repo licence file 404s

visit Stella →full Stella review →

Nomic Embed Text v2

#9 in embedding models · a mixture-of-experts embedder that only wakes up two thirds of itself

66/100

verdicta clever, genuinely open, efficient multilingual model undone for most rag work by a 512-token context.

Nomic Embed Text v2 vs Qwen3-Embedding-8B

	Qwen3-Embedding-8B	Nomic Embed Text v2
price	$0.010 / 1m tokens on DeepInfra	free — self-host
free tier	yes	yes
price / 1m	$0.010 hosted, free self-hosted	free — self-host
dimensions	4,096, truncatable to 32	768, truncatable to 256
context	32,768 tokens	512 tokens
licence	Apache-2.0	Apache-2.0
multimodal	no — text only	no — text only

switch forself-hosted multilingual embedding on constrained compute

pros

+apache-2.0 weights with no commercial conditions
+mixture-of-experts design activates only 305m of 475m parameters
+around 100 languages from a very small model
+matryoshka truncation from 768 to 256 dimensions

cons

−512-token context makes it unsuitable for document retrieval
−no hosted list price confirmable from a primary source
−no MTEB score published on the model card
−no reranker sibling

visit Nomic Embed Text v2 →full Nomic Embed Text v2 review →

Mistral Embed

#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 Qwen3-Embedding-8B

	Qwen3-Embedding-8B	Mistral Embed
price	$0.010 / 1m tokens on DeepInfra	$0.10 / 1m tokens
free tier	yes	yes
price / 1m	$0.010 hosted, free self-hosted	$0.10
dimensions	4,096, truncatable to 32	not published by vendor
context	32,768 tokens	8,000 tokens
licence	Apache-2.0	proprietary api
multimodal	no — text only	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

visit Mistral Embed →full Mistral Embed review →

Jina Embeddings v5

#11 in embedding models · excellent quality per parameter, on weights you may not use commercially

58/100

verdicta genuinely strong sub-1b multilingual model that most readers of this page cannot legally deploy — and the licence is not what its reputation suggests.

Jina Embeddings v5 vs Qwen3-Embedding-8B

	Qwen3-Embedding-8B	Jina Embeddings v5
price	$0.010 / 1m tokens on DeepInfra	not published per token
free tier	yes	yes
price / 1m	$0.010 hosted, free self-hosted	not published
dimensions	4,096, truncatable to 32	1,024, truncatable to 32
context	32,768 tokens	32,768 tokens
licence	Apache-2.0	CC-BY-NC-4.0 — non-commercial
multimodal	no — text only	no — v5-omni is a separate model

switch forresearch and evaluation work where the non-commercial terms are not a problem

pros

+677m parameters with 32,768 tokens of context
+matryoshka truncation from 1,024 down to 32 dimensions
+93 languages claimed from a sub-billion-parameter model
+a separate v5-omni line covers images, audio, video and pdfs

cons

−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

visit Jina Embeddings v5 →full Jina Embeddings v5 review →

+ 1 more tested, not detailed here

we ranked 12 embedding models in total. the 1 that didn't make this page are written up in the full ranking →

how these were compared

every tool on this page went through the same test as Qwen3-Embedding-8B — same tasks, same order, scored the same way. the comparison tables are the figures from that testing, not vendor spec sheets.

the embedding models test in full →

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