Gemini 3.8 Flash Google 1048576
💰 Total Cost Calculation (from Plugin)
Output: $0.003750
Output: $0.003750
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 20,000 input tokens and 1,000 output tokens:
- Input Cost: $0.015000 (rounded ~ $0.02)
- Output Cost: $0.003750
- Total Cost: $0.012000 (rounded ~ $0.01)
- Cost per 1K tokens: $0.000571
- Tokens per dollar: 1,750,000 tokens
- Context Window: 1048576 tokens
Speed & Performance Analysis
With a processing speed of 340 tokens per second and 105ms time to first token:
- Processing Time: 1 minute, 6.27 seconds
- Latency: 105 milliseconds to first token
- Base Throughput: 340 tokens/second
- Effective Throughput: 318 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Gemini 3.8 Flash| Rank | AI Model & Provider | Total Cost | vs Gemini 3.8 Flash |
|---|---|---|---|
| 🏆 |
Claude Haiku 5.5
Anthropic
|
$0.001600 Best Value | ↓ 86.7% cheaper |
| 🥈 |
GPT-6 Luna
OpenAI
|
$0.001600 | ↓ 86.7% cheaper |
| 🥉 |
GPT-5.6 Luna
OpenAI
|
$0.003400 | ↓ 71.7% cheaper |
| #4 |
Grok Code Fast 1
xAI
|
$0.003700 | ↓ 69.2% cheaper |
| #5 |
Gemini 3.1 Flash Lite
Google
|
$0.004250 | ↓ 64.6% cheaper |
| #6 |
Gemini 3.5 Flash-Lite
Google
|
$0.005800 (rounded ~ $0.01) | ↓ 51.7% cheaper |
| #7 |
Gemini 2.5 Flash
Google
|
$0.005800 (rounded ~ $0.01) | ↓ 51.7% cheaper |
| #8 |
Mistral Large 3
Mistral AI
|
$0.007000 (rounded ~ $0.01) | ↓ 41.7% cheaper |
| #9 |
Gemini 3.1 Flash
Google
|
$0.008500 (rounded ~ $0.01) | ↓ 29.2% cheaper |
| #10 |
Gemini 3.6 Flash
Google
|
$0.012000 (rounded ~ $0.01) | Same price |
| #11 |
GPT-5.4 mini
OpenAI
|
$0.012750 (rounded ~ $0.01) | ↑ 6.3% more |
| #12 |
Grok Build 0.1
xAI
|
$0.013000 (rounded ~ $0.01) | ↑ 8.3% more |
| #13 |
Claude Haiku 4.5
Anthropic
|
$0.016000 (rounded ~ $0.02) | ↑ 33.3% more |
| #14 |
Grok 4.3
xAI
|
$0.016250 (rounded ~ $0.02) | ↑ 35.4% more |
| #15 |
Grok 4.20 Beta
xAI
|
$0.016250 (rounded ~ $0.02) | ↑ 35.4% more |
| #16 |
Mistral Large 4
Mistral AI
|
$0.019140 | ↑ 59.5% more |
| #17 |
Gemini 2.5 Pro
Google
|
$0.023750 (rounded ~ $0.02) | ↑ 97.9% more |
| #18 |
Grok 4.7
xAI
|
$0.028000 (rounded ~ $0.03) | ↑ 133.3% more |
| #19 |
Grok 4.6
xAI
|
$0.028000 (rounded ~ $0.03) | ↑ 133.3% more |
| #20 |
Grok 4.5
xAI
|
$0.028000 (rounded ~ $0.03) | ↑ 133.3% more |
| #21 |
Claude Sonnet 5.5
Anthropic
|
$0.031000 (rounded ~ $0.03) | ↑ 158.3% more |
| #22 |
Claude Sonnet 5
Anthropic
|
$0.032000 (rounded ~ $0.03) | ↑ 166.7% more |
| #23 |
Gemini 4 Argon
Google
|
$0.032000 (rounded ~ $0.03) | ↑ 166.7% more |
| #24 |
GPT-6.1 Sol
OpenAI
|
$0.032000 (rounded ~ $0.03) | ↑ 166.7% more |
| #25 |
GPT-6 Sol
OpenAI
|
$0.032000 (rounded ~ $0.03) | ↑ 166.7% more |
| #26 |
GPT-5.3 Codex Spark
OpenAI
|
$0.033250 (rounded ~ $0.03) | ↑ 177.1% more |
| #27 |
GPT-5.3 Instant
OpenAI
|
$0.033250 (rounded ~ $0.03) | ↑ 177.1% more |
| #28 |
Kimi K3.1 Pro
Moonshot AI
|
$0.033400 (rounded ~ $0.03) | ↑ 178.3% more |
| #29 |
Gemini 3.1 Pro
Google
|
$0.034000 (rounded ~ $0.03) | ↑ 183.3% more |
| #30 |
GPT-5.6 Terra
OpenAI
|
$0.034000 (rounded ~ $0.03) | ↑ 183.3% more |
| #31 |
GPT-5.4
OpenAI
|
$0.042500 (rounded ~ $0.04) | ↑ 254.2% more |
| #32 |
GPT-5.4 Thinking
OpenAI
|
$0.042500 (rounded ~ $0.04) | ↑ 254.2% more |
| #33 |
Claude Sonnet 4.6
Anthropic
|
$0.048000 (rounded ~ $0.05) | ↑ 300% more |
| #34 |
Kimi K3
Moonshot AI
|
$0.050100 | ↑ 317.5% more |
| #35 |
Claude Opus 5.5
Anthropic
|
$0.062000 (rounded ~ $0.06) | ↑ 416.7% more |
| #36 |
GPT-5.6 Sol
OpenAI
|
$0.064000 (rounded ~ $0.06) | ↑ 433.3% more |
| #37 |
