Claude 3.5 Haiku
Ultra-fast and cost-efficient frontier intelligence model for high-throughput and sub-agent workflows.
200k
Tokens
8.192k
Output limit
Dense Transformer
Model family
Not Disclosed
Total / Active
$0.80
Per 1M tokens
$4.00
Per 1M tokens
Model Overview
Claude 3.5 Haiku offers rapid execution speeds and strong reasoning at low per-token pricing, matching previous generation flagship models (Claude 3 Opus) across multiple benchmarks while operating at fraction of the latency.
Developer Implementation Notes
Ideal for sub-agent routing, real-time chatbots, and high-frequency API tasks. Input pricing is $0.80/MTok with $0.08/MTok cached.
Key Strengths
- Very low latency and high tokens/second throughput
- Matches Claude 3 Opus on coding benchmarks at 1/15th the price
- 200k context window supported
Limitations & Boundaries
- Text-only; image input is not supported on Haiku 3.5
- Less nuanced on multi-turn complex mathematical derivations
Capabilities & Modalities
Best Production Use Cases
- Customer support routing & triage
- High-volume synthetic data generation
- Fast code autocomplete and linter fixes
Verified Benchmark Results
Standardized evaluations with methodology notes and authoritative citation links.
Pricing & Inference Cost Calculator
Token Cost Estimator – Claude 3.5 Haiku
Calculate projected inference spend with prompt caching
Quick Workload Presets
$1.6000
2.00M input tokens
$2.0000
0.50M output tokens
$3.60
Avg: $0.00360 / req
Compare with Similar Models
Claude 3.7 Sonnet
Anthropic's first hybrid reasoning frontier model with dynamic thinking budget control and state-of-the-art coding capabilities.
Gemini 2.0 Pro (Experimental)
Google's flagship intelligence model engineered for complex coding, mathematical proofs, and 2M token context.
OpenAI o3-mini
Next-generation cost-efficient reasoning model optimized for STEM, competitive math, and coding.
Data Accuracy & Verification Notice
AI model specifications, pricing records, and benchmark metrics published on this platform are compiled directly from authoritative sources (official provider documentation, research papers, and verified evaluation harnesses). Benchmark results reflect specific test harnesses and prompting methodologies; scores are not directly comparable across differing evaluation setups.