The first entry is always the pin's source. Overall confidence is a weighted average of how firmly each reference supports the start and end times used above; a reference counts half as much for every 180 days older than the newest.
The model card carries no launch date, so the date is the launch post's: "Introducing the next generation of Claude, Mar 4, 2024" - "Opus and Sonnet are now available to use in claude.ai and the Claude API"; AWS's post of 04 MAR 2024 announces "the availability of Anthropic's[3] Claude 3 Sonnet today in Amazon[4] Bedrock".
The Sonnet table lists Claude 3 Sonnet as released 4 March 2024 and discontinued; the article adds that when Anthropic[1][3] retired it in July 2025 around 200 people gathered in San Francisco for a "funeral".
Anthropic's[1] launch post, dated "Mar 4, 2024": "Opus and Sonnet are now available to use in claude.ai and the Claude API which is now generally available in 159 countries", with Sonnet at $3/$15 per million tokens and a 200K context window, powering the free claude.ai. The model card that is the source carries no launch date, so this row dates the pin.
AWS News Blog post by Channy Yun, 04 MAR 2024: "We're also announcing the availability of Anthropic's[1][3] Claude 3 Sonnet today in Amazon Bedrock, with Claude 3 Opus and Claude 3 Haiku coming soon"; Sonnet is two times faster than Claude 2 and 2.1.
TechCrunch's same-day report: Opus and Sonnet "are available now on the web and via Anthropic's[1][3] dev console and API, Amazon's[4] Bedrock platform and Google's Vertex AI"; Claude 3 is Anthropic's first multimodal model, takes up to 20 images per request, cannot identify people and answers only from data before August 2023.
Something missing or wrong? Say it in your own words: a link that backs this pin up, a different start or end date and why, or a fact it lacks or gets wrong. The AI checks it against this pin's sources, searches for better ones, and adds any page that backs you up. The pin's own sources still count most. A picture that shows something else, or shows it badly, is looked at too, and moved down or replaced.
| API price per 1M tokens (input / output) | $3 / $15 |
|---|
Prices and context from Anthropic's Claude 3 launch post; chart figures from the Claude 3 model card, where competitor numbers are as reported by their providers with differing settings. The post says all three models can accept inputs over 1M tokens for select customers and gives no maximum output. · source
In an Elo-based test, Claude Sonnet (the mid-sized version) was about 100 Elo points better than Claude 2.1.
In practical applications, especially in data science, Claude 3 Sonnet has demonstrated superior performance compared to ChatGPT 3.5.
Sonnet is twice as fast as Claude 2, but with "higher levels of intelligence."
| Anthropic unveils new "Claude 3" AI models to beat OpenAI and Google · Mar 4, 2024 | 0.60 |
|---|---|
| On Claude 3.0 · Mar 6, 2024 | 0.70 |
| Claude vs. ChatGPT: Which AI Chatbot is Best for Everyday Tasks? · Mar 9, 2024 | 0.50 |
| Benchmark | Claude 3 Opus | Claude 3 Sonnet | Claude 3 Haiku | GPT-4 | GPT-3.5 | Gemini 1.0 Ultra | Gemini 1.0 Pro |
|---|---|---|---|---|---|---|---|
| Undergraduate level knowledge (MMLU) | 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) |
| Graduate level reasoning (GPQA, Diamond) | 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) | — | — |
| Grade school math (GSM8K) | 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 problem-solving (MATH) | 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) |
| Multilingual math (MGSM) | 90.7% (0-shot) | 83.5% (0-shot) | 75.1% (0-shot) | 74.5% (8-shot) | — | 79.0% (8-shot) | 63.5% (8-shot) |
| Code (HumanEval) | 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) |
| Reasoning over text (DROP, F1 score) | 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) |
| Mixed evaluations (BIG-Bench-Hard) | 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) |
| Knowledge Q&A (ARC-Challenge) | 96.4% (25-shot) | 93.2% (25-shot) | 89.2% (25-shot) | 96.3% (25-shot) | 85.2% (25-shot) | — | — |
| Common knowledge (HellaSwag) | 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) |