Gemini 3.8 Flash Google 1048576
💰 Total Cost Calculation (from Plugin)
Output: $0.000938
Output: $0.000938
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Multimodal Input Details
Resolution: Medium
Tokens: 5,160,000
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 0 input tokens and 1,000 output tokens:
- Input Cost: $0.967500 (rounded ~ $0.97)
- Output Cost: $0.000938
- Total Cost: $0.533063 (rounded ~ $0.53)
- Cost per 1K tokens: $0.000103
- Tokens per dollar: 9,681,792 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: 4 hours, 25 minutes, 38.56 seconds
- Latency: 105 milliseconds to first token
- Base Throughput: 340 tokens/second
- Effective Throughput: 324 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 |
|---|---|---|---|
| 🏆 |
Ministral 3 (14B)
Mistral AI
|
$0.142050 (rounded ~ $0.14) Best Value | ↓ 73.4% cheaper |
| 🥈 |
GPT-5.6 Luna
OpenAI
|
$0.142200 (rounded ~ $0.14) | ↓ 73.3% cheaper |
| 🥉 |
Gemini 3.1 Flash Lite
Google
|
$0.177750 (rounded ~ $0.18) | ↓ 66.7% cheaper |
| #4 |
Gemini 3.5 Flash-Lite
Google
|
$0.213475 (rounded ~ $0.21) | ↓ 60% cheaper |
| #5 |
Gemini 2.5 Flash
Google
|
$0.213475 (rounded ~ $0.21) | ↓ 60% cheaper |
| #6 |
GPT-6 Luna
OpenAI
|
$0.284175 (rounded ~ $0.28) | ↓ 46.7% cheaper |
| #7 |
Mistral Large 3
Mistral AI
|
$0.355375 (rounded ~ $0.36) | ↓ 33.3% cheaper |
| #8 |
Gemini 3.6 Flash
Google
|
$0.533063 (rounded ~ $0.53) | Same price |
| #9 |
GPT-5.4 mini
OpenAI
|
$0.533250 (rounded ~ $0.53) | Same price |
| #10 |
Claude Haiku 5.5
Anthropic
|
$0.710750 | ↑ 33.3% more |
| #11 |
Claude Haiku 4.5
Anthropic
|
$0.710750 | ↑ 33.3% more |
| #12 |
Mistral Large 4
Mistral AI
|
$0.966645 (rounded ~ $0.97) | ↑ 81.3% more |
| #13 |
GPT-5.3 Codex Spark
OpenAI
|
$1.245125 (rounded ~ $1.25) | ↑ 133.6% more |
| #14 |
GPT-5.3 Instant
OpenAI
|
$1.245125 (rounded ~ $1.25) | ↑ 133.6% more |
| #15 |
Claude Sonnet 5.5
Anthropic
|
$1.357000 (rounded ~ $1.36) | ↑ 154.6% more |
| #16 |
Claude Sonnet 5
Anthropic
|
$1.421500 (rounded ~ $1.42) | ↑ 166.7% more |
| #17 |
Gemini 4 Argon
Google
|
$1.421500 (rounded ~ $1.42) | ↑ 166.7% more |
| #18 |
Gemini 3.1 Flash
Google
|
$1.422000 (rounded ~ $1.42) | ↑ 166.8% more |
| #19 |
GPT-5.6 Terra
OpenAI
|
$1.422000 (rounded ~ $1.42) | ↑ 166.8% more |
| #20 |
Claude Sonnet 4.6
Anthropic
|
$2.132250 (rounded ~ $2.13) | ↑ 300% more |
| #21 |
Claude Opus 5.5
Anthropic
|
$2.714000 (rounded ~ $2.71) | ↑ 409.1% more |
| #22 |
GPT-5.6 Sol
OpenAI
|
$2.843000 (rounded ~ $2.84) | ↑ 433.3% more |
| #23 |
Claude Opus 4.7
Anthropic
|
$3.553750 (rounded ~ $3.55) | ↑ 566.7% more |
| #24 |
Claude Opus 5
Anthropic
|
$3.553750 (rounded ~ $3.55) | ↑ 566.7% more |
| #25 |
Claude Opus 4.8
Anthropic
|
$3.553750 (rounded ~ $3.55) | ↑ 566.7% more |
| #26 |
Claude Opus 4.6
Anthropic
|
$3.553750 (rounded ~ $3.55) | ↑ 566.7% more |
| #27 |
Gemini 2.5 Pro
Google
|
$3.555000 (rounded ~ $3.56) | ↑ 566.9% more |
| #28 |
GPT-5.5 Instant
OpenAI
|
$3.555000 (rounded ~ $3.56) | ↑ 566.9% more |
| #29 |
Grok 4.3
xAI
|
$5.680000 | ↑ 965.5% more |
| #30 |
GPT-6.1 Sol
OpenAI
|
$5.683500 (rounded ~ $5.68) | ↑ 966.2% more |
| #31 |
GPT-6 Sol
OpenAI
|
$5.683500 (rounded ~ $5.68) | ↑ 966.2% more |
| #32 |
Gemini 3.1 Pro
Google
|
