Claude Haiku 4.6 Anthropic
💰 Total Cost Calculation (from Plugin)
Output: $0.000156
Output: $0.000156
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 500 output tokens:
- Input Cost: $0.006250 (rounded ~ $0.01)
- Output Cost: $0.000156
- Total Cost: $0.003594
- Cost per 1K tokens: $0.000036
- Tokens per dollar: 27,965,217 tokens
- Context Window: 200000 tokens
Speed & Performance Analysis
With a processing speed of 850 tokens per second and 75ms time to first token:
- Processing Time: 2 minutes, 6.69 seconds
- Latency: 75 milliseconds to first token
- Base Throughput: 850 tokens/second
- Effective Throughput: 794 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Claude Haiku 4.6| Rank | AI Model & Provider | Total Cost | vs Claude Haiku 4.6 |
|---|---|---|---|
| 🏆 |
Claude Haiku 5.5
Anthropic
|
$0.002875 Best Value | ↓ 20% cheaper |
| 🥈 |
GPT-6 Luna
OpenAI
|
$0.002875 | ↓ 20% cheaper |
| 🥉 |
GPT-5.6 Luna
OpenAI
|
$0.002900 | ↓ 19.3% cheaper |
| #4 |
Gemini 3.1 Flash Lite
Google
|
$0.003625 | ↑ 0.9% more |
| #5 |
Gemini 3.5 Flash-Lite
Google
|
$0.004438 | ↑ 23.5% more |
| #6 |
Gemini 2.5 Flash
Google
|
$0.004438 | ↑ 23.5% more |
| #7 |
Mistral Large 3
Mistral AI
|
$0.007063 (rounded ~ $0.01) | ↑ 96.5% more |
| #8 |
Gemini 3.8 Flash
Google
|
$0.010781 | ↑ 200% more |
| #9 |
Gemini 3.6 Flash
Google
|
$0.010781 | ↑ 200% more |
| #10 |
GPT-5.4 mini
OpenAI
|
$0.010875 | ↑ 202.6% more |
| #11 |
Claude Haiku 4.5
Anthropic
|
$0.014375 (rounded ~ $0.01) | ↑ 300% more |
| #12 |
Gemini 3.1 Flash
Google
|
$0.014500 (rounded ~ $0.01) | ↑ 303.5% more |
| #13 |
Mistral Large 4
Mistral AI
|
$0.019223 | ↑ 434.9% more |
| #14 |
GPT-5.3 Codex Spark
OpenAI
|
$0.025813 (rounded ~ $0.03) | ↑ 618.3% more |
| #15 |
GPT-5.3 Instant
OpenAI
|
$0.025813 (rounded ~ $0.03) | ↑ 618.3% more |
| #16 |
Claude Sonnet 5.5
Anthropic
|
$0.027500 (rounded ~ $0.03) | ↑ 665.2% more |
| #17 |
Gemini 4 Argon
Google
|
$0.028750 (rounded ~ $0.03) | ↑ 700% more |
| #18 |
Claude Sonnet 5
Anthropic
|
$0.028750 (rounded ~ $0.03) | ↑ 700% more |
| #19 |
GPT-5.6 Terra
OpenAI
|
$0.029000 | ↑ 707% more |
| #20 |
Gemini 2.5 Pro
Google
|
$0.036875 (rounded ~ $0.04) | ↑ 926.1% more |
| #21 |
Claude Sonnet 4.6
Anthropic
|
$0.043125 (rounded ~ $0.04) | ↑ 1100% more |
| #22 |
Claude Opus 5.5
Anthropic
|
$0.055000 (rounded ~ $0.06) | ↑ 1430.4% more |
| #23 |
Grok 4.3
xAI
|
$0.056000 (rounded ~ $0.06) | ↑ 1458.3% more |
| #24 |
Grok 4.20 Beta
xAI
|
$0.056000 (rounded ~ $0.06) | ↑ 1458.3% more |
| #25 |
GPT-6.1 Sol
OpenAI
|
$0.057500 (rounded ~ $0.06) | ↑ 1500% more |
| #26 |
GPT-6 Sol
OpenAI
|
$0.057500 (rounded ~ $0.06) | ↑ 1500% more |
| #27 |
GPT-5.6 Sol
OpenAI
|
$0.057500 (rounded ~ $0.06) | ↑ 1500% more |
| #28 |
Gemini 3.1 Pro
Google
|
$0.058000 (rounded ~ $0.06) | ↑ 1513.9% more |
| #29 |
Claude Opus 4.7
Anthropic
|
$0.071875 (rounded ~ $0.07) | ↑ 1900% more |
| #30 |
Claude Opus 5
Anthropic
|
$0.071875 (rounded ~ $0.07) | ↑ 1900% more |
| #31 |
Claude Opus 4.8
Anthropic
|
$0.071875 (rounded ~ $0.07) | ↑ 1900% more |
| #32 |
Claude Opus 4.6
Anthropic
|
$0.071875 (rounded ~ $0.07) | ↑ 1900% more |
| #33 |
GPT-5.4
OpenAI
|
