Qwen Qwen2.5 7B Instruct
DeepInfra chatQwen Qwen2.5 7B Instruct is a DeepInfra chat model.It supports a 32,768-token context windowwith up to 32,768 output tokens.Input is priced at $0.0400/M tokens and output at $0.1000/M tokens. Route Qwen Qwen2.5 7B Instruct via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Qwen Qwen2.5 7B Instruct spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.0400/M input · $0.1000/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 | $0.0400/M | |
| Output | $0.1000/M |
Limits
- Context window
- 32,768 tokens
- Max input
- 32,768 tokens
- Max output
- 32,768 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 — not advertised
- Prompt caching — not advertised
- Reasoning — not advertised
Where it's strong
Watch out for
- !high cost — input + output rates are in the top 98% of priced chat peers; consider a cheaper sibling for high-volume workloads
- !limited context — 32,768-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
- !strict structured output — no JSON-schema enforcement, expect retry loops
Benchmarks pending
We haven't logged public benchmark scores for Qwen Qwen2.5 7B Instruct yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Qwen Qwen2.5 7B 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.
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.# Qwen Qwen2.5 7B 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="deepinfra/qwen-qwen2-5-7b-instruct",
messages=[{"role": "user", "content": "Hello, Qwen Qwen2.5 7B 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="deepinfra/qwen-qwen2-5-7b-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"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Same model on other providers
qwen-qwen2-5-7b-instruct is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| Novita AI | $0.0700/M | $0.0700/M | May 12, 2026 |
Compare with similar models
Qwen Qwen2.5 7B 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 Qwen Qwen2.5 7B Instruct cost?
Input is priced at $0.0400 per 1M tokens and output at $0.1000 per 1M tokens (DeepInfra, last verified May 12, 2026).
What is the context window of Qwen Qwen2.5 7B Instruct?
Qwen Qwen2.5 7B Instruct supports a 32,768-token context window with up to 32,768 output tokens.
Does Qwen Qwen2.5 7B Instruct support function calling?
Qwen Qwen2.5 7B 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 Qwen Qwen2.5 7B Instruct good for production?
Qwen Qwen2.5 7B 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 Qwen Qwen2.5 7B 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 Qwen Qwen2.5 7B Instruct
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.