DeepSeek V4 Flash DeepSeek 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.001200
Output: $0.001200
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 2,000 output tokens:
- Input Cost: $0.015000 (rounded ~ $0.02)
- Output Cost: $0.001200
- Total Cost: $0.008850 (rounded ~ $0.01)
- Cost per 1K tokens: $0.000087
- Tokens per dollar: 11,525,424 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 650 tokens per second and 95ms time to first token:
- Processing Time: 2 minutes, 49.66 seconds
- Latency: 95 milliseconds to first token
- Base Throughput: 650 tokens/second
- Effective Throughput: 602 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to DeepSeek V4 Flash| Rank | AI Model & Provider | Total Cost | vs DeepSeek V4 Flash |
|---|---|---|---|
| 🏆 |
Claude Haiku 5.5
Anthropic
|
$0.003250 Best Value | ↓ 63.3% cheaper |
| 🥈 |
GPT-6 Luna
OpenAI
|
$0.003250 | ↓ 63.3% cheaper |
| 🥉 |
GPT-5.6 Luna
OpenAI
|
$0.003350 | ↓ 62.1% cheaper |
| #4 |
Gemini 3.1 Flash Lite
Google
|
$0.004188 | ↓ 52.7% cheaper |
| #5 |
Nemotron 3 Super
NVIDIA
|
$0.004535 | ↓ 48.8% cheaper |
| #6 |
Gemini 3.5 Flash-Lite
Google
|
$0.005375 (rounded ~ $0.01) | ↓ 39.3% cheaper |
| #7 |
Gemini 2.5 Flash
Google
|
$0.005375 (rounded ~ $0.01) | ↓ 39.3% cheaper |
| #8 |
Mistral Large 3
Mistral AI
|
$0.007625 (rounded ~ $0.01) | ↓ 13.8% cheaper |
| #9 |
Gemini 3.8 Flash
Google
|
$0.012188 (rounded ~ $0.01) | ↑ 37.7% more |
| #10 |
Gemini 3.6 Flash
Google
|
$0.012188 (rounded ~ $0.01) | ↑ 37.7% more |
| #11 |
GPT-5.4 mini
OpenAI
|
$0.012563 (rounded ~ $0.01) | ↑ 41.9% more |
| #12 |
Claude Haiku 4.5
Anthropic
|
$0.016250 (rounded ~ $0.02) | ↑ 83.6% more |
| #13 |
Gemini 3.1 Flash
Google
|
$0.016750 (rounded ~ $0.02) | ↑ 89.3% more |
| #14 |
Mistral Large 4
Mistral AI
|
$0.020790 | ↑ 134.9% more |
| #15 |
GPT-5.3 Codex Spark
OpenAI
|
$0.031063 (rounded ~ $0.03) | ↑ 251% more |
| #16 |
GPT-5.3 Instant
OpenAI
|
$0.031063 (rounded ~ $0.03) | ↑ 251% more |
| #17 |
Claude Sonnet 5.5
Anthropic
|
$0.031250 (rounded ~ $0.03) | ↑ 253.1% more |
| #18 |
Claude Sonnet 5
Anthropic
|
$0.032500 (rounded ~ $0.03) | ↑ 267.2% more |
| #19 |
Gemini 4 Argon
Google
|
$0.032500 (rounded ~ $0.03) | ↑ 267.2% more |
| #20 |
GPT-5.6 Terra
OpenAI
|
$0.033500 (rounded ~ $0.03) | ↑ 278.5% more |
| #21 |
Gemini 2.5 Pro
Google
|
$0.044375 (rounded ~ $0.04) | ↑ 401.4% more |
| #22 |
Claude Sonnet 4.6
Anthropic
|
$0.048750 (rounded ~ $0.05) | ↑ 450.8% more |
| #23 |
Grok 4.3
xAI
|
$0.059000 (rounded ~ $0.06) | ↑ 566.7% more |
| #24 |
Grok 4.20 Beta
xAI
|
$0.059000 (rounded ~ $0.06) | ↑ 566.7% more |
| #25 |
Claude Opus 5.5
Anthropic
|
$0.062500 (rounded ~ $0.06) | ↑ 606.2% more |
| #26 |
GPT-6.1 Sol
OpenAI
|
$0.065000 (rounded ~ $0.07) | ↑ 634.5% more |
| #27 |
GPT-6 Sol
OpenAI
|
$0.065000 (rounded ~ $0.07) | ↑ 634.5% more |
| #28 |
GPT-5.6 Sol
OpenAI
|
$0.065000 (rounded ~ $0.07) | ↑ 634.5% more |
| #29 |
Gemini 3.1 Pro
Google
|
$0.067000 (rounded ~ $0.07) | ↑ 657.1% more |
| #30 |
Claude Opus 4.7
Anthropic
|
$0.081250 (rounded ~ $0.08) | ↑ 818.1% more |
| #31 |
Claude Opus 5
Anthropic
|
$0.081250 (rounded ~ $0.08) | ↑ 818.1% more |
| #32 |
Claude Opus 4.8
Anthropic
|
$0.081250 (rounded ~ $0.08) | ↑ 818.1% more |
| #33 |
Claude Opus 4.6
Anthropic
|
$0.081250 (rounded ~ $0.08) | ↑ 818.1% more |
| #34 |
