Abliterated Large Flash vs Gemma 4 31B
Practical read: Abliterated Large Flash reads four times more context. Gemma 4 31B costs about a seventh as much and starts answering sooner. One API key runs both.
No card required. Start with 1 million free tokens.
Side by side:
| Attribute | Abliterated Large Flash | Gemma 4 31B |
|---|---|---|
| Price, 1M in + out | $3.16 Abliteration, cheapest route | $0.46 Weights & Biases, cheapest route |
| Best for | Long documents | Low cost and speed |
| Input / output, 1M | $1.06 / $2.11 | $0.11 / $0.36 |
| Cached input | $0.1055/1M | $0.060927/1M to $0.26375/1M |
| Context window | 1.05M 1,048,576 tokens | 262K 262,144 tokens |
| First token, p50 | 2.6 s via Abliteration | 0.63 s via Tinfoil |
| Throughput | Measuring | 373 tok/s via FriendliAI |
| AI IQ | Unrated | IQ 101 #120 on AI IQ |
| Provider routes | 1 | 15 with failover |
| Privacy | Zero retention route | Confidential E2EE and zero retention route |
| Recent route uptime range | 100.00% | 0.00% to 100.00% |
| Publisher | abliteration | Google AI 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 Abliterated Large Flash and Gemma 4 31B 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 abliteration/abliterated-model-large-flash, then on google/gemma-4-31b-it.
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 ("abliteration/abliterated-model-large-flash", "google/gemma-4-31b-it"):
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 abliteration/abliterated-model-large-flash google/gemma-4-31b-it; 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, Abliterated Large Flash or Google: Gemma 4 31B?
Abliterated Large Flash has the larger context window. Google: Gemma 4 31B has the lower published input-plus-output rate, more Credits provider routes and the lower measured p50 time to first token.
Is Abliterated Large Flash or Google: Gemma 4 31B cheaper?
The current cheapest TrustedRouter route is $3.165/1M for Abliterated Large Flash and $0.4642/1M for Google: Gemma 4 31B. The comparison uses current catalog prices and updates as provider pricing changes.
Is Abliterated Large Flash or Google: Gemma 4 31B faster?
Current measured p50 time to first token is 2620 ms for Abliterated Large Flash and 629 ms for Google: Gemma 4 31B. These are routed probe measurements, not vendor-advertised speeds, and update as new samples arrive.
Can I test Abliterated Large Flash and Google: Gemma 4 31B with the same API?
Yes. Use the same OpenAI-compatible TrustedRouter base URL and API key, then change only the model id between abliteration/abliterated-model-large-flash and google/gemma-4-31b-it. This makes side-by-side evals possible without maintaining two provider integrations.
More comparisons
All comparisonsTry Abliterated Large Flash and Gemma 4 31B 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.