Gemini 4 Argon Google 1048576
💰 Total Cost Calculation (from Plugin)
Output: $0.037500 (rounded ~ $0.04)
Output: $0.037500 (rounded ~ $0.04)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 50,000 input tokens and 15,000 output tokens:
- Input Cost: $0.025000 (rounded ~ $0.03)
- Output Cost: $0.037500 (rounded ~ $0.04)
- Total Cost: $0.044500 (rounded ~ $0.04)
- Cost per 1K tokens: $0.000685
- Tokens per dollar: 1,460,674 tokens
- Context Window: 1048576 tokens
Speed & Performance Analysis
With a processing speed of 180 tokens per second and 260ms time to first token:
- Processing Time: 6 minutes, 19.35 seconds
- Latency: 260 milliseconds to first token
- Base Throughput: 180 tokens/second
- Effective Throughput: 171 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Gemini 4 Argon| Rank | AI Model & Provider | Total Cost | vs Gemini 4 Argon |
|---|---|---|---|
| 🏆 |
Claude Haiku 5.5
Anthropic
|
$0.004450 Best Value | ↓ 90% cheaper |
| 🥈 |
GPT-6 Luna
OpenAI
|
$0.004450 | ↓ 90% cheaper |
| 🥉 |
GPT-5.6 Luna
OpenAI
|
$0.005200 (rounded ~ $0.01) | ↓ 88.3% cheaper |
| #4 |
Gemini 3.1 Flash Lite
Google
|
$0.006500 (rounded ~ $0.01) | ↓ 85.4% cheaper |
| #5 |
Mistral Large 3
Mistral AI
|
$0.007375 (rounded ~ $0.01) | ↓ 83.4% cheaper |
| #6 |
Gemini 3.5 Flash-Lite
Google
|
$0.010425 | ↓ 76.6% cheaper |
| #7 |
Gemini 2.5 Flash
Google
|
$0.010425 | ↓ 76.6% cheaper |
| #8 |
Gemini 3.8 Flash
Google
|
$0.016688 (rounded ~ $0.02) | ↓ 62.5% cheaper |
| #9 |
Gemini 3.6 Flash
Google
|
$0.016688 (rounded ~ $0.02) | ↓ 62.5% cheaper |
| #10 |
GPT-5.4 mini
OpenAI
|
$0.019500 | ↓ 56.2% cheaper |
| #11 |
Mistral Large 4
Mistral AI
|
$0.020435 | ↓ 54.1% cheaper |
| #12 |
Claude Haiku 4.5
Anthropic
|
$0.022250 (rounded ~ $0.02) | ↓ 50% cheaper |
| #13 |
Gemini 3.1 Flash
Google
|
$0.026000 (rounded ~ $0.03) | ↓ 41.6% cheaper |
| #14 |
Claude Sonnet 5.5
Anthropic
|
$0.043500 (rounded ~ $0.04) | ↓ 2.2% cheaper |
| #15 |
Grok 4.3
xAI
|
$0.044000 (rounded ~ $0.04) | ↓ 1.1% cheaper |
| #16 |
Grok 4.20 Beta
xAI
|
$0.044000 (rounded ~ $0.04) | ↓ 1.1% cheaper |
| #17 |
Claude Sonnet 5
Anthropic
|
$0.044500 (rounded ~ $0.04) | Same price |
| #18 |
GPT-5.6 Terra
OpenAI
|
$0.052000 (rounded ~ $0.05) | ↑ 16.9% more |
| #19 |
GPT-5.3 Codex Spark
OpenAI
|
$0.058625 (rounded ~ $0.06) | ↑ 31.7% more |
| #20 |
GPT-5.3 Instant
OpenAI
|
$0.058625 (rounded ~ $0.06) | ↑ 31.7% more |
| #21 |
Claude Sonnet 4.6
Anthropic
|
$0.066750 (rounded ~ $0.07) | ↑ 50% more |
| #22 |
Gemini 2.5 Pro
Google
|
$0.083750 (rounded ~ $0.08) | ↑ 88.2% more |
| #23 |
Claude Opus 5.5
Anthropic
|
$0.087000 (rounded ~ $0.09) | ↑ 95.5% more |
| #24 |
GPT-6.1 Sol
OpenAI
|
$0.089000 (rounded ~ $0.09) | ↑ 100% more |
| #25 |
GPT-6 Sol
OpenAI
|
$0.089000 (rounded ~ $0.09) | ↑ 100% more |
| #26 |
GPT-5.6 Sol
OpenAI
|
$0.089000 (rounded ~ $0.09) | ↑ 100% more |
| #27 |
Gemini 3.1 Pro
Google
|
$0.104000 (rounded ~ $0.10) | ↑ 133.7% more |
| #28 |
Claude Opus 4.7
Anthropic
|
$0.111250 (rounded ~ $0.11) | ↑ 150% more |
| #29 |
Claude Opus 5
Anthropic
|
$0.111250 (rounded ~ $0.11) | ↑ 150% more |
| #30 |
Claude Opus 4.8
Anthropic
|
