Claude Sonnet 4.6 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.187500 (rounded ~ $0.19)
Output: $0.187500 (rounded ~ $0.19)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 50,000 input tokens and 50,000 output tokens:
- Input Cost: $0.037500 (rounded ~ $0.04)
- Output Cost: $0.187500 (rounded ~ $0.19)
- Total Cost: $0.214875 (rounded ~ $0.21)
- Cost per 1K tokens: $0.002149
- Tokens per dollar: 465,387 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 450 tokens per second and 200ms time to first token:
- Processing Time: 3 minutes, 46.85 seconds
- Latency: 200 milliseconds to first token
- Base Throughput: 450 tokens/second
- Effective Throughput: 441 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Claude Sonnet 4.6| Rank | AI Model & Provider | Total Cost | vs Claude Sonnet 4.6 |
|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.004663 Best Value | ↓ 97.8% cheaper |
| 🥈 |
Devstral Small 2
Mistral AI
|
$0.004663 | ↓ 97.8% cheaper |
| 🥉 |
Nemotron 3 Super
NVIDIA
|
$0.012988 (rounded ~ $0.01) | ↓ 94% cheaper |
| #4 |
Devstral 2
Mistral AI
|
$0.014900 (rounded ~ $0.01) | ↓ 93.1% cheaper |
| #5 |
Gemini 3.1 Flash Lite
Google
|
$0.021031 (rounded ~ $0.02) | ↓ 90.2% cheaper |
| #6 |
Mistral Large 3
Mistral AI
|
$0.023313 (rounded ~ $0.02) | ↓ 89.2% cheaper |
| #7 |
Gemini 3.5 Flash-Lite
Google
|
$0.033988 (rounded ~ $0.03) | ↓ 84.2% cheaper |
| #8 |
Gemini 2.5 Flash
Google
|
$0.033988 (rounded ~ $0.03) | ↓ 84.2% cheaper |
| #9 |
Gemini 3.8 Flash
Google
|
$0.053719 (rounded ~ $0.05) | ↓ 75% cheaper |
| #10 |
o4-mini Deep Research
OpenAI
|
$0.059125 | ↓ 72.5% cheaper |
| #11 |
GPT-5.4 mini
OpenAI
|
$0.063094 (rounded ~ $0.06) | ↓ 70.6% cheaper |
| #12 |
o4-mini
OpenAI
|
$0.065038 (rounded ~ $0.07) | ↓ 69.7% cheaper |
| #13 |
Claude Haiku 4.5
Anthropic
|
$0.071625 (rounded ~ $0.07) | ↓ 66.7% cheaper |
| #14 |
Magistral Medium
Mistral AI
|
$0.080750 | ↓ 62.4% cheaper |
| #15 |
Gemini 3.1 Flash
Google
|
$0.084125 (rounded ~ $0.08) | ↓ 60.8% cheaper |
| #16 |
GPT-5.6 Luna
OpenAI
|
$0.084125 (rounded ~ $0.08) | ↓ 60.8% cheaper |
| #17 |
Gemini 3.6 Flash
Google
|
$0.107438 (rounded ~ $0.11) | ↓ 50% cheaper |
| #18 |
Gemini 3.5 Flash
Google
|
$0.126188 (rounded ~ $0.13) | ↓ 41.3% cheaper |
| #19 |
Grok 4.3
xAI
|
$0.136500 (rounded ~ $0.14) | ↓ 36.5% cheaper |
| #20 |
Grok 4.20 Beta
xAI
|
$0.136500 (rounded ~ $0.14) | ↓ 36.5% cheaper |
| #21 |
Claude Sonnet 5
Anthropic
|
$0.143250 (rounded ~ $0.14) | ↓ 33.3% cheaper |
| #22 |
GPT-5.3 Codex Spark
OpenAI
|
$0.190969 | ↓ 11.1% cheaper |
| #23 |
GPT-5.3 Instant
OpenAI
|
$0.190969 | ↓ 11.1% cheaper |
| #24 |
GPT-5.6 Terra
OpenAI
|
$0.210313 | ↓ 2.1% cheaper |
| #25 |
Gemini 2.5 Pro
Google
|
$0.272813 (rounded ~ $0.27) | ↑ 27% more |
| #26 |
Gemini 3.1 Pro
Google
|
$0.336500 (rounded ~ $0.34) | ↑ 56.6% more |
| #27 |
Claude Opus 4.7
Anthropic
|
$0.358125 (rounded ~ $0.36) | ↑ 66.7% more |
| #28 |
Claude Opus 5
Anthropic
|
$0.358125 (rounded ~ $0.36) | ↑ 66.7% more |
| #29 |
Claude Opus 4.8
Anthropic
|
$0.358125 (rounded ~ $0.36) | ↑ 66.7% more |
| #30 |
Claude Opus 4.6
Anthropic
|
$0.358125 (rounded ~ $0.36) | ↑ 66.7% more |
| #31 |
GPT-5.4
OpenAI
|
