Estimate from the version's published specs, benchmarks and reviews, 0-5; scores are only comparable within one criteria year. Scored as launched (Claude Haiku 4.5, 15 Oct 2025): 200K-token context and 64K max output (Willison); list price $1 in / $5 out per 1M tokens = $2 blended at 3:1 (Anthropic); text and image input (DataCamp page was Cloudflare-blocked; Willison and Anthropic's model docs describe image input). Anthropic: SWE-bench Verified 73.3%, OSWorld 50.7%, original Terminal-Bench about 41% (Terminus 2 harness; not Terminal-Bench 4.0, so coding null). Speed null: Anthropic says 'more than twice the speed' of Sonnet 4 and swyx measured ~3.5x faster than Sonnet 4.5, neither is tokens/s. Reasoning null (no launch-version AA index opened). Reviews: Willison, Zvi (AI #138), Latent Space AINews (aggregated hands-on reports), Analytics Vidhya (a few real-world prompts), TechCrunch.[1]
Haiku 4.5 certainly isn't that, it looks like they're continuing to focus squarely on the "great at code" part of the market.
The use case here is that it is fast and cheaper, so if you need things like coding subagents this could be the right tool for you.
Early hands-on reports suggest Haiku 4.5 materially improves iteration speed and UX.
In real-world tests, Haiku 4.5 demonstrated strong coding proficiency, logical reasoning, and the ability to adapt to user feedback, making it suitable for both developers and enterprises.
Haiku scored 73% on SWE-Bench verified and 41% on the command-line-focused Terminal-Bench — below Sonnet 4.5, but on par with Sonnet 4, GPT-5, and Gemini 2.5 in each case.
| Introducing Claude Haiku 4.5 · 2025. 10. 15. | 0.20 |
|---|---|
| Claude Haiku 4.5 · 2025. 10. 15. | 0.60 |
| Anthropic launches new version of scaled-down 'Haiku' model · 2025. 10. 15. | 0.40 |
| AI #138 Part 1: The People Demand Erotic Sycophants · 2025. 10. 16. | 0.50 |
| Claude Haiku 4.5 is Here… and it's BETTER than Sonnet 4.5? · 2025. 10. 24. | 0.60 |
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첫 번째 항목은 항상 핀의 출처입니다. 전체 신뢰도는 각 자료가 위의 시작·종료 시각을 얼마나 확실히 뒷받침하는지에 대한 가중 평균이며, 자료는 최신 자료보다 180일 오래될 때마다 가중치가 절반이 됩니다.
Anthropic's[1][3][4] model table: Claude Haiku 4.5 (claude-haiku-4-5-20251001), "The fastest model with near-frontier intelligence", $1 / $5 per MTok, extended thinking, a 200K-token context window, 64K max output, February 2025 reliable knowledge cutoff, July 2025 training data cutoff, retirement not sooner than 15 October 2026.
Anthropic's[1][4] system card: Haiku 4.5 was trained on public internet data up to February 2025, and its safety testing supports release under the AI Safety Level 2 (ASL-2) standard.
Anthropic's[1][3] Haiku model page: Haiku 4.5 is on Claude[2].ai (web, iOS, Android), the Claude Platform, Amazon Bedrock, Google Cloud's Vertex AI, Microsoft Foundry and Claude Code; pricing starts at $1 per million input and $5 per million output tokens, with up to 90% savings from prompt caching and 50% from batch processing.
CNBC (published 2025-10-15T17:00Z): "Anthropic[1][3][4] on Wednesday announced Claude[2] Haiku 4.5", available to free users and now the cheapest model for paid users; it is better at using computers than Sonnet 4, and CPO Mike Krieger says "It punches way above its weight".
빠지거나 잘못된 점이 있나요? 이 핀을 뒷받침하는 링크, 다른 시작일이나 종료일과 그 이유, 빠지거나 잘못된 사실을 자신의 말로 알려주세요. AI가 이 핀의 출처와 대조하고 더 나은 자료를 찾아, 뒷받침하는 페이지를 추가합니다. 핀 자체의 출처가 여전히 가장 중요하게 반영됩니다. AI는 이미지도 확인합니다. 다른 것을 보여주거나 제대로 보여주지 못하는 이미지는 뒤로 옮기거나 교체합니다.
원본 제목: Claude Haiku 4.5 Released
| 벤치마크 | Claude Sonnet 4.5 | Claude Haiku 4.5 | Claude Sonnet 4 | GPT-5 | Gemini 2.5 Pro |
|---|---|---|---|---|---|
| SWE-bench Verified (에이전트 코딩) | 77.2% | 73.3% | 72.7% | 72.8% GPT-5 (high), 74.5% GPT-5-Codex | 67.2% |
| Terminal-Bench (에이전트 터미널 코딩) | 50.0% | 41.0% | 36.4% | 43.8% | 25.3% |
| τ2-bench (에이전트 도구 사용) | 소매 86.2%, 항공 70.0%, 통신 98.0% | 소매 83.2%, 항공 63.6%, 통신 83.0% | 소매 83.8%, 항공 63.0%, 통신 49.6% | 소매 81.1%, 항공 62.6%, 통신 96.7% | — |
| OSWorld (컴퓨터 사용) | 61.4% | 50.7% | 42.2% | — | — |
| AIME 2025 (고등학교 수학 경시대회) | 100% (python), 87.0% (no tools) | 96.3% (python), 80.7% (no tools) | 70.5% | 99.6% (python), 94.6% (no tools) | 88.0% |
| GPQA Diamond (대학원 수준 추론) | 83.4% | 73.0% | 76.1% | 85.7% | 86.4% |
| MMMLU (다국어 질의응답) | 89.1% | 83.0% | 86.5% | 89.4% | — |
| MMMU, validation (시각적 추론) | 77.8% | 73.2% | 74.4% | 84.2% | 82.0% |