Haiku 5.5 hits price parity with GPT-6 Luna ($0.10/$0.50) for short contexts, but tokenizer inflation and steep 5x pricing cliff above 100k tokens create a narrow competitive window.
Summary
Cost-sensitive inference workloads under 100k tokens now have true parity benchmarking; beyond that threshold, token efficiency and pricing trade-offs force explicit model selection per use case rather than blanket adoption.
Why it matters
Cost-sensitive inference workloads under 100k tokens now have true parity benchmarking; beyond that threshold, token efficiency and pricing trade-offs force explicit model selection per use case rather than blanket adoption.
Implementation verdict
Replaces Haiku 4.5 for cost-optimized inference. Requires re-baselining token counts (1.25x inflation vs prior version) and profiling context lengths before commit. Test now if you're already on Anthropic; switch only if workloads consistently exceed 100k tokens where Luna becomes 5x cheaper.
Sources
Dev Signal
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