$0.420625 | ↑ 95.8% more |
| #32 |
GPT-5.4 Thinking
OpenAI
|
$0.420625 | ↑ 95.8% more |
| #33 |
GPT-5.5 Instant
OpenAI
|
$0.420625 | ↑ 95.8% more |
| #34 |
GPT-5.6 Sol
OpenAI
|
$0.420625 | ↑ 95.8% more |
| #35 |
o3 Deep Research
OpenAI
|
$0.591250 (rounded ~ $0.59) | ↑ 175.2% more |
| #36 |
Claude Fable 5.1
Anthropic
|
$0.713438 (rounded ~ $0.71) | ↑ 232% more |
| #37 |
Claude Mythos 5.1
Anthropic
|
$0.713438 (rounded ~ $0.71) | ↑ 232% more |
| #38 |
Claude Fable 5
Anthropic
|
$0.716250 (rounded ~ $0.72) | ↑ 233.3% more |
| #39 |
Claude Mythos 5
Anthropic
|
$0.716250 (rounded ~ $0.72) | ↑ 233.3% more |
| #40 |
GPT-5.5
OpenAI
|
$0.841250 (rounded ~ $0.84) | ↑ 291.5% more |
| #41 |
o3 Pro
OpenAI
|
$1.182500 (rounded ~ $1.18) | ↑ 450.3% more |
| #42 |
GPT-6 Astra
OpenAI
|
$1.432500 (rounded ~ $1.43) | ↑ 566.7% more |
| #43 |
GPT-5.2 Pro
OpenAI
|
$2.291625 (rounded ~ $2.29) | ↑ 966.5% more |
| #44 |
GPT-5.2 Pro
OpenAI
|
$2.291625 (rounded ~ $2.29) | ↑ 966.5% more |
Mistral Small 3 Mistral AI
Devstral Small 2 Mistral AI
Nemotron 3 Super NVIDIA
Devstral 2 Mistral AI
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
o4-mini Deep Research OpenAI
GPT-5.4 mini OpenAI
o4-mini OpenAI
Claude Haiku 4.5 Anthropic
Magistral Medium Mistral AI
Gemini 3.1 Flash Google
GPT-5.6 Luna OpenAI
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
Grok 4.3 xAI
Grok 4.20 Beta xAI
Claude Sonnet 5 Anthropic
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
GPT-5.6 Terra OpenAI
Gemini 2.5 Pro Google
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
o3 Deep Research OpenAI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
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 product managers and content leads overseeing global e-commerce expansions, maintaining brand integrity across 12+ languages is the primary challenge. Claude Sonnet 4.6 has emerged as a preferred solution for high-fidelity translation pipelines where quality and cultural relevance take precedence over sheer raw speed.
Unlike generic translation engines, Claude Sonnet 4.6 excels at understanding the underlying intent and stylistic nuances of marketing copy. When translating product descriptions, it avoids the common pitfalls of direct, literal translation, instead favoring natural, idiomatic phrasing that resonates with local target audiences. This makes it particularly effective for fashion, luxury, or lifestyle brands where tone-of-voice is part of the product value proposition.
From a pipeline perspective, this model integrates seamlessly into existing workflows that require complex multi-step reasoning—such as extracting key product attributes from raw manufacturer data and then localizing them for specific regions. Its performance on complex, context-heavy tasks ensures that terminology remains consistent, even when dealing with technical documentation or specialized industry jargon. For teams that view localization as a strategic differentiator rather than a commodity, investing in the reasoning capabilities of Sonnet 4.6 can significantly reduce the overhead of human review and re-localization efforts, leading to a more streamlined and accurate global content strategy.