Claude Sonnet 4.6 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.375000 (rounded ~ $0.38)
Output: $0.375000 (rounded ~ $0.38)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 100,000 output tokens:
- Input Cost: $0.075000 (rounded ~ $0.08)
- Output Cost: $0.375000 (rounded ~ $0.38)
- Total Cost: $0.416250 (rounded ~ $0.42)
- Cost per 1K tokens: $0.002081
- Tokens per dollar: 480,480 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: 7 minutes, 37.96 seconds
- Latency: 200 milliseconds to first token
- Base Throughput: 450 tokens/second
- Effective Throughput: 437 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 |
|---|---|---|---|
| 🏆 |
Devstral Small 2
Mistral AI
|
$0.008875 (rounded ~ $0.01) Best Value | ↓ 97.9% cheaper |
| 🥈 |
Nemotron 3 Super
NVIDIA
|
$0.024625 (rounded ~ $0.02) | ↓ 94.1% cheaper |
| 🥉 |
Devstral 2
Mistral AI
|
$0.028000 (rounded ~ $0.03) | ↓ 93.3% cheaper |
| #4 |
Gemini 3.1 Flash Lite
Google
|
$0.040938 | ↓ 90.2% cheaper |
| #5 |
Mistral Large 3
Mistral AI
|
$0.044375 (rounded ~ $0.04) | ↓ 89.3% cheaper |
| #6 |
Gemini 3.5 Flash-Lite
Google
|
$0.066625 (rounded ~ $0.07) | ↓ 84% cheaper |
| #7 |
Gemini 2.5 Flash
Google
|
$0.066625 (rounded ~ $0.07) | ↓ 84% cheaper |
| #8 |
Gemini 3.8 Flash
Google
|
$0.104063 (rounded ~ $0.10) | ↓ 75% cheaper |
| #9 |
GPT-5.4 mini
OpenAI
|
$0.122813 (rounded ~ $0.12) | ↓ 70.5% cheaper |
| #10 |
o4-mini
OpenAI
|
$0.125125 (rounded ~ $0.13) | ↓ 69.9% cheaper |
| #11 |
Claude Haiku 4.5
Anthropic
|
$0.138750 (rounded ~ $0.14) | ↓ 66.7% cheaper |
| #12 |
Gemini 3.1 Flash
Google
|
$0.163750 (rounded ~ $0.16) | ↓ 60.7% cheaper |
| #13 |
GPT-5.6 Luna
OpenAI
|
$0.163750 (rounded ~ $0.16) | ↓ 60.7% cheaper |
| #14 |
Gemini 3.6 Flash
Google
|
$0.208125 (rounded ~ $0.21) | ↓ 50% cheaper |
| #15 |
Gemini 3.5 Flash
Google
|
$0.245625 (rounded ~ $0.25) | ↓ 41% cheaper |
| #16 |
Grok 4.3
xAI
|
$0.255000 (rounded ~ $0.26) | ↓ 38.7% cheaper |
| #17 |
Grok 4.20 Beta
xAI
|
$0.255000 (rounded ~ $0.26) | ↓ 38.7% cheaper |
| #18 |
Claude Sonnet 5
Anthropic
|
$0.277500 (rounded ~ $0.28) | ↓ 33.3% cheaper |
| #19 |
GPT-5.3 Codex Spark
OpenAI
|
$0.374063 (rounded ~ $0.37) | ↓ 10.1% cheaper |
| #20 |
GPT-5.6 Terra
OpenAI
|
$0.409375 | ↓ 1.7% cheaper |
| #21 |
Gemini 2.5 Pro
Google
|
$0.534375 (rounded ~ $0.53) | ↑ 28.4% more |
| #22 |
Gemini 3.1 Pro
Google
|
$0.655000 (rounded ~ $0.66) | ↑ 57.4% more |
| #23 |
Claude Opus 4.7
Anthropic
|
$0.693750 (rounded ~ $0.69) | ↑ 66.7% more |
| #24 |
Claude Opus 5
Anthropic
|
$0.693750 (rounded ~ $0.69) | ↑ 66.7% more |
| #25 |
Claude Opus 4.8
Anthropic
|
$0.693750 (rounded ~ $0.69) | ↑ 66.7% more |
| #26 |
Claude Opus 4.6
Anthropic
|
$0.693750 (rounded ~ $0.69) | ↑ 66.7% more |
| #27 |
GPT-5.4
OpenAI
|
$0.818750 (rounded ~ $0.82) | ↑ 96.7% more |
| #28 |
GPT-5.4 Thinking
OpenAI
|
$0.818750 (rounded ~ $0.82) | ↑ 96.7% more |
| #29 |
GPT-5.5 Instant
OpenAI
|
$0.818750 (rounded ~ $0.82) | ↑ 96.7% more |
| #30 |
GPT-5.6 Sol
OpenAI
|
$0.818750 (rounded ~ $0.82) | ↑ 96.7% more |
| #31 |
o3 Deep Research
OpenAI
|
$1.137500 (rounded ~ $1.14) | ↑ 173.3% more |
| #32 |
Claude Fable 5.1
Anthropic
|
$1.378125 (rounded ~ $1.38) | ↑ 231.1% more |
| #33 |
Claude Mythos 5.1
Anthropic
|
$1.378125 (rounded ~ $1.38) | ↑ 231.1% more |
| #34 |
Claude Fable 5
Anthropic
|
$1.387500 (rounded ~ $1.39) | ↑ 233.3% more |
| #35 |
Claude Mythos 5
Anthropic
|
$1.387500 (rounded ~ $1.39) | ↑ 233.3% more |
| #36 |
GPT-5.5
OpenAI
|
$1.637500 (rounded ~ $1.64) | ↑ 293.4% more |
| #37 |
o3 Pro
OpenAI
|
$2.275000 (rounded ~ $2.28) | ↑ 446.5% more |
| #38 |
GPT-6 Astra
OpenAI
|
$2.775000 (rounded ~ $2.78) | ↑ 566.7% more |
| #39 |
GPT-6 Astra
OpenAI
|
$2.775000 (rounded ~ $2.78) | ↑ 566.7% more |
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
GPT-5.4 mini OpenAI
o4-mini OpenAI
Claude Haiku 4.5 Anthropic
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-5.6 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-6 Astra OpenAI
Translating a 100K-token product catalog requires more than just raw speed; it demands a model that can maintain consistent terminology and brand voice across multiple languages. For creators and indie developers building translation pipelines, this workload is best handled by a model that prioritizes instruction-following and nuance. Claude Sonnet 4.6 excels in this domain because of its ability to handle complex system prompts and maintain stylistic integrity, which is vital when localizing product descriptions that need to resonate culturally.
In a typical product catalog pipeline, the challenge is not just the initial translation but the adherence to a specific glossary. Claude Sonnet 4.6 consistently demonstrates the ability to respect these constraints without hallucinating terms, making it a reliable choice for high-quality, mid-volume translation tasks. Unlike smaller, faster models that might drift in tone, this model remains anchored to your provided style guidelines. If your translation workflow involves heavy emphasis on maintaining a premium brand voice or requires accurate handling of nuanced marketing copy, deploying this model ensures that the output is ready for publication with minimal human post-editing. For developers setting up automated pipelines, the integration is straightforward, allowing you to treat the translation step as an extension of your content management system.