Whisper Large V3 Turbo
IBM watsonx audio transcriptionWhisper Large V3 Turbo is an IBM watsonx speech-to-text model. Route Whisper Large V3 Turbo via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
We don't have verified per-token pricing for Whisper Large V3 Turbo yet. If you have a source from IBM watsonx's documentation, help us add it — your submission gets reviewed within 48 hours.
Pricing
Per-token rates, expressed in USD per 1M tokens. Verified May 12, 2026.
| Input | — | |
| Output | — |
Limits
- Context window
- —
- Max input
- —
- Max output
- —
- Modalities
- audio, 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
Benchmarks pending
We haven't logged public benchmark scores for Whisper Large V3 Turbo yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Whisper Large V3 Turbo 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.# Whisper Large V3 Turbo 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="watsonx/whisper-large-v3-turbo",
messages=[{"role": "user", "content": "Hello, Whisper Large V3 Turbo!"}],
)
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="watsonx/whisper-large-v3-turbo",
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 ↗Same model on other providers
whisper-large-v3-turbo is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| Groq | — | — | May 12, 2026 |
FAQ
How much does Whisper Large V3 Turbo cost?
Public per-token pricing for Whisper Large V3 Turbo is not yet published. Submit a source on this page to help us add it.
What is the context window of Whisper Large V3 Turbo?
Context window for Whisper Large V3 Turbo is not currently public.
Does Whisper Large V3 Turbo support function calling?
Whisper Large V3 Turbo 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 Whisper Large V3 Turbo good for production?
Whisper Large V3 Turbo is best evaluated against your own production traces. Pipe traffic through Agent Command Center to compare it head-to-head against alternatives in shadow mode.
How can I route to Whisper Large V3 Turbo 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 Whisper Large V3 Turbo
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.