Google · Efficient
Gemini 3.5 Flash Thinking
Gemini 3.5 Flash with higher thinking enabled for harder reasoning, agentic coding, tool use, multimodal analysis, and long-context workflows. Audio input costs $3.00 per million audio tokens.
- Reasoning
- Vision
- Audio input
- Video input
- Input
- Text, Images, Video, Audio
- Output
- Text
- Reasoning effort
- minimal · low · medium · high
- Discovery tier
- Efficient
Pricing
USD per 1M tokens- Input
- $1.50
- Output
- $9.00
- Cache read
- $0.15
- Cache write
- $0.0833
Reference prices as published by the model provider. API example
Specifications
- Context window
- 1M
- Max output
- 65.5K
- Average output (7 days)
- 1.7K tokens
- Longer than 88% of 465 models
Side by side
Compare models
Put Gemini 3.5 Flash Thinking next to up to three other models. The comparison stays in the link, so you can share it.
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
Intelligence Index
32.6
Tested at high reasoning effort
Better than 76% of 326 scored models
Indexes
Coding Index
70.1Composite of the coding evaluations
Better than 77% of 168 scored models
Agentic work
AutomationBench-AA
42.1%Workflow automation, penalized for guardrail violations
Better than 58% of 173 scored models
AutomationBench-AA tasks completed
13.5%Workflows finished end to end without a guardrail violation
Better than 56% of 173 scored models
AA-Briefcasev1.1
874 EloAgentic knowledge work, rated head to head
Better than 36% of 166 scored models
GDPval-AAv2.1
1,202 EloEconomically valuable occupational tasks, rated head to head
Better than 51% of 187 scored models
Document reasoning
GDP.pdf
19.8%Professional reasoning over PDF documents, all-pass rate
Better than 76% of 171 scored models
AA-LCRv1.1
73.3%Reasoning across long documents
Better than 55% of 309 scored models
MLCR-AA
10.0%Long-context reasoning over medical records
Better than 33% of 121 scored models
Reasoning and knowledge
Humanity’s Last Exam
42.7%Expert-level questions across academic fields
Better than 84% of 323 scored models
GPQA Diamond
92.2%Graduate-level science questions
Better than 87% of 291 scored models
IFBench
76.3%Following precise output instructions
Better than 92% of 215 scored models
Coding
Terminal-Benchv4.0
6.6%Practical software engineering in a terminal
Better than 57% of 171 scored models
SciCode
53.9%Scientific computing in Python
Better than 68% of 174 scored models
Legacy evaluations
Terminal-Bench Hard
40.9%Earlier terminal task suite
Better than 77% of 212 scored models
τ²-Bench Telecom
95.3%Tool use in a customer-support simulation
Better than 86% of 214 scored models
AA-LCR (unversioned)
73.3%Long-context reasoning, earlier grading
Better than 55% of 309 scored models
Scores from Artificial Analysis, updated Oct 26, 2026. Gemini 3.5 Flash Thinking was tested at high reasoning effort; compare scores measured at the same effort.Artificial Analysis
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Throughput
- 48.6 tok/s
- Faster than 36% of 250 models
- Time to first token
- 6.5 s
- Quicker than 22% of 250 models
- Prompt cache hits
- 23.5%
- About $1.18 per 1M input tokens with caching
- Request success (24h)
- 94.3%
- Unstable
Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.
- Healthy (98% or more)
- Degraded (90–98%)
- Down (under 90%)
- No requests
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "google/gemini-3.5-flash-thinking",
"reasoning_effort": "medium",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="google/gemini-3.5-flash-thinking",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Related models
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