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Lance Martin
c30d329f58
* Sync claude-api skill with latest upstream updates - Add token-counting.md and SKILL.md trigger description update - Add auth guidance: env credential resolution, ant auth login, OAuth/WIF doc links, 401 causes - Add mid-conversation system messages (beta) to prompt-caching, agent-design, SKILL.md, Python/TS READMEs - Add cache pre-warming (max_tokens: 0) section to prompt-caching - Add Managed Agents pre-flight viability check to onboarding and overview - Add Bedrock model-ID section to model-migration; add Bedrock row to live-sources - Add /claude-api migrate subcommand row and migrate-entry callout - Fix MA networking config: limited type with allow_package_managers/allow_mcp_servers - Bump MA create-operations rate limit to 300 RPM - Fix MA SDK drift: sessions.events.stream(), event.name, typed event arrays - Add SDK coverage: stop_details, error .type, C# tool runner + MA support, Go model constants, Java 2.34.0, client config, response helpers, auto-pagination, advisor tool - Move Sonnet 4 / Opus 4 to deprecated in models.md * Add Anthropic CLI and Claude Platform on AWS docs to claude-api skill - Add shared/anthropic-cli.md: install, auth profiles, OAuth scopes, command structure, version-controlled Managed Agents resources, credential traps - Add shared/claude-platform-on-aws.md: AnthropicAWS clients, SigV4 auth, workspace_id, regions, feature availability - Restore cross-references to both files throughout SKILL.md and the managed-agents docs (previously rewritten to live-sources.md pointers) - Restore Claude Platform on AWS provider taxonomy in SKILL.md, the migration-guide section, and live-sources rows
57 lines
1.6 KiB
Markdown
57 lines
1.6 KiB
Markdown
# Token Counting
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Use the `count_tokens` endpoint (`POST /v1/messages/count_tokens`) for accurate
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token counts against Claude models. Token counts are **model-specific** — pass
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the same model ID you'll use for inference.
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**Do not use `tiktoken`.** It's OpenAI's tokenizer. It undercounts Claude
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tokens by ~15–20% on typical text, and by much more on code or non-English
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input. Any estimate from `tiktoken`, `gpt-tokenizer`, or similar is wrong for
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Claude.
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## Count a file or string
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```python
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from anthropic import Anthropic
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client = Anthropic()
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resp = client.messages.count_tokens(
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model="claude-opus-4-8",
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messages=[{"role": "user", "content": open("CLAUDE.md").read()}],
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)
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print(resp.input_tokens)
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```
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TypeScript: `await client.messages.countTokens({model, messages})` →
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`.input_tokens`. See `{lang}/claude-api/README.md` for other SDKs.
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## CLI
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```sh
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ant messages count-tokens --model claude-opus-4-8 \
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--message '{role: user, content: "@./CLAUDE.md"}' \
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--transform input_tokens -r
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```
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## Diffing a file across two versions
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The endpoint is stateless — count each version separately and subtract:
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```python
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from anthropic import Anthropic
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import subprocess
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client = Anthropic()
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def count(text: str) -> int:
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return client.messages.count_tokens(
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model="claude-opus-4-8",
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messages=[{"role": "user", "content": text}],
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).input_tokens
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before = subprocess.check_output(["git", "show", "HEAD:CLAUDE.md"], text=True)
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after = open("CLAUDE.md").read()
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print(count(after) - count(before))
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```
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Full docs: see the Token Counting entry in `shared/live-sources.md`.
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