Happyhorse 1.0 R2v

Alibaba DashScope video generation

Happyhorse 1.0 R2v is an Alibaba DashScope video generation model. Route Happyhorse 1.0 R2v via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: unknown Last verified: Oct 22, 2026 View source ↗
Pricing not yet public

We don't have verified per-token pricing for Happyhorse 1.0 R2v yet. If you have a source from Alibaba DashScope's documentation, help us add it — your submission gets reviewed within 48 hours.

Pricing

Per-token rates, expressed in USD per 1M tokens. Verified Oct 22, 2026.

Input —
Output —

Context & limits

Context window
—
Max input
—
Max output
—
Modalities
video, 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 Happyhorse 1.0 R2v yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Happyhorse 1.0 R2v 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.

SDK
Native Future AGI client (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.
# Happyhorse 1.0 R2v 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="dashscope/happyhorse-1-0-r2v",
    messages=[{"role": "user", "content": "Hello, Happyhorse 1.0 R2v!"}],
)

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="dashscope/happyhorse-1-0-r2v",
    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"))
Set AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗

Explore similar models

Happyhorse 1.0 R2v 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.

FAQ

How much does Happyhorse 1.0 R2v cost?

Public per-token pricing for Happyhorse 1.0 R2v is not yet published. Submit a source on this page to help us add it.

What is the context window of Happyhorse 1.0 R2v?

Context window for Happyhorse 1.0 R2v is not currently public.

Does Happyhorse 1.0 R2v support function calling?

Happyhorse 1.0 R2v 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 Happyhorse 1.0 R2v good for production?

Happyhorse 1.0 R2v 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 Happyhorse 1.0 R2v 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 Happyhorse 1.0 R2v

Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.