Qwen2.5 Coder 32B Instruct

OVHcloud AI chat

Qwen2.5 Coder 32B Instruct is an OVHcloud AI chat model.It supports a 32,000-token context windowwith up to 32,000 output tokens.Input is priced at $0.870/M tokens and output at $0.870/M tokens. Route Qwen2.5 Coder 32B Instruct via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: May 12, 2026 View source ↗
Cost calculator

Estimate Qwen2.5 Coder 32B Instruct spend

Pick a workload, fine-tune the sliders, and see the monthly bill.

~3K in / ~400 out · 5K req/day
3,000
032,000
400
032,000
5,000
01,000,000
Per request
$0.002958
in $0.002610 · out $0.000348
Per day
$14.79
5,000 requests
Per month
$450
152,188 requests

Estimate uses $0.8700/M input · $0.8700/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 May 12, 2026.

Input $0.870/M
Output $0.870/M

Limits

Context window
32,000 tokens
Max input
32,000 tokens
Max output
32,000 tokens
Modalities
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 ✓ supported
  • Prompt caching — not advertised
  • Reasoning — not advertised

Where it's strong

Watch out for

  • !limited context — 32,000-token window is in the bottom quartile; not ideal for long documents or large RAG
  • !agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback

Benchmarks pending

We haven't logged public benchmark scores for Qwen2.5 Coder 32B Instruct yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Qwen2.5 Coder 32B Instruct 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.
# Qwen2.5 Coder 32B Instruct 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="ovhcloud/qwen2-5-coder-32b-instruct",
    messages=[{"role": "user", "content": "Hello, Qwen2.5 Coder 32B Instruct!"}],
)

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="ovhcloud/qwen2-5-coder-32b-instruct",
    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 ↗

Compare with similar models

Qwen2.5 Coder 32B Instruct doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.

FAQ

How much does Qwen2.5 Coder 32B Instruct cost?

Input is priced at $0.870 per 1M tokens and output at $0.870 per 1M tokens (OVHcloud AI, last verified May 12, 2026).

What is the context window of Qwen2.5 Coder 32B Instruct?

Qwen2.5 Coder 32B Instruct supports a 32,000-token context window with up to 32,000 output tokens.

Does Qwen2.5 Coder 32B Instruct support function calling?

Qwen2.5 Coder 32B Instruct 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 Qwen2.5 Coder 32B Instruct good for production?

Qwen2.5 Coder 32B Instruct 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 Qwen2.5 Coder 32B Instruct 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 Qwen2.5 Coder 32B Instruct

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