$0.111250 (rounded ~ $0.11) | ↑ 150% more |
| #31 |
Claude Opus 4.6
Anthropic
|
$0.111250 (rounded ~ $0.11) | ↑ 150% more |
| #32 |
GPT-5.4
OpenAI
|
$0.130000 | ↑ 192.1% more |
| #33 |
GPT-5.4 Thinking
OpenAI
|
$0.130000 | ↑ 192.1% more |
| #34 |
GPT-5.5 Instant
OpenAI
|
$0.130000 | ↑ 192.1% more |
| #35 |
Claude Fable 5.1
Anthropic
|
$0.215000 (rounded ~ $0.22) | ↑ 383.1% more |
| #36 |
Claude Mythos 5.1
Anthropic
|
$0.215000 (rounded ~ $0.22) | ↑ 383.1% more |
| #37 |
Claude Fable 5
Anthropic
|
$0.222500 (rounded ~ $0.22) | ↑ 400% more |
| #38 |
Claude Mythos 5
Anthropic
|
$0.222500 (rounded ~ $0.22) | ↑ 400% more |
| #39 |
GPT-5.5
OpenAI
|
$0.260000 | ↑ 484.3% more |
| #40 |
GPT-6 Astra
OpenAI
|
$0.445000 (rounded ~ $0.45) | ↑ 900% more |
| #41 |
GPT-5.2 Pro
OpenAI
|
$0.703500 (rounded ~ $0.70) | ↑ 1480.9% more |
| #42 |
GPT-5.2 Pro
OpenAI
|
$0.703500 (rounded ~ $0.70) | ↑ 1480.9% more |
Claude Haiku 5.5 Anthropic
GPT-6 Luna OpenAI
GPT-5.6 Luna OpenAI
Gemini 3.1 Flash Lite Google
Mistral Large 3 Mistral AI
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.8 Flash Google
Gemini 3.6 Flash Google
GPT-5.4 mini OpenAI
Mistral Large 4 Mistral AI
Claude Haiku 4.5 Anthropic
Gemini 3.1 Flash Google
Claude Sonnet 5.5 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
Claude Sonnet 5 Anthropic
GPT-5.6 Terra OpenAI
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Claude Sonnet 4.6 Anthropic
Gemini 2.5 Pro Google
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
For research teams managing extensive literature review pipelines, Gemini 4 Argon represents a significant advancement in frontier reasoning. This model is designed to handle complex, multi-turn investigations, which makes it particularly well-suited for summarizing dense scientific documentation or cross-referencing findings across disparate research papers. The core value for a customer support lead or technical analyst here is the model’s ability to sustain deep, multi-step tasks without losing coherence, even when the input volume reaches 65K tokens or more.
One of the primary benefits of deploying Gemini 4 Argon in a research context is its balance of multimodal understanding and strong writing capabilities. If your workflow involves not just text-based papers but also analyzing figures, charts, or diagrams within research documents, the model’s visual perception allows for a more integrated analysis than text-only alternatives. This is crucial for verifying data presented in graphical format against the textual claims in an abstract or discussion section. Additionally, the model’s reasoning effort can be adjusted, allowing teams to balance computational spend with the required depth of analysis for different types of documents—from quick exploratory summaries to intensive, audit-level reviews. While the model excels at deep work, teams should ensure their implementation accounts for the occasional latency associated with high-effort reasoning modes. For organizations looking to automate the synthesis of technical data at scale, Gemini 4 Argon provides a robust, highly capable foundation for modern research workflows.