meta-llama/Llama-3.3-70B-Instruct-Turbo vs GLM 5.3 Flash
Practical read: GLM 5.3 Flash costs less and reads eight times more context. One API key runs both.
No card required. Start with 1 million free tokens.
Side by side:
| Attribute | meta-llama/Llama-3.3-70B-Instruct-Turbo | GLM 5.3 Flash |
|---|---|---|
| Price, 1M in + out | $0.44 DeepInfra, cheapest route | $0.26 Wafer, cheapest route |
| Best for | Low cost and long documents | |
| Input / output, 1M | $0.11 / $0.34 | $0.053 / $0.21 |
| Cached input | Not published | $0.01266/1M to $0.079125/1M |
| Context window | 131K 131,072 tokens | 1.05M 1,048,576 tokens |
| First token, p50 | Measuring | 0.89 s via FriendliAI |
| AI IQ | Unrated | IQ 119 #50 on AI IQ |
| Provider routes | 1 | 33 with failover |
| Privacy | Varies by provider | Confidential E2EE and zero retention route |
| Recent route uptime range | not enough data | 0.00% to 100.00% |
| Publisher | Meta | Z.AI |
| 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 meta-llama/Llama-3.3-70B-Instruct-Turbo and GLM 5.3 Flash 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 meta-llama/llama-3.3-70b-instruct-turbo, then on z-ai/glm-5.3-flash.
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 ("meta-llama/llama-3.3-70b-instruct-turbo", "z-ai/glm-5.3-flash"):
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 meta-llama/llama-3.3-70b-instruct-turbo z-ai/glm-5.3-flash; 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?\"}]}"
doneQuestions
Which should I use, meta-llama/Llama-3.3-70B-Instruct-Turbo or Z.ai: GLM 5.3 Flash?
Z.ai: GLM 5.3 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 meta-llama/Llama-3.3-70B-Instruct-Turbo or Z.ai: GLM 5.3 Flash cheaper?
The current cheapest TrustedRouter route is $0.4431/1M for meta-llama/Llama-3.3-70B-Instruct-Turbo and $0.26375/1M for Z.ai: GLM 5.3 Flash. The comparison uses current catalog prices and updates as provider pricing changes.
Is meta-llama/Llama-3.3-70B-Instruct-Turbo or Z.ai: GLM 5.3 Flash faster?
Current measured p50 time to first token is not enough data for meta-llama/Llama-3.3-70B-Instruct-Turbo and 888 ms for Z.ai: GLM 5.3 Flash. These are routed probe measurements, not vendor-advertised speeds, and update as new samples arrive.
Can I test meta-llama/Llama-3.3-70B-Instruct-Turbo and Z.ai: GLM 5.3 Flash with the same API?
Yes. Use the same OpenAI-compatible TrustedRouter base URL and API key, then change only the model id between meta-llama/llama-3.3-70b-instruct-turbo and z-ai/glm-5.3-flash. This makes side-by-side evals possible without maintaining two provider integrations.
More comparisons
All comparisonsTry meta-llama/Llama-3.3-70B-Instruct-Turbo and GLM 5.3 Flash 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.