GPT-5.4
OpenAI
|
$0.083750 (rounded ~ $0.08) | ↑ 846.3% more |
| #35 |
GPT-5.4 Thinking
OpenAI
|
$0.083750 (rounded ~ $0.08) | ↑ 846.3% more |
| #36 |
GPT-5.5 Instant
OpenAI
|
$0.083750 (rounded ~ $0.08) | ↑ 846.3% more |
| #37 |
Claude Fable 5.1
Anthropic
|
$0.153125 (rounded ~ $0.15) | ↑ 1630.2% more |
| #38 |
Claude Mythos 5.1
Anthropic
|
$0.153125 (rounded ~ $0.15) | ↑ 1630.2% more |
| #39 |
Claude Fable 5
Anthropic
|
$0.162500 (rounded ~ $0.16) | ↑ 1736.2% more |
| #40 |
Claude Mythos 5
Anthropic
|
$0.162500 (rounded ~ $0.16) | ↑ 1736.2% more |
| #41 |
GPT-5.5
OpenAI
|
$0.167500 (rounded ~ $0.17) | ↑ 1792.7% more |
| #42 |
GPT-6 Astra
OpenAI
|
$0.325000 (rounded ~ $0.33) | ↑ 3572.3% more |
| #43 |
GPT-5.2 Pro
OpenAI
|
$0.372750 (rounded ~ $0.37) | ↑ 4111.9% more |
| #44 |
GPT-5.2 Pro
OpenAI
|
$0.372750 (rounded ~ $0.37) | ↑ 4111.9% more |
Claude Haiku 5.5 Anthropic
GPT-6 Luna OpenAI
GPT-5.6 Luna OpenAI
Gemini 3.1 Flash Lite Google
Nemotron 3 Super NVIDIA
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
Gemini 3.8 Flash Google
Gemini 3.6 Flash Google
GPT-5.4 mini OpenAI
Claude Haiku 4.5 Anthropic
Gemini 3.1 Flash Google
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
GPT-5.6 Terra OpenAI
Gemini 2.5 Pro Google
Claude Sonnet 4.6 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
Claude Opus 5.5 Anthropic
GPT-6.1 Sol OpenAI
GPT-6 Sol OpenAI
GPT-5.6 Sol OpenAI
Gemini 3.1 Pro Google
Claude Opus 4.7 Anthropic
Claude Opus 5 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-5.5 Instant OpenAI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-5.5 OpenAI
GPT-6 Astra OpenAI
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
Managing the AI budget for small-scale marketing experiments often involves choosing models that deliver maximum performance for high-throughput, repetitive tasks. When running 100K-token loads for A/B testing campaign copy, DeepSeek V4 Flash has established itself as an efficiency-first option.
Unlike flagship models reserved for complex, multi-step agentic workflows, DeepSeek V4 Flash is purpose-built for high-throughput, routine tasks. For a social media manager producing 20 variations of ad copy, this model provides consistent performance without the premium cost associated with larger reasoning models. It handles the bulk generation of marketing content effectively, allowing teams to iterate on language angles and calls-to-action rapidly.
One of the primary benefits of DeepSeek V4 Flash for this specific use case is its architecture. It is a Mixture-of-Experts (MoE) model that offers high performance while maintaining cost-efficiency. For teams that need to run daily experiments without constant budget monitoring, this predictability is a major advantage.
However, it is important to note that while excellent for high-volume drafting and copy variants, this model is not designed for deep, multi-step logical reasoning. If your A/B testing requires the model to analyze complex performance data or synthesize competitor research alongside copy generation, you may find that it lacks the deep analysis capabilities of larger, more expensive models. For pure creative drafting and high-volume variation generation, it represents a highly efficient choice for cost-conscious solo practitioners and indie teams.