Estimate from the version's published specs, benchmarks and reviews, 0-5; scores are only comparable within one criteria year. Scored as launched (Claude 3 Opus, 4 Mar 2024): 200K-token context (Anthropic; >1M for select customers only); list price $15 in / $75 out per 1M tokens = $30 blended at 3:1; text and image input (Anthropic, TechCrunch). Reasoning null: the Artificial Analysis Intelligence Index did not exist at launch and AA's current page re-scores Opus 3 on index v4.1, so no launch-version figure was opened; Anthropic's own headline benchmarks (GPQA, MMLU, HumanEval) have no anchor. Coding null: no Terminal-Bench 4.0 result. Speed null: no tokens/s published at launch. Reviews: Ars Technica, TechCrunch (own question-set test of Opus), Zvi, Willison, The Decoder; independent hands-on tests of Opus are mostly early-days impressions.[1]
I'll say off the bat that Opus is among the more helpful chatbots I've played with, at least in the sense that its answers — when it gives answers — are succinct, pretty jargon-free and actionable.
We signed up for Claude Pro to try Opus for ourselves with a few informal tests. Opus feels similar in capability to ChatGPT-4.
Claude 3.0's largest version is in a similar class to GPT-4 and Gemini Advanced. It could plausibly now be the best model for many practical uses, with praise especially coming in on coding and creative writing.
Claude 3 Opus has self-reported benchmark scores that consistently beat GPT-4. This is a really big deal: in the 12+ months since the GPT-4 release no other model has consistently beat it in this way.
the recently released Claude 3 Opus is capable of generating coherent code for entire applications.
| The AI wars heat up with Claude 3, claimed to have "near-human" abilities · 2024. 3. 4. | 0.30 |
|---|---|
| The new Claude 3 model family from Anthropic · 2024. 3. 4. | 0.80 |
| On Claude 3.0 · 2024. 3. 6. | 0.80 |
| We tested Anthropic's new chatbot — and came away a bit disappointed · 2024. 3. 7. | 0.30 |
| Claude 3 writes multi-user browser painting app in 2 minutes and 48 seconds · 2024. 3. 7. | 0.60 |
독자들의 사진으로, 출처가 있는 핀의 미디어와 별도로 표시됩니다. 먹거나 본 것을 평가하고, 레스토랑 핀에서는 사진에 바로 요리를 태그해 보세요.
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[2]의 게시물은 “Mar 4, 2024”로 날짜가 표시돼 있으며 “Opus와 Sonnet은 이제 claude[3].ai와 Claude API에서 이용할 수 있으며, 159개국에서 정식 서비스된다”고 밝혔다. CNBC[4]와 TechCrunch[5]도 같은 월요일에 출시를 보도했다.
Anthropic's[1] Claude[3] 3 model card (the link resolves to the PDF): the Claude 3 models' knowledge cutoff is August 2023 and they are offered through the Claude API, Amazon Bedrock and Google Vertex AI; it carries the full evaluations behind the launch post's table.
Anthropic's[1][2] deprecation history: on 30 June 2025 it notified developers of Claude Opus 3's retirement, and claude-3-opus-20240229 was retired on 5 January 2026 with claude-opus-4-8 as the recommended replacement.
CNBC's same-day report (published 2024-03-04T14:00Z): "Anthropic[1][2] on Monday debuted Claude[3] 3"; Opus outperformed GPT-4 and Gemini Ultra on benchmark tests, it is Anthropic's first multimodal model, Opus and Sonnet are available in 159 countries, and Anthropic's backers include Google, Salesforce and Amazon after about $7.3 billion in funding deals over the past year.
TechCrunch (Kyle Wiggers, 11:50 AM PST, 4 March 2024): Claude[3] 3 is Anthropic's[1][2] first multimodal model, can analyze up to 20 images in one request, starts with a 200,000-token context window (1 million for select customers), answers from data before August 2023, and Opus costs $15 per million input and $75 per million output tokens.
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 3 Opus Released
| 벤치마크 | Claude 3 Opus | Claude 3 Sonnet | Claude 3 Haiku | GPT-4 | GPT-3.5 | Gemini 1.0 Ultra | Gemini 1.0 Pro |
|---|---|---|---|---|---|---|---|
| MMLU (학부 수준 지식) | 86.8% 5 shot | 79.0% 5-shot | 75.2% 5-shot | 86.4% 5-shot | 70.0% 5-shot | 83.7% 5-shot | 71.8% 5-shot |
| GPQA, Diamond (대학원 수준 추론) | 50.4% 0-shot CoT | 40.4% 0-shot CoT | 33.3% 0-shot CoT | 35.7% 0-shot CoT | 28.1% 0-shot CoT | — | — |
| GSM8K (초등 수학) | 95.0% 0-shot CoT | 92.3% 0-shot CoT | 88.9% 0-shot CoT | 92.0% 5-shot CoT | 57.1% 5-shot | 94.4% Maj1@32 | 86.5% Maj1@32 |
| MATH (수학 문제 해결) | 60.1% 0-shot CoT | 43.1% 0-shot CoT | 38.9% 0-shot CoT | 52.9% 4-shot | 34.1% 4-shot | 53.2% 4-shot | 32.6% 4-shot |
| MGSM (다국어 수학) | 90.7% 0-shot | 83.5% 0-shot | 75.1% 0-shot | 74.5% 8-shot | — | 79.0% 8-shot | 63.5% 8-shot |
| HumanEval (코드) | 84.9% 0-shot | 73.0% 0-shot | 75.9% 0-shot | 67.0% 0-shot | 48.1% 0-shot | 74.4% 0-shot | 67.7% 0-shot |
| DROP, F1 score (텍스트 기반 추론) | 83.1 3-shot | 78.9 3-shot | 78.4 3-shot | 80.9 3-shot | 64.1 3-shot | 82.4 Variable shots | 74.1 Variable shots |
| BIG-Bench-Hard (혼합 평가) | 86.8% 3-shot CoT | 82.9% 3-shot CoT | 73.7% 3-shot CoT | 83.1% 3-shot CoT | 66.6% 3-shot CoT | 83.6% 3-shot CoT | 75.0% 3-shot CoT |
| ARC-Challenge (지식 질의응답) | 96.4% 25-shot | 93.2% 25-shot | 89.2% 25-shot | 96.3% 25-shot | 85.2% 25-shot | — | — |
| HellaSwag (상식) | 95.4% 10-shot | 89.0% 10-shot | 85.9% 10-shot | 95.3% 10-shot | 85.5% 10-shot | 87.8% 10-shot | 84.7% 10-shot |