DeepSeek V4.1 Flash vs Qwen2.5 VL 72B Instruct
Practical read: DeepSeek V4.1 Flash costs under a tenth as much and reads eight times more context. One API key runs both.
No card required. Start with 1 million free tokens.
Side by side:
| Attribute | DeepSeek V4.1 Flash | Qwen2.5 VL 72B Instruct |
|---|---|---|
| Price, 1M in + out | $0.16 Engy, cheapest route | $1.90 Parasail, cheapest route |
| Best for | Low cost and long documents | |
| Input / output, 1M | $0.032 / $0.13 | $0.84 / $1.06 |
| Cached input | $0.01/1M to $0.1899/1M | $0.422/1M |
| Context window | 1.05M 1,048,576 tokens | 128K 128,000 tokens |
| First token, p50 | 0.71 s via Together | Measuring |
| AI IQ | IQ 126 #24 on AI IQ | Unrated |
| Provider routes | 21 with failover | 1 |
| Privacy | Confidential E2EE and zero retention route | Zero retention route |
| Recent route uptime range | 80.00% to 100.00% | 100.00% |
| Publisher | DeepSeek | Alibaba Cloud Model Studio |
| Modes | chat decide | chat |
| More detail |
Price adds one million input and one million output tokens on the cheapest route. Prices and speed change: see the monthly reports and run a small eval before you choose a default.
Test both with your agent
Paste this into Claude Code, Cursor or any coding agent.
Compare DeepSeek V4.1 Flash and Qwen2.5 VL 72B Instruct in this project through TrustedRouter.
1. Read https://trustedrouter.com/docs/llms.txt
2. Point the OpenAI-compatible client at
https://api.trustedrouter.com/v1 and read the key from
TRUSTEDROUTER_API_KEY.
3. Run our eval prompts on deepseek/deepseek-v4.1-flash, then on qwen/qwen2.5-vl-72b-instruct.
4. Show me cost, latency and quality side by side before changing the default.
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.trustedrouter.com/v1",
api_key=os.environ["TRUSTEDROUTER_API_KEY"],
)
for model in ("deepseek/deepseek-v4.1-flash", "qwen/qwen2.5-vl-72b-instruct"):
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": "What is the capital of France?"}],
)
print(model, response.choices[0].message.content)
for model in deepseek/deepseek-v4.1-flash qwen/qwen2.5-vl-72b-instruct; do
curl https://api.trustedrouter.com/v1/chat/completions \
-H "Authorization: Bearer $TRUSTEDROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d "{\"model\": \"$model\", \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of France?\"}]}"
done
Questions
Which should I use, DeepSeek: DeepSeek V4.1 Flash or Qwen: Qwen2.5 VL 72B Instruct?
DeepSeek V4.1 Flash has the lower published input-plus-output rate, more Credits provider routes and the larger context window. There is not enough recent probe data to compare speed.
Is DeepSeek: DeepSeek V4.1 Flash or Qwen: Qwen2.5 VL 72B Instruct cheaper?
The current cheapest TrustedRouter route is $0.15825/1M for DeepSeek: DeepSeek V4.1 Flash and $1.899/1M for Qwen: Qwen2.5 VL 72B Instruct. The comparison uses current catalog prices and updates as provider pricing changes.
Is DeepSeek: DeepSeek V4.1 Flash or Qwen: Qwen2.5 VL 72B Instruct faster?
Current measured p50 time to first token is 711 ms for DeepSeek: DeepSeek V4.1 Flash and not enough data for Qwen: Qwen2.5 VL 72B Instruct. These are routed probe measurements, not vendor-advertised speeds, and update as new samples arrive.
Can I test DeepSeek: DeepSeek V4.1 Flash and Qwen: Qwen2.5 VL 72B Instruct with the same API?
Yes. Use the same OpenAI-compatible TrustedRouter base URL and API key, then change only the model id between deepseek/deepseek-v4.1-flash and qwen/qwen2.5-vl-72b-instruct. This makes side-by-side evals possible without maintaining two provider integrations.
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Try DeepSeek V4.1 Flash and Qwen2.5 VL 72B Instruct with one key.
TrustedRouter is useful when you know the model you want, but still need provider rollover, budget limits, usage records, and a prompt path you can verify.
No card required. Start with 1 million free tokens.