On October 7, 2026, Anthropic released Claude Haiku 5.5, its new small model, on the Claude Platform, AWS, Google Cloud and Microsoft Azure under the model id claude-haiku-5-5.
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What Anthropic released
On October 7, 2026, Anthropic released Claude Haiku 5.5, its new small model, on the Claude Platform, AWS, Google Cloud and Microsoft Azure under the model id claude-haiku-5-5. The headline is the price. For prompts up to 100,000 tokens it costs $0.10 per million input tokens and $0.50 per million output, 90% less than Haiku 4.5 and exactly what OpenAI charges for GPT-6 Luna. It is also the first Haiku with adjustable effort levels, from low to max, and it can drive a computer or a browser through a beta in Anthropic's SDKs.
The 100,000-token line is the whole pricing story
Haiku 5.5 has two prices. Under 100,000 tokens of prompt it is the cheapest Claude has ever been. Over it, every token costs five times as much: $0.50 in and $2.50 out. Anthropic says about 90% of Haiku 4.5 requests fall under the line, which is why it describes the average saving as "around 75%" rather than 90%.
| Per million tokens | Input | Output | Cache reads |
|---|---|---|---|
| Haiku 5.5, up to 100K | $0.10 | $0.50 | $0.01 |
| Haiku 5.5, over 100K | $0.50 | $2.50 | $0.05 |
| GPT-6 Luna | $0.10 | $0.50 | See OpenAI |
| Haiku 4.5 | $1.00 | $5.00 | $0.10 |
| Sonnet 5.5 | $2.00 | $10.00 | $0.10 |
Two smaller details change the real bill. Haiku 5.5 uses the newer tokenizer from Sonnet 5.5 and Opus 5.5, which Anthropic says "uses slightly more tokens per task," so the same prompt counts as a little more than it did on Haiku 4.5. And on the same day Anthropic halved Sonnet 5.5's cache reads from $0.20 to $0.10 per million, which it estimates cuts the cost of a typical agentic task by about 20%.
How it compares with GPT-6 Luna
At the same price, the question is which model does more. On Anthropic's own table, Haiku 5.5 is ahead of Luna on every benchmark both were run on:
| Benchmark | Haiku 5.5 | GPT-6 Luna | Haiku 4.5 |
|---|---|---|---|
| GDPval-AA v2.1 (knowledge work) | 1620 | 1437 | 735 |
| OSWorld 2.1 (computer use) | 72.4% | 48.9% | 15.7% |
| Terminal-Bench 4.0 (agentic coding) | 39.2% | 16.4% | 0.0% |
| FrontierCode 1.1 | 46.4% | 42.4% | Not reported |
| Chartography (visual reasoning) | 46.4% | 29.1% | 6.4% |
Those are Anthropic's numbers, run by Anthropic. The independent check is the Artificial Analysis Intelligence Index, which as of October 8 scores Haiku 5.5 at 43.4 on max effort against 38.1 for GPT-6 Luna on max. The gap is smaller than Anthropic's tables suggest, but it runs the same way. Both sit well below Sonnet 5.5 at 56.0, which is what twenty times the input price buys. All three are on the leaderboard.
The biggest gaps are in computer use and agentic coding, the work small models were weakest at. On Terminal-Bench 4.0, Haiku 4.5 scored zero. That is the real change in this release: a small model that can now do agent work, not only fast answers.
What to watch
- Long prompts. If your requests regularly cross 100,000 tokens, such as long documents or deep agent histories, you pay $0.50 and $2.50 per million, and the comparison with Luna changes. Trim context or cache it.
- Security work. Anthropic has tightened Haiku 5.5's cybersecurity limits compared with Haiku 4.5. It allows a wider range of defensive tasks but blocks penetration testing. If that is your use, check before you migrate.
- Refusals. According to Unite.AI's reading of the system card, Anthropic's automated audits found Haiku 5.5 over-refused more than any other model it tested, and Anthropic recommends developers add their own safeguards, especially with thinking turned off.
- Effort settings. The default is medium. The index score above is max effort; at medium the same index puts it at 34.5. Pick the level per task rather than leaving the default everywhere.
Should you switch?
The listing is at Claude Haiku 5.5, alongside GPT-6 Luna and Haiku 4.5. For keeping agent bills under control, see AI agent cost control and the LLM pricing comparison. The launch is in the October 7 briefing.
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