DeepSeek R1 DeepSeek
💰 Total Cost Calculation (from Plugin)
Output: $0.004380
Output: $0.004380
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 2,000 output tokens:
- Input Cost: $0.055000 (rounded ~ $0.06)
- Output Cost: $0.004380
- Total Cost: $0.037820 (rounded ~ $0.04)
- Cost per 1K tokens: $0.000371
- Tokens per dollar: 2,696,986 tokens
- Context Window: 163840 tokens
Speed & Performance Analysis
With a processing speed of 120 tokens per second and 220ms time to first token:
- Processing Time: 15 minutes, 9.68 seconds
- Latency: 220 milliseconds to first token
- Base Throughput: 120 tokens/second
- Effective Throughput: 112 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to DeepSeek R1| Rank | AI Model & Provider | Total Cost | vs DeepSeek R1 |
|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.001750 Best Value | ↓ 95.4% cheaper |
| 🥈 |
Gemini 3.1 Flash Lite
Google
|
$0.004750 | ↓ 87.4% cheaper |
| 🥉 |
Gemini 3.5 Flash-Lite
Google
|
$0.006050 (rounded ~ $0.01) | ↓ 84% cheaper |
| #4 |
Gemini 2.5 Flash
Google
|
$0.006050 (rounded ~ $0.01) | ↓ 84% cheaper |
| #5 |
Mistral Large 3
Mistral AI
|
$0.008750 (rounded ~ $0.01) | ↓ 76.9% cheaper |
| #6 |
Gemini 3.8 Flash
Google
|
$0.013875 (rounded ~ $0.01) | ↓ 63.3% cheaper |
| #7 |
GPT-5.4 mini
OpenAI
|
$0.014250 (rounded ~ $0.01) | ↓ 62.3% cheaper |
| #8 |
o4-mini Deep Research
OpenAI
|
$0.018000 (rounded ~ $0.02) | ↓ 52.4% cheaper |
| #9 |
Claude Haiku 4.5
Anthropic
|
$0.018500 (rounded ~ $0.02) | ↓ 51.1% cheaper |
| #10 |
Gemini 3.1 Flash
Google
|
$0.019000 (rounded ~ $0.02) | ↓ 49.8% cheaper |
| #11 |
GPT-5.6 Luna
OpenAI
|
$0.019000 (rounded ~ $0.02) | ↓ 49.8% cheaper |
| #12 |
o4-mini
OpenAI
|
$0.019800 | ↓ 47.6% cheaper |
| #13 |
Gemini 3.6 Flash
Google
|
$0.027750 (rounded ~ $0.03) | ↓ 26.6% cheaper |
| #14 |
Gemini 3.5 Flash
Google
|
$0.028500 (rounded ~ $0.03) | ↓ 24.6% cheaper |
| #15 |
GPT-5.3 Codex Spark
OpenAI
|
$0.035000 (rounded ~ $0.04) | ↓ 7.5% cheaper |
| #16 |
GPT-5.3 Instant
OpenAI
|
$0.035000 (rounded ~ $0.04) | ↓ 7.5% cheaper |
| #17 |
Claude Sonnet 5
Anthropic
|
$0.037000 (rounded ~ $0.04) | ↓ 2.2% cheaper |
| #18 |
GPT-5.6 Terra
OpenAI
|
$0.047500 (rounded ~ $0.05) | ↑ 25.6% more |
| #19 |
Gemini 2.5 Pro
Google
|
$0.050000 | ↑ 32.2% more |
| #20 |
Claude Sonnet 4.6
Anthropic
|
$0.055500 (rounded ~ $0.06) | ↑ 46.7% more |
| #21 |
Grok 4.3
xAI
|
$0.068000 (rounded ~ $0.07) | ↑ 79.8% more |
| #22 |
Grok 4.20 Beta
xAI
|
$0.068000 (rounded ~ $0.07) | ↑ 79.8% more |
| #23 |
Gemini 3.1 Pro
Google
|
$0.076000 (rounded ~ $0.08) | ↑ 101% more |
| #24 |
Claude Opus 4.7
Anthropic
|
$0.092500 (rounded ~ $0.09) | ↑ 144.6% more |
| #25 |
Claude Opus 5
Anthropic
|
$0.092500 (rounded ~ $0.09) | ↑ 144.6% more |
| #26 |
Claude Opus 4.8
Anthropic
|
$0.092500 (rounded ~ $0.09) | ↑ 144.6% more |
| #27 |
Claude Opus 4.6
Anthropic
|
$0.092500 (rounded ~ $0.09) | ↑ 144.6% more |
| #28 |
GPT-5.4
OpenAI
|
$0.095000 (rounded ~ $0.10) | ↑ 151.2% more |
| #29 |
GPT-5.4 Thinking
OpenAI
|
$0.095000 (rounded ~ $0.10) | ↑ 151.2% more |
