meta-models/Muse-Glimmer-30B vs Kimi K3
Practical read: meta-models/Muse-Glimmer-30B costs about an eighth as much. Kimi K3 reads eight times more context. One API key runs both.
No card required. Start with 1 million free tokens.
Side by side:
| Attribute | meta-models/Muse-Glimmer-30B | Kimi K3 |
|---|---|---|
| Price, 1M in + out | $1.58 DeepInfra, cheapest route | $11.97 Chutes, cheapest route |
| Best for | Low cost | Long documents |
| Input / output, 1M | $0.32 / $1.27 | $1.95 / $10.02 |
| Cached input | $0.0422/1M | $0.1688/1M to $1.046349/1M |
| Context window | 131K 131,072 tokens | 1.05M 1,048,576 tokens |
| First token, p50 | Measuring | 0.86 s via Makora |
| Throughput | Measuring | 246 tok/s via Baseten |
| AI IQ | Unrated | IQ 127 #22 on AI IQ |
| Provider routes | 1 | 27 with failover |
| Privacy | Varies by provider | Confidential E2EE and zero retention route |
| Recent route uptime range | not enough data | 50.00% to 100.00% |
| Publisher | meta-models | Kimi |
| 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-models/Muse-Glimmer-30B and Kimi K3 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-models/muse-glimmer-30b, then on moonshotai/kimi-k3.
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-models/muse-glimmer-30b", "moonshotai/kimi-k3"):
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-models/muse-glimmer-30b moonshotai/kimi-k3; 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-models/Muse-Glimmer-30B or MoonshotAI: Kimi K3?
meta-models/Muse-Glimmer-30B has the lower published input-plus-output rate. MoonshotAI: Kimi K3 has more Credits provider routes and the larger context window. There is not enough recent probe data to compare speed.
Is meta-models/Muse-Glimmer-30B or MoonshotAI: Kimi K3 cheaper?
The current cheapest TrustedRouter route is $1.5825/1M for meta-models/Muse-Glimmer-30B and $11.97425/1M for MoonshotAI: Kimi K3. The comparison uses current catalog prices and updates as provider pricing changes.
Is meta-models/Muse-Glimmer-30B or MoonshotAI: Kimi K3 faster?
Current measured p50 time to first token is not enough data for meta-models/Muse-Glimmer-30B and 864 ms for MoonshotAI: Kimi K3. These are routed probe measurements, not vendor-advertised speeds, and update as new samples arrive.
Can I test meta-models/Muse-Glimmer-30B and MoonshotAI: Kimi K3 with the same API?
Yes. Use the same OpenAI-compatible TrustedRouter base URL and API key, then change only the model id between meta-models/muse-glimmer-30b and moonshotai/kimi-k3. This makes side-by-side evals possible without maintaining two provider integrations.
More comparisons
All comparisonsTry meta-models/Muse-Glimmer-30B and Kimi K3 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.