Cohere Embed V5 Pro
Azure AI Foundry embedding Deprecates in 80d (Jan 23, 2027)model fallback routing to swap models without code changes.
Cohere Embed V5 Pro is an Azure AI Foundry embedding model.It supports a 128,000-token context window.Input is priced at $0.120/M tokens Route Cohere Embed V5 Pro via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Cohere Embed V5 Pro spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.1200/M input · $0.000000/M output. Provider pricing changes. Production costs vary with retries, streaming overhead, and tool-call rounds.
Want this for free? Cache + route via Agent Command Center — first 100K requests and 100K cache hits free every month.
Pricing
Per-token rates, expressed in USD per 1M tokens. Verified Oct 22, 2026.
| Input | $0.120/M | |
| Output | — |
Context & limits
- Context window
- 128,000 tokens
- Max input
- 128,000 tokens
- Max output
- —
- Modalities
- embedding, text
Capabilities
- Function calling — not advertised
- Parallel tool calls — not advertised
- Vision input — not advertised
- Audio input — not advertised
- Audio output — not advertised
- PDF input — not advertised
- Streaming ✓ supported
- Structured output — not advertised
- Prompt caching — not advertised
- Reasoning — not advertised
Strengths & caveats — when to pick Cohere Embed V5 Pro
Data-driven from Cohere Embed V5 Pro's percentile within embedding peers.
Where it's strong
- +pricing — cheaper than 81% of priced embedding models on Future AGI
- +long-context work — top-2 largest context window across 143 embedding models
Watch out for
- !scheduled deprecation on Jan 23, 2027 (80 days)
Benchmarks pending
We haven't logged public benchmark scores for Cohere Embed V5 Pro yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Cohere Embed V5 Pro via Agent Command Center
One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.
agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.# Cohere Embed V5 Pro via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC
client = AgentCC(
api_key=os.environ["AGENTCC_API_KEY"], # from app.futureagi.com → Settings → API Keys
base_url="https://gateway.futureagi.com/v1",
)
resp = client.chat.completions.create(
model="azure-ai-foundry/cohere-embed-v5-pro",
messages=[{"role": "user", "content": "Hello, Cohere Embed V5 Pro!"}],
)
print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")
# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
model="azure-ai-foundry/cohere-embed-v5-pro",
messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost: ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache: ", raw.headers.get("x-agentcc-cache"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Explore similar models
Cohere Embed V5 Pro doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark. Side-by-side links appear where a comparison page exists; otherwise, open the model profile.
- Cohere Embed V5 FastAzure AI Foundry · $0.0800/M in · $0.000000/M out · 128,000 ctxView model →
- Embed V 4.0Azure AI Foundry · $0.120/M in · $0.000000/M out · 128,000 ctxView model →
- Cohere Embed V3 EnglishAzure AI Foundry · $0.1000/M in · $0.000000/M out · 512 ctxView model →
- Cohere Embed V3 MultilingualAzure AI Foundry · $0.1000/M in · $0.000000/M out · 512 ctxView model →
FAQ
How much does Cohere Embed V5 Pro cost?
Input is priced at $0.120 per 1M tokens and output at $0.000000 per 1M tokens (Azure AI Foundry, last verified Oct 22, 2026).
What is the context window of Cohere Embed V5 Pro?
Cohere Embed V5 Pro supports a 128,000-token context window.
Does Cohere Embed V5 Pro support function calling?
Cohere Embed V5 Pro does not currently advertise function-calling support. For agentic workloads, prefer a tool-calling-capable model and route via Agent Command Center for fallback.
Is Cohere Embed V5 Pro good for production?
Cohere Embed V5 Pro is well-suited for pricing — cheaper than 81% of priced embedding models on Future AGI and long-context work — top-2 largest context window across 143 embedding models. Consider alternatives if you need scheduled deprecation on Jan 23, 2027 (80 days).
How can I route to Cohere Embed V5 Pro with fallback?
Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.
Useful links for Cohere Embed V5 Pro
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.