Claude 1
Anthropic's First Public Model
Anthropic's first publicly available chatbot, launched four months after ChatGPT and three weeks after GPT-4. Same conversational interface, but with longer context and a different alignment recipe (Constitutional AI) that produced a more cautious, more verbose persona.
Intelligence
Good
Speed
Slow
42 tok/s output
Cost
High
$15.00 in / $75.00 out
Context
9K
Up to 9,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Why it matters
Claude 1 made the LLM API market a market — i.e., something with competing vendors, comparison-shopping, and migration paths. Every enterprise AI procurement decision since 2023 implicitly assumes the existence of at least one credible alternative to the dominant vendor. That assumption starts here.
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Generative
Produces images, video, audio, or other media.
Context Window
9k tokens
≈ short doc
4k Chat 聊天
32k Long docs 长文档
128k Books 整本书
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
9k
Availability
API
Available
Product / App
Available
Open Source
Not released
Enterprise
Contact sales
Pricing Model
Pay per token
Input and output billed separately.
Pay-per-token Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
2.2
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- Original Claude 1 — historical baseline; superseded by Claude 3+.
- Three tiers (Opus / Sonnet / Haiku) — first time the same family covered flagship-quality and ultra-fast in one release
- Opus Needle-in-Haystack: 99.4% recall average, 98.3% even at 200K tokens — long-context that genuinely works
- Opus 90.5% one-shot, 89.2% zero-shot — set new state-of-the-art on multiple expert benchmarks
Best use cases
- Long-document analysis (200K context with reliable retrieval)
- Vision + text tasks where Opus / Sonnet outperform GPT-4V
- Deployments needing a tier-aware family (Haiku triage → Sonnet workflow → Opus hard cases)
Tools to try
Not ideal for
- Bleeding-edge reasoning by 2025 — superseded by Claude 3.5 / 3.7 / 4 family
- Self-hosted / open-weights deployments