Claude Haiku 5.5 Launch

Haiku 5.5's pricing looks simple on a spec sheet - $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100,000 tokens ^[2]- but that flat rate ends at a hard wall. Cross the 100K-token line and the price jumps fivefold, to $0.50/$2.50 per million tokens ^[2]. The model's expanded 1-million-token context window ^[3]means it's now technically possible to feed it prompts 10 times past that cliff, which makes the threshold easy to hit by accident on long documents, chat histories, or multi-file codebases. Independent testing cited in community discussion found the practical effect is brutal: a prompt just over 100,000 tokens can cost roughly five times as much as one just under it, even though the actual text grew by only a couple of percent. One technical rebuttal argued the cliff isn't arbitrary - it likely reflects real inference economics, where prefill and decode costs scale differently once a request exceeds the model's efficient KV-cache window, and smaller models may be hit harder by that scaling than larger ones. Either way, the cliff turns prompt-length budgeting into a cost-control discipline that didn't matter nearly as much under Haiku 4.5's flatter, higher-priced structure.

Anthropic's headline claim is that Haiku 5.5 matches GPT-6 Luna's per-token price for prompts under 100K tokens ^[2], but matching price-per-token is not the same as matching price-per-task. Artificial Analysis found that on equivalent Intelligence Index tasks at maximum effort, Haiku 5.5 consumes roughly three times the output tokens that GPT-6 Luna uses (about 162,000 versus 50,000 tokens), which translated into a benchmarked cost-per-task of $0.21 for Haiku 5.5 versus $0.07 for Luna in one comparison ^[5]. That gap shows up outside the lab too: an independent builder's identical animated-voxel rendering task reportedly cost over ten times more on Haiku 5.5 than on Luna despite identical sticker pricing, a result that fed a skeptical community thread even as critics disputed the specific methodology rather than defending the raw cost parity claim. The lesson converges from multiple directions - official benchmarks, independent cost tests, and community pushback: a model's verbosity and effort setting can swing real spend far more than the per-token rate card suggests, and Anthropic's own framing of '90% cheaper' (per-token, sub-100K) versus '75% cheaper on average' (blended across all requests) already hints that the two numbers describe different things ^[3].

The benchmark story behind Haiku 5.5 is less about general knowledge and more about a specific skill: operating tools and environments. On Terminal-Bench 4.0, an agentic coding benchmark, Haiku 5.5 scored 39.2% versus 0.0% for Haiku 4.5 and 16.4% for GPT-6 Luna ^[1]. On the OSWorld 2.1 computer-use benchmark, Haiku 5.5 reached 72.4% against baselines in the 15.7-48.9% range for the prior Haiku and for Luna ^[3]. The GDPval-AA v2.1 knowledge benchmark tells a similar story of a huge generational leap - 1,620 for Haiku 5.5 versus 735 for Haiku 4.5, and ahead of Luna's 1,437 ^[3]. Artificial Analysis summarized the pattern as 'highly capable agentic knowledge work' paired with comparatively 'lower factual knowledge' but 'relatively low hallucinations' ^[5]- a classic small-model tradeoff where the model is built to act reliably inside a sandboxed task rather than to recall broad facts. Worth flagging: nearly all of these figures are Anthropic's own self-reported numbers, repeated across outlets rather than independently re-run.

Haiku 5.5 wasn't the only price change Anthropic made on October 7. It also halved Claude Sonnet 5.5's cache-read price, from $0.20 to $0.10 per million tokens, which Anthropic expects to cut roughly 20% off the cost of most agentic work ^[4]. The Decoder frames this bundled cut as 'almost certainly a reaction' to OpenAI's newer GPT-6.1 series rather than a standalone improvement ^[3], which reframes the whole Haiku 5.5 launch: it isn't just a new small model, it's one move in a two-part pricing response aimed at defending Anthropic's position across both its small and mid-tier model lines at once. Combined with Haiku 5.5's sub-100K price match to GPT-6 Luna, the pattern suggests the small-model price war between Anthropic and OpenAI is intensifying rather than settling, with each side's cuts triggering counter-cuts in adjacent product tiers rather than staying contained to a single model.