Kimi K3 vs @cf/qwen/qwen2.5-coder-32b-instruct
Practical read: Kimi K3 reads more context. @cf/qwen/qwen2.5-coder-32b-instruct costs about a seventh as much. One API key runs both.
No card required. Start with 1 million free tokens.
Side by side:
| Attribute | Kimi K3 | @cf/qwen/qwen2.5-coder-32b-instruct |
|---|---|---|
| Price, 1M in + out | $11.97 Chutes, cheapest route | $1.75 Cloudflare Workers AI, cheapest route |
| Best for | Long documents | Low cost |
| Input / output, 1M | $1.95 / $10.02 | $0.70 / $1.06 |
| Cached input | $0.1688/1M to $0.910887/1M | Not published |
| Context window | 1.05M 1,048,576 tokens | 33K 32,768 tokens |
| First token, p50 | 0.82 s via Telnyx | Measuring |
| Throughput | 255 tok/s via Wafer | Measuring |
| AI IQ | IQ 127 #22 on AI IQ | Unrated |
| Provider routes | 26 with failover | 1 |
| Privacy | Confidential E2EE and zero retention route | Varies by provider |
| Recent route uptime range | 66.67% to 100.00% | not enough data |
| Publisher | Kimi | Alibaba Cloud Model Studio |
| Modes | chat | 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 Kimi K3 and @cf/qwen/qwen2.5-coder-32b-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 moonshotai/kimi-k3, then on qwen/qwen2.5-coder-32b-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 ("moonshotai/kimi-k3", "qwen/qwen2.5-coder-32b-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 moonshotai/kimi-k3 qwen/qwen2.5-coder-32b-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, MoonshotAI: Kimi K3 or @cf/qwen/qwen2.5-coder-32b-instruct?
MoonshotAI: Kimi K3 has more Credits provider routes and the larger context window. @cf/qwen/qwen2.5-coder-32b-instruct has the lower published input-plus-output rate. There is not enough recent probe data to compare speed.
Is MoonshotAI: Kimi K3 or @cf/qwen/qwen2.5-coder-32b-instruct cheaper?
The current cheapest TrustedRouter route is $11.97425/1M for MoonshotAI: Kimi K3 and $1.7513/1M for @cf/qwen/qwen2.5-coder-32b-instruct. The comparison uses current catalog prices and updates as provider pricing changes.
Is MoonshotAI: Kimi K3 or @cf/qwen/qwen2.5-coder-32b-instruct faster?
Current measured p50 time to first token is 823 ms for MoonshotAI: Kimi K3 and not enough data for @cf/qwen/qwen2.5-coder-32b-instruct. These are routed probe measurements, not vendor-advertised speeds, and update as new samples arrive.
Can I test MoonshotAI: Kimi K3 and @cf/qwen/qwen2.5-coder-32b-instruct with the same API?
Yes. Use the same OpenAI-compatible TrustedRouter base URL and API key, then change only the model id between moonshotai/kimi-k3 and qwen/qwen2.5-coder-32b-instruct. This makes side-by-side evals possible without maintaining two provider integrations.
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Try Kimi K3 and @cf/qwen/qwen2.5-coder-32b-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.