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 |
読者の写真で、出典付きのピンのメディアとは別に表示されます。食べたものや見たものを評価でき、レストランのピンでは写真上に料理をタグ付けできます。
最初の項目は常にピンの出典です。全体の確度は、各資料が上記の開始・終了時刻をどれだけ強く裏付けているかの加重平均です。最新の資料より180日古くなるごとに、重みは半分になります。
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".
足りない点や誤りがありますか? このピンを裏付けるリンク、別の開始日・終了日とその理由、欠けている情報や誤っている情報を、自由に書いてください。AIがこのピンの出典と照らし合わせ、より良い情報源を探し、裏付けとなるページがあれば参考資料として追加します。ピン自身の出典が引き続き最も重視されます。AIは画像も確認します。別のものが写っている画像や写りの悪い画像は、後ろに回すか差し替えます。
元のタイトル: Claude Haiku 4.5 Released
| ベンチマーク | Claude Sonnet 4.5 | Claude Haiku 4.5 | Claude Sonnet 4 | GPT-5 | Gemini 2.5 Pro |
|---|---|---|---|---|---|
| SWE-bench Verified (エージェント型コーディング) | 77.2% | 73.3% | 72.7% | 72.8% GPT-5 (high), 74.5% GPT-5-Codex | 67.2% |
| Terminal-Bench (エージェント型ターミナルコーディング) | 50.0% | 41.0% | 36.4% | 43.8% | 25.3% |
| τ2-bench (エージェント型ツール利用) | 小売 86.2%, 航空 70.0%, 通信 98.0% | 小売 83.2%, 航空 63.6%, 通信 83.0% | 小売 83.8%, 航空 63.0%, 通信 49.6% | 小売 81.1%, 航空 62.6%, 通信 96.7% | — |
| OSWorld (コンピュータ操作) | 61.4% | 50.7% | 42.2% | — | — |
| AIME 2025 (高校数学コンテスト) | 100% (python), 87.0% (ツールなし) | 96.3% (python), 80.7% (ツールなし) | 70.5% | 99.6% (python), 94.6% (ツールなし) | 88.0% |
| GPQA Diamond (大学院レベルの推論) | 83.4% | 73.0% | 76.1% | 85.7% | 86.4% |
| MMMLU (多言語問答) | 89.1% | 83.0% | 86.5% | 89.4% | — |
| MMMU, validation (視覚的推論) | 77.8% | 73.2% | 74.4% | 84.2% | 82.0% |