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Cohere’s Embed 5 is a two-tier embedding family: Pro targets retrieval quality, while Fast targets lower latency and higher-volume traffic. In Cohere’s published ViDoRe V3 results, both score above Voyage 4 Large, Gemini Embedding 2 and OpenAI’s text-embedding-3-large. Those are vendor-reported reranking results, not an independent test or proof that Embed 5 will perform best on every search workload. The practical choice depends on how each model handles your own documents, queries, languages, latency needs and costs.
What is Cohere Embed 5?
Launched on September 30, 2026, Embed 5 is a family of two embedding models: embed-v5.0-pro and embed-v5.0-fast. Embedding models turn content into vectors that applications can use to retrieve semantically related text or other supported inputs. Cohere positions Pro for quality-sensitive retrieval and offline indexing, and Fast for interactive search, agent loops and high-volume query traffic. Cohere’s launch announcement and its Embed 5 documentation entry describe the release.
Pro and Fast can share an index
Cohere says both variants use a shared embedding space. A team can create corpus vectors with Pro and query that index with Fast without rebuilding it, provided the output dimensions match. This gives teams a potential way to spend more computation during offline indexing and use the faster model for live queries. The shared space does not remove the need to validate retrieval quality on the application’s actual data.
Inputs, context and outputs
Cohere lists text, images and fused text-image inputs, including use cases such as embedding PDF pages. Its model documentation lists support for more than 100 languages, a 128K-token context window, selectable output dimensions of 256, 512, 768, 1024, 1536 or 2048, and float, int8 or binary output formats. Dimension and output-format choices affect vector storage and downstream compatibility; select settings that your index and retrieval stack support. See Cohere’s Embed model details for model usage information.
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How do the published benchmark scores compare?
The following figures are averages reported by Cohere in 2026. Its ViDoRe V3 figures use RCP-nDCG@10, and the parsed-document figures are from Cohere’s separate suite. They are not results from an independent evaluator.
| Model | Cohere-reported ViDoRe V3 average | Cohere-reported parsed-document suite |
|---|---|---|
| Cohere Embed 5 Pro | 85.8 | 84.8 |
| Cohere Embed 5 Fast | 84.5 | 83.4 |
| Voyage 4 Large | 83.7 | 83.6 |
| Gemini Embedding 2 | 83.2 | 80.8 |
| OpenAI text-embedding-3-large | 75.5 | Not stated by Cohere |
| Cohere Embed 4 | Not stated in this comparison | 78.6 |
Cohere says Embed 5 Pro improves by 8.8 points over Embed 4 on its ViDoRe V3 evaluation. In the parsed-document suite, Cohere describes the test set as service documentation, corporate reports, SEC filings, product manuals and privacy policies; the documents were parsed with Gemini 1.5 Flash. The results and benchmark descriptions are in Cohere’s announcement.
Finance results reported by Cohere
Cohere reports these Pro and Fast scores on three finance-related public benchmarks and says Pro ranked first on all three:
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| Benchmark | Embed 5 Pro | Embed 5 Fast |
|---|---|---|
| FinanceBench | 80.1 | 80.0 |
| FinQA | 90.0 | 88.8 |
| ViDoRe V3 Finance | 85.0 | 83.9 |
These figures are Cohere’s reported results, not a guarantee of performance on a particular financial corpus or application.
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Cohere’s RCP-nDCG@10 method uses each model’s similarity scores to reorder a fixed candidate set. The resulting scores therefore measure reranking quality over those candidates, rather than the model’s ability to find relevant documents during first-stage retrieval. Cohere says its ViDoRe annotations and evaluation code are available alongside its reported results.
That distinction matters in a retrieval-augmented generation (RAG) system. A model can reorder a supplied set effectively yet still perform differently when the application’s search stack must retrieve relevant material from a large corpus. The benchmark also cannot determine how a model behaves with your chunking strategy, metadata filters, document parsing, query mix or relevance labels. Treat the vendor comparison as evidence about one evaluation setup, then test candidates using the same candidate-generation pipeline and representative queries on your own corpus.
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How do language, context and serving options differ?
