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Model comparison
EmbeddingGemma 2 vs Cohere Embed 4
Compare EmbeddingGemma 2 and Cohere Embed 4 using the same provider-sourced embeddings & vector search rubric. No mystery score and no invented benchmark ranking.
Estimate your cost
Set your usage. Your estimate updates as you type.
Assumes 600 tokens per page, processed separately. Actual token counts vary. This covers embedding only, not storage, search, or generated answers.
How this estimate works
Estimates exclude taxes, tools, cache storage/writes, free allowances and custom discounts. Image estimates cover output only, not prompt or reference-image charges. Quality modes differ by model. Unlisted settings are not treated as free.
| Model | Access | Estimated total (USD) |
|---|---|---|
| Cohere Embed 4Cohere | Cohere | No reviewed rate |
| EmbeddingGemma 2Google DeepMind | Google DeepMind | No reviewed rate |
Quality and speed evidence
Results describe a specific test, language and configuration—not overall intelligence. Missing results do not imply worse quality.
Cohere Embed 4 · FinanceBenchRetrieval
Community-submitted result
Retrieval relevance (0–1; higher is better)
Test configuration
MTEB · 1.38.43 · eng-Latn · test/default
dimensions: 1536 · similarity: cosine · modelMetadata: https://github.com/embeddings-benchmark/results/blob/main/results/Cohere__Cohere-embed-v4.0/1/model_meta.json
Checked: September 5, 2026
MTEB contributors · FinanceBenchRetrieval0.8833 nDCG@10
Quick take
EmbeddingGemma 2
Google DeepMind's open-weight embedding model for local search across text, code, images, audio, and video.
Best for
- Local and offline multimodal search
- RAG over private media and documents
- Codebase retrieval on consumer hardware
Watch out for
Open weights do not make compute free. Rebuild and evaluate the index when changing models, use the documented numerical precision, and verify truncated vectors on your own data.
Cohere Embed 4
Cohere's enterprise embedding model for multilingual text, images, and visually rich documents with a 128K context window.
Best for
- Enterprise search over visually rich documents
- Multilingual RAG and semantic search
- Organizations that need private deployment choices
Watch out for
Cohere does not publish one simple hosted token price for Embed 4 on the reviewed pricing page. Ask for the exact SaaS or private-deployment rate before comparing total cost.
Compare the published facts
EmbeddingGemma 2 vs Cohere Embed 4
Values use each provider's own published units and limits. A blank means the provider did not publish a directly comparable value in the sources reviewed.
| Embeddings & vector search | EmbeddingGemma 2 | Cohere Embed 4 |
|---|---|---|
| Embedding priceCurrent provider price per million input tokens or the closest published billing unit. | Open weights; self-hosted | Hosted unit price not published |
| Input capacityMaximum content accepted in one embedding input, using the provider's documented token basis. | 8,192 shared tokens across text and media | 128K tokens |
| Vector dimensionsSupported output sizes; smaller vectors reduce storage while larger vectors may preserve more information. | 768 default; 512, 256, or 128 with re-normalization | 256, 512, 1,024, or 1,536 |
| Accepted inputsText, code, image, audio, video, PDF, or document-aware input supported by the endpoint. | Text, code, images, audio, and sampled video frames | Text, images, and mixed-content PDFs |
| Retrieval controlsQuery/document modes, task types, truncation, chunking, or other controls that shape vectors for retrieval. | Task prompts, selective encoders, vector truncation, and normalization | Search query/document, classification, and clustering input types |
| Where it runsDirect API, cloud marketplace, private deployment, or self-hosted route documented by the provider. | Local or self-hosted; Apache 2.0 weights | Cohere API, Model Vault, Microsoft Foundry, SageMaker |
How to choose
Compare the job, not the hype.
Start with the job you need to complete, then validate cost, access, and policy details on your exact provider route.
EmbeddingGemma 2
Inference can run locally without sending source content to an embedding API. Data retention and network access depend on the application and services you connect.
Cohere Embed 4
Cohere enterprise customers can opt out of training; SaaS prompts and generations are generally deleted after 30 days. Approved zero-data-retention accounts and private deployments offer stronger controls.
Frequently asked questions
EmbeddingGemma 2 vs Cohere Embed 4 FAQ
What is the main difference between EmbeddingGemma 2 and Cohere Embed 4?
EmbeddingGemma 2: Google DeepMind's open-weight embedding model for local search across text, code, images, audio, and video. Cohere Embed 4: Cohere's enterprise embedding model for multilingual text, images, and visually rich documents with a 128K context window.
Should I choose EmbeddingGemma 2 or Cohere Embed 4?
Consider EmbeddingGemma 2 when your priority is Local and offline multimodal search. Consider Cohere Embed 4 when your priority is Enterprise search over visually rich documents. Test both with your own data and provider route before committing.
Is this EmbeddingGemma 2 vs Cohere Embed 4 comparison based on Cody benchmarks?
No. This comparison aligns provider-published facts for the Embeddings & vector search category. It does not claim a universal winner or combine incompatible third-party benchmark scores.