Claude Opus 4.7
Anthropic
|
$0.080000 | ↑ 566.7% more |
| #38 |
Claude Opus 5
Anthropic
|
$0.080000 | ↑ 566.7% more |
| #39 |
Claude Opus 4.8
Anthropic
|
$0.080000 | ↑ 566.7% more |
| #40 |
Claude Opus 4.6
Anthropic
|
$0.080000 | ↑ 566.7% more |
| #41 |
GPT-5.5
OpenAI
|
$0.085000 (rounded ~ $0.09) | ↑ 608.3% more |
| #42 |
GPT-5.5 Instant
OpenAI
|
$0.085000 (rounded ~ $0.09) | ↑ 608.3% more |
| #43 |
Muse Wonder 1.0
Meta AI
|
$0.100000 | ↑ 733.3% more |
| #44 |
Claude Fable 5.1
Anthropic
|
$0.152500 (rounded ~ $0.15) | ↑ 1170.8% more |
| #45 |
Claude Mythos 5.1
Anthropic
|
$0.152500 (rounded ~ $0.15) | ↑ 1170.8% more |
| #46 |
GPT-6 Astra
OpenAI
|
$0.160000 | ↑ 1233.3% more |
| #47 |
Claude Fable 5
Anthropic
|
$0.160000 | ↑ 1233.3% more |
| #48 |
Claude Mythos 5
Anthropic
|
$0.160000 | ↑ 1233.3% more |
| #49 |
GPT-5.2 Pro
OpenAI
|
$0.399000 (rounded ~ $0.40) | ↑ 3225% more |
| #50 |
GPT-5.2 Pro
OpenAI
|
$0.399000 (rounded ~ $0.40) | ↑ 3225% more |
Claude Haiku 5.5 Anthropic
GPT-6 Luna OpenAI
GPT-5.6 Luna OpenAI
Grok Code Fast 1 xAI
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
Gemini 3.1 Flash Google
Gemini 3.6 Flash Google
GPT-5.4 mini OpenAI
Grok Build 0.1 xAI
Claude Haiku 4.5 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
Mistral Large 4 Mistral AI
Gemini 2.5 Pro Google
Grok 4.7 xAI
Grok 4.6 xAI
Grok 4.5 xAI
Claude Sonnet 5.5 Anthropic
Claude Sonnet 5 Anthropic
Gemini 4 Argon Google
GPT-6.1 Sol OpenAI
GPT-6 Sol OpenAI
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Kimi K3.1 Pro Moonshot AI
Gemini 3.1 Pro Google
GPT-5.6 Terra OpenAI
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
Claude Sonnet 4.6 Anthropic
Kimi K3 Moonshot AI
Claude Opus 5.5 Anthropic
GPT-5.6 Sol OpenAI
Claude Opus 4.7 Anthropic
Claude Opus 5 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
GPT-5.5 OpenAI
GPT-5.5 Instant OpenAI
Muse Wonder 1.0 Meta AI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
GPT-6 Astra OpenAI
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
Gemini 3.8 Flash is an excellent choice for educational tutoring platforms requiring high-speed, multimodal interaction. In a typical 30-minute tutoring session, maintaining conversational momentum is vital for student engagement. This model excels in latency-sensitive tasks, ensuring that responses to student queries or uploaded documents—such as study guides or charts—are processed almost instantaneously. Its ability to ingest 20K tokens allows the system to maintain a complete history of the student’s progress, previous explanations, and specific learning gaps without hitting context limits.
For legal tech engineers and EdTech developers, the core advantage here is the balance of performance and operational efficiency. Unlike larger, more compute-heavy reasoning models, this solution delivers the responsiveness necessary for real-time pedagogical support while remaining cost-effective at scale. The model’s support for audio and vision is particularly relevant for tutoring, allowing for the analysis of diagrams, handwritten notes, or verbal questions during the session. When implementing, focus on structuring your system prompts to leverage the model’s strengths in clear, empathetic communication. By keeping the session history within the context window, you ensure the AI acts as a consistent, informed partner throughout the learning process. This model represents a robust, reliable option for high-volume tutoring applications that prioritize user experience and low-latency feedback.