$0.072500 (rounded ~ $0.07) | ↑ 1917.4% more |
| #34 |
GPT-5.4 Thinking
OpenAI
|
$0.072500 (rounded ~ $0.07) | ↑ 1917.4% more |
| #35 |
GPT-5.5 Instant
OpenAI
|
$0.072500 (rounded ~ $0.07) | ↑ 1917.4% more |
| #36 |
Claude Fable 5.1
Anthropic
|
$0.134375 (rounded ~ $0.13) | ↑ 3639.1% more |
| #37 |
Claude Mythos 5.1
Anthropic
|
$0.134375 (rounded ~ $0.13) | ↑ 3639.1% more |
| #38 |
Claude Fable 5
Anthropic
|
$0.143750 (rounded ~ $0.14) | ↑ 3900% more |
| #39 |
Claude Mythos 5
Anthropic
|
$0.143750 (rounded ~ $0.14) | ↑ 3900% more |
| #40 |
GPT-5.5
OpenAI
|
$0.145000 (rounded ~ $0.15) | ↑ 3934.8% more |
| #41 |
GPT-6 Astra
OpenAI
|
$0.287500 (rounded ~ $0.29) | ↑ 7900% more |
| #42 |
GPT-5.2 Pro
OpenAI
|
$0.309750 | ↑ 8519.1% more |
| #43 |
GPT-5.2 Pro
OpenAI
|
$0.309750 | ↑ 8519.1% more |
Claude Haiku 5.5 Anthropic
GPT-6 Luna OpenAI
GPT-5.6 Luna OpenAI
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
Gemini 3.6 Flash Google
GPT-5.4 mini OpenAI
Claude Haiku 4.5 Anthropic
Gemini 3.1 Flash Google
Mistral Large 4 Mistral AI
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Claude Sonnet 5.5 Anthropic
Gemini 4 Argon Google
Claude Sonnet 5 Anthropic
GPT-5.6 Terra OpenAI
Gemini 2.5 Pro Google
Claude Sonnet 4.6 Anthropic
Claude Opus 5.5 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
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
Scaling Multilingual Support with High-Efficiency Models
For customer support leads managing high-volume, multilingual environments, the bottleneck is often the trade-off between model intelligence and processing speed. Claude Haiku 4.6 has emerged as a high-efficiency workhorse, particularly for pipelines processing 100K-token batches of customer inquiries. It allows teams to maintain rapid response times without compromising on the quality of the interaction.
The primary advantage of Haiku 4.6 in a support context is its exceptional instruction following at high throughput. When you are routing escalations across different languages, you need a model that maintains strict adherence to tone and policy without the latency penalties often associated with larger, more generalized reasoning engines. It is optimized for the kind of rapid-fire, structured output required for ticket classification, sentiment analysis, and initial triage.
Beyond raw speed, the ecosystem maturity surrounding this model is a significant factor for production teams. Integration with existing retrieval-augmented generation frameworks is seamless, ensuring that when your agent needs to retrieve a policy document to answer a multilingual query, it can do so without breaking the flow. While larger models might offer deeper logic for truly complex edge-case escalations, this model hits a sweet spot of performance that allows for massive scaling without ballooning your infrastructure requirements.
For support teams, the decision to standardize on this model usually hinges on its consistency. You need predictable behavior across languages, and this model delivers that stability, making it ideal for maintaining high customer satisfaction scores in automated support workflows.