| #30 |
GPT-5.5 Instant
OpenAI
|
$0.095000 (rounded ~ $0.10) | ↑ 151.2% more |
| #31 |
GPT-5.6 Sol
OpenAI
|
$0.095000 (rounded ~ $0.10) | ↑ 151.2% more |
| #32 |
Claude Fable 5.1
Anthropic
|
$0.177500 (rounded ~ $0.18) | ↑ 369.3% more |
| #33 |
Claude Mythos 5.1
Anthropic
|
$0.177500 (rounded ~ $0.18) | ↑ 369.3% more |
| #34 |
o3 Deep Research
OpenAI
|
$0.180000 | ↑ 375.9% more |
| #35 |
Claude Fable 5
Anthropic
|
$0.185000 (rounded ~ $0.19) | ↑ 389.2% more |
| #36 |
Claude Mythos 5
Anthropic
|
$0.185000 (rounded ~ $0.19) | ↑ 389.2% more |
| #37 |
GPT-5.5
OpenAI
|
$0.190000 | ↑ 402.4% more |
| #38 |
o3 Pro
OpenAI
|
$0.360000 | ↑ 851.9% more |
| #39 |
GPT-6 Astra
OpenAI
|
$0.370000 | ↑ 878.3% more |
| #40 |
GPT-5.2 Pro
OpenAI
|
$0.420000 | ↑ 1010.5% more |
| #41 |
GPT-5.2 Pro
OpenAI
|
$0.420000 | ↑ 1010.5% more |
Mistral Small 3 Mistral AI
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
Gemini 3.8 Flash Google
GPT-5.4 mini OpenAI
o4-mini Deep Research OpenAI
Claude Haiku 4.5 Anthropic
Gemini 3.1 Flash Google
GPT-5.6 Luna OpenAI
o4-mini OpenAI
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Claude Sonnet 5 Anthropic
GPT-5.6 Terra OpenAI
Gemini 2.5 Pro Google
Claude Sonnet 4.6 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
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
GPT-5.6 Sol OpenAI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
o3 Deep Research OpenAI
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-5.5 OpenAI
o3 Pro OpenAI
GPT-6 Astra OpenAI
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
For indie hackers and mobile developers working on MVP agentic pipelines, controlling operational overhead without sacrificing reasoning quality is the primary challenge. DeepSeek R1 has emerged as a specialized contender for these tasks, particularly when your workflow involves heavy reasoning or mathematical logic within an agentic loop. In an orchestration pattern, worker agents often consume the majority of your token budget. Using a model like DeepSeek R1 for these worker tasks allows you to maintain high-quality reasoning outputs while keeping your operational costs predictable.
Because R1 excels at ‘thinking’ before generating a response, it is particularly effective for tasks requiring step-by-step logic, such as data extraction, code debugging, or complex classification, which are common in 100,000-token agentic workflows. When integrating this model into a mobile backend, consider the latency profile of these thinking-heavy processes. While they provide superior chain-of-thought results compared to standard text models, the ‘time-to-first-token’ may be slightly higher. However, for backend-side agent processing where result accuracy is more critical than instantaneous responsiveness, this trade-off is usually acceptable. It is an excellent choice for developers looking to build robust, reasoning-capable agents on a startup budget, allowing you to allocate more resources to the orchestrator layer or other parts of your app stack.