$5.685000 (rounded ~ $5.69) | ↑ 966.5% more |
| #33 |
Claude Fable 5.1
Anthropic
|
$6.623750 (rounded ~ $6.62) | ↑ 1142.6% more |
| #34 |
Claude Mythos 5.1
Anthropic
|
$6.623750 (rounded ~ $6.62) | ↑ 1142.6% more |
| #35 |
GPT-5.4
OpenAI
|
$7.106250 (rounded ~ $7.11) | ↑ 1233.1% more |
| #36 |
GPT-5.4 Thinking
OpenAI
|
$7.106250 (rounded ~ $7.11) | ↑ 1233.1% more |
| #37 |
Claude Fable 5
Anthropic
|
$7.107500 (rounded ~ $7.11) | ↑ 1233.3% more |
| #38 |
Claude Mythos 5
Anthropic
|
$7.107500 (rounded ~ $7.11) | ↑ 1233.3% more |
| #39 |
GPT-5.5
OpenAI
|
$14.212500 (rounded ~ $14.21) | ↑ 2566.2% more |
| #40 |
GPT-5.2 Pro
OpenAI
|
$14.941500 (rounded ~ $14.94) | ↑ 2703% more |
| #41 |
GPT-6 Astra
OpenAI
|
$28.430000 | ↑ 5233.3% more |
| #42 |
GPT-6 Astra
OpenAI
|
$28.430000 | ↑ 5233.3% more |
Ministral 3 (14B) Mistral AI
GPT-5.6 Luna OpenAI
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
GPT-6 Luna OpenAI
Mistral Large 3 Mistral AI
Gemini 3.6 Flash Google
GPT-5.4 mini OpenAI
Claude Haiku 5.5 Anthropic
Claude Haiku 4.5 Anthropic
Mistral Large 4 Mistral AI
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Claude Sonnet 5.5 Anthropic
Claude Sonnet 5 Anthropic
Gemini 4 Argon Google
Gemini 3.1 Flash Google
GPT-5.6 Terra OpenAI
Claude Sonnet 4.6 Anthropic
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
Gemini 2.5 Pro Google
GPT-5.5 Instant OpenAI
Grok 4.3 xAI
GPT-6.1 Sol OpenAI
GPT-6 Sol OpenAI
Gemini 3.1 Pro Google
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-5.5 OpenAI
GPT-5.2 Pro OpenAI
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
Optimizing Storyboard Workflows
For solo founders building video storyboarding tools, balancing visual accuracy with per-unit processing costs is essential. Gemini 3.8 Flash offers a compelling balance for high-volume visual tasks, particularly when your MVP needs to analyze large batches of generated or uploaded storyboard frames without stalling your budget.
Visual Processing at Scale
The primary advantage here is the model’s native multimodal capability, which allows it to handle both the image inputs and the accompanying scene descriptions in a single request. This reduces the latency often associated with multi-step pipelines where you might otherwise need separate vision and text models.
When scaling to 10,000 images, latency and throughput become as critical as the direct unit cost. Gemini 3.8 Flash excels in scenarios where you need rapid, consistent analysis of storyboard sequences. Its ability to parse visual compositions and generate descriptive scene text makes it ideal for automated narrative generation, allowing you to iterate on video concepts faster than manual processes allow.
However, evaluators should consider the trade-offs in reasoning depth compared to larger, more expensive frontier models. If your storyboard generation requires highly nuanced artistic direction or complex cinematic structural analysis, this model serves as an efficient layer for initial pass-throughs, while more intensive reasoning tasks might be reserved for specific, high-value shots. For cost-sensitive startups, the efficiency of this model allows for more experimentation with prompt engineering and output structure without the risk of runaway expenses.