Language performance depends on which languages you need
Cohere’s reported further-language results are mixed, not a consistent Embed 5 lead. In the listed comparisons with Gemini Embedding 2, Cohere reports Gemini ahead of Pro in Japanese (90 versus 87), Korean (87 versus 85), Arabic (87 versus 83), Hindi (84 versus 80), Bengali (89 versus 83), Telugu (91 versus 80), Indonesian (88 versus 85), Thai (88 versus 82) and Farsi (83 versus 81). Pro is slightly ahead in Chinese (82 versus 81). Cohere also reports a favorable Pro average across five European languages, illustrating how an aggregate can change with language selection and weighting. For multilingual search, evaluate the actual languages and query directions your users employ rather than relying on a broad language-support count.
Voyage 4 Large’s documented context and dimensions
Voyage AI lists Voyage 4 Large with a 32K-token context window and 1024 default dimensions, with 256, 512 and 2048 dimension options. Cohere lists Embed 5’s context window at 128K tokens and dimensions from 256 through 2048 across six selectable sizes. These are vendor specifications, not a direct test of which model handles a particular long document better. Voyage says its 4-series models share an embedding space and describes indexing with a larger model and querying with a smaller one; consult its embedding documentation and Voyage 4 family announcement.
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Embed 5 accepts text, images and mixed text-image inputs, and Cohere publishes a separate parsed-document result. If your corpus includes scanned pages, charts, tables or complex layouts, check how your complete ingestion pipeline extracts and represents those elements. A multimodal input option alone does not establish that a model will preserve every structure or retrieve the right passage in your application. Compare on documents processed through the same realistic pipeline, especially when PDF parsing or image content is central.
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What does Embed 5 cost, and how should you compare total cost?
Cohere’s September 30, 2026 launch article lists the following token prices:
| Embed 5 tier | Text input | Image input |
|---|---|---|
| Pro | $0.12 per million tokens | $0.40 per million tokens |
| Fast | $0.08 per million tokens | $0.40 per million tokens |
These are the prices stated in Cohere’s launch article; verify current pricing and applicable terms with Cohere before budgeting. The cited comparison does not establish directly comparable current prices for Voyage 4 Large, Gemini Embedding 2 and OpenAI text-embedding-3-large, so these figures alone cannot determine the least expensive option. Include indexing and query volume, image processing, output dimensions, vector storage, infrastructure and deployment terms in a cost estimate. Cohere’s announcement lists deployment availability; Voyage’s family article describes API and MongoDB Atlas access, but those descriptions do not provide a complete, like-for-like deployment-cost comparison.
How should you choose among Embed 5, Voyage, Gemini and OpenAI?
Start with a controlled evaluation rather than selecting a model from a single aggregate score. Keep the corpus, document processing, chunking, candidate-generation method and relevance judgments consistent; then compare retrieval quality and operating constraints.
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- Build a representative test set. Include common and difficult queries, relevant and near-miss documents, and the languages and content types that occur in production. Use human-checked relevance judgments where possible.
- Run the same retrieval pipeline. Keep indexing, candidate generation, filters and reranking conditions consistent so model differences are not confused with changes elsewhere in the system.
- Measure the outcomes that matter. Track retrieval quality, query latency, indexing throughput and failure cases. Evaluate first-stage retrieval separately from reranking if your application uses both.
- Test the intended Pro/Fast arrangement. If considering Cohere’s shared-space workflow, index with Pro and query with Fast at the same vector dimensions, then compare it with using one tier for both tasks.
- Compare real operating costs and constraints. Estimate token charges, image inputs, vector-storage needs, serving requirements and deployment terms at expected scale. Confirm current provider prices and availability before committing.
The earlier Voyage comparison article reviewed in this context evaluated Gemini Embedding 001, Cohere Embed v4 and OpenAI v3 Large—not the newer model versions compared here. Its results should not be treated as a direct head-to-head test of Voyage 4 Large, Gemini Embedding 2, Embed 5 and OpenAI text-embedding-3-large.
Which model should you use?
Embed 5 Pro is the Cohere option to test first when retrieval quality is the priority; Fast is the tier to test when query latency or volume matters, particularly if sharing an index with Pro is useful. Voyage 4 Large, Gemini Embedding 2 and OpenAI text-embedding-3-large remain comparison candidates, but the cited evidence does not establish one universal winner. Choose based on controlled results for your corpus and users, then weigh those results against context, modality, serving and total-cost requirements.
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