DeepSeek V4 Flash DeepSeek 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.000140
Output: $0.000140
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 10,000 input tokens and 500 output tokens:
- Input Cost: $0.000700
- Output Cost: $0.000140
- Total Cost: $0.000336
- Cost per 1K tokens: $0.000032
- Tokens per dollar: 31,250,000 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: 17.14 seconds
- Latency: 95 milliseconds to first token
- Base Throughput: 650 tokens/second
- Effective Throughput: 619 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for DeepSeek V4 Flash. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.
Get my instant AI audit — $39 →✨ Market Recommendations AI Model Registry
← Back to DeepSeek V4 Flash| Rank | AI Model & Provider | Total Cost | vs DeepSeek V4 Flash |
|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.000108 Best Value | ↓ 68% cheaper |
| 🥈 |
Grok Code Fast 1
xAI
|
$0.000328 | ↓ 2.5% cheaper |
| 🥉 |
Gemini 3.1 Flash Lite
Google
|
$0.000363 | ↑ 7.9% more |
| #4 |
Gemini 2.5 Flash
Google
|
$0.000523 | ↑ 55.5% more |
| #5 |
Mistral Large 3
Mistral AI
|
$0.000538 | ↑ 60% more |
| #6 |
GPT-5.4 mini
OpenAI
|
$0.001088 | ↑ 223.7% more |
| #7 |
Grok 4.3
xAI
|
$0.001188 | ↑ 253.4% more |
| #8 |
o4-mini Deep Research
OpenAI
|
$0.001200 | ↑ 257.1% more |
| #9 |
o4-mini
OpenAI
|
$0.001320 | ↑ 292.9% more |
| #10 |
Claude Haiku 4.5
Anthropic
|
$0.001325 | ↑ 294.3% more |
| #11 |
Gemini 3.1 Flash
Google
|
$0.001450 | ↑ 331.5% more |
| #12 |
Grok 4.20 Beta
xAI
|
$0.002150 | ↑ 539.9% more |
| #13 |
Gemini 3.5 Flash
Google
|
$0.002175 | ↑ 547.3% more |
| #14 |
GPT-5.3 Codex Spark
OpenAI
|
$0.002975 | ↑ 785.4% more |
| #15 |
GPT-5.3 Instant
OpenAI
|
$0.002975 | ↑ 785.4% more |
| #16 |
Claude Sonnet 4.6
Anthropic
|
$0.003975 | ↑ 1083% more |
| #17 |
Gemini 2.5 Pro
Google
|
$0.004250 | ↑ 1164.9% more |
| #18 |
Gemini 3.1 Pro
Google
|
$0.005800 (rounded ~ $0.01) | ↑ 1626.2% more |
| #19 |
Claude Opus 4.7
Anthropic
|
$0.006625 (rounded ~ $0.01) | ↑ 1871.7% more |
| #20 |
Claude Opus 4.8
Anthropic
|
$0.006625 (rounded ~ $0.01) | ↑ 1871.7% more |
| #21 |
Claude Opus 4.6
Anthropic
|
$0.006625 (rounded ~ $0.01) | ↑ 1871.7% more |
| #22 |
GPT-5.4
OpenAI
|
$0.007250 (rounded ~ $0.01) | ↑ 2057.7% more |
| #23 |
GPT-5.4 Thinking
OpenAI
|
$0.007250 (rounded ~ $0.01) | ↑ 2057.7% more |
| #24 |
GPT-5.5 Instant
OpenAI
|
$0.007250 (rounded ~ $0.01) | ↑ 2057.7% more |
| #25 |
o3 Deep Research
OpenAI
|
$0.012000 (rounded ~ $0.01) | ↑ 3471.4% more |
| #26 |
GPT-5.5
OpenAI
|
$0.014500 (rounded ~ $0.01) | ↑ 4215.5% more |
| #27 |
o3 Pro
OpenAI
|
$0.024000 (rounded ~ $0.02) | ↑ 7042.9% more |
| #28 |
GPT-5.2 Pro
OpenAI
|
$0.035700 (rounded ~ $0.04) | ↑ 10525% more |
| #29 |
GPT-5.2 Pro
OpenAI
|
$0.035700 (rounded ~ $0.04) | ↑ 10525% more |
Mistral Small 3 Mistral AI
Grok Code Fast 1 xAI
Gemini 3.1 Flash Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
GPT-5.4 mini OpenAI
Grok 4.3 xAI
o4-mini Deep Research OpenAI
o4-mini OpenAI
Claude Haiku 4.5 Anthropic
Gemini 3.1 Flash Google
Grok 4.20 Beta xAI
Gemini 3.5 Flash Google
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Claude Sonnet 4.6 Anthropic
Gemini 2.5 Pro Google
Gemini 3.1 Pro Google
Claude Opus 4.7 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
o3 Deep Research OpenAI
GPT-5.5 OpenAI
o3 Pro OpenAI
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
DeepSeek V4 Flash has established itself as an essential tool for academic researchers building high-volume, retrieval-augmented generation (RAG) systems. In environments where researchers must regularly query massive document databases—often involving thousands of requests per day—balancing performance with operational efficiency is critical.
This model is specifically optimized for high-throughput scenarios, providing a compelling solution for researchers who need to balance the costs of large-scale literature analysis with the necessity of rapid response times. Unlike more parameter-heavy models designed for complex reasoning, DeepSeek V4 Flash excels in tasks that require quick retrieval, summarization, and extraction of information from structured and unstructured sources.
By deploying this model for standard RAG queries, researchers can maintain a responsive research interface while preserving higher-tier model capacity for tasks that demand deeper, more resource-intensive reasoning. It is particularly effective for exploratory search tools where the goal is to quickly surface relevant passages from an extensive library of papers. Because of its lean architecture, the model provides a highly consistent latency profile, which is vital when building tools that require real-time interaction for literature discovery. For research teams scaling their infrastructure, this model represents a strategic choice for managing large-scale document pipelines without compromising on the depth of the information retrieved, allowing for more frequent, iterative queries across the entire research corpus.