AWS Bedrock’s CountTokens API reached GA in August 2025 and is free to call. It covers Claude 3.5 through Claude Opus 4 and Sonnet 4 on Bedrock, accepts the same payload formats as InvokeModel and Converse, and returns a single inputTokens integer. The critical engineering constraint: the Anthropic SDK’s AsyncAnthropicBedrock client does not proxy the Bedrock CountTokens endpoint as of mid-2026 — teams must call the boto3 bedrock-runtime client directly. A second, less-documented gap affects tool-heavy agentic workloads: while the Bedrock API accepts toolConfig in a CountTokens request, the returned count may not precisely reflect how the model’s actual inference path accounts for tool schema tokens, because tool definitions are processed through a special formatting layer before tokenization. For high-accuracy pre-flight budgeting on tool-heavy requests, verify the CountTokens result empirically against a test inference call.
Client-side estimation via tiktoken or similar libraries runs at ±5–10% accuracy for Claude models because Anthropic’s tokenizer is not public. The new tokenizer shipped with Claude Opus 4.7+ (also used in Claude Fable 5 and Mythos 5) produces roughly 30–35% more tokens for the same text than pre-Opus-4.7 models — making stale tiktoken estimates increasingly dangerous in mixed-model deployments.
LiteLLM provides cost_per_token() and completion_cost() for post-call accounting and pre-call estimation, with a configurable price registry. A January 2026 bug fix corrected incorrect cached-token billing for Bedrock. Custom pricing overrides are available via YAML config when the community-maintained registry lags actual Bedrock price changes.
1. Bedrock CountTokens API
1.1 Endpoint and Request Format
POST https://bedrock-runtime.{region}.amazonaws.com/model/{modelId}/count-tokens
Content-Type: application/json
{
"input": {
"converse": {
"messages": [...],
"system": [...],
"toolConfig": {...}
}
}
}
The modelId path parameter is the Bedrock model identifier or cross-region inference profile ARN, for example anthropic.claude-sonnet-4-5-20251001-v1:0. The input field is a tagged union: specify either input.invokeModel.body (raw bytes, the body you would send to InvokeModel) or input.converse (structured conversation format matching the Converse API).
Response:
{ "inputTokens": 403 }
The count is model-specific. Bedrock routes the request to the model’s tokenizer and returns the same count that would appear in billing for an equivalent inference call.
1.2 Boto3 Usage
import boto3
import json
bedrock = boto3.client("bedrock-runtime", region_name="us-west-2")
# InvokeModel format
model_id = "anthropic.claude-sonnet-4-5-20251001-v1:0"
body = json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": 1024,
"messages": [{"role": "user", "content": "Summarize the Q4 earnings report."}]
})
response = bedrock.count_tokens(
modelId=model_id,
input={"invokeModel": {"body": body.encode()}}
)
print(f"Input tokens: {response['inputTokens']}") # e.g., 18
# Converse format (multi-turn with system prompt)
response = bedrock.count_tokens(
modelId=model_id,
input={
"converse": {
"messages": [
{"role": "user", "content": [{"text": "What is the capital of France?"}]},
{"role": "assistant", "content": [{"text": "Paris."}]},
{"role": "user", "content": [{"text": "And Germany?"}]},
],
"system": [{"text": "You are a geography expert."}],
}
}
)
print(f"Input tokens: {response['inputTokens']}") # e.g., 39
Note the 7-token overhead the Bedrock wrapper adds to raw text inputs. The ttok4bedrock library (open source) compensates for this automatically when used as a drop-in replacement for the ttok CLI tool.
1.3 Supported Models (mid-2026)
CountTokens is supported on:
- Anthropic Claude 3.5 Haiku (
anthropic.claude-3-5-haiku-*) - Anthropic Claude 3.5 Sonnet v1 and v2 (
anthropic.claude-3-5-sonnet-*) - Anthropic Claude 3.7 Sonnet (
anthropic.claude-3-7-sonnet-*) - Anthropic Claude Opus 4 / Sonnet 4 (
anthropic.claude-opus-4-*,anthropic.claude-sonnet-4-*)
Models outside this list return a ValidationException. Amazon Nova models are not listed in the CountTokens supported set as of this writing; verify against the current Bedrock documentation before building Nova-based cost estimation pipelines.
1.4 Pricing and Rate Limits
CountTokens calls are free. They do not consume input or output tokens. They are subject to independent RPM limits, separate from the inference endpoint’s rate limits.
| Usage Tier | CountTokens RPM |
|---|---|
| 1 | 100 |
| 2 | 2,000 |
| 3 | 4,000 |
| 4 | 8,000 |
At Tier 1, a pre-flight CountTokens call before every inference call halves your effective throughput on a single-threaded pipeline. For high-volume deployments, either upgrade tiers or limit pre-flight checks to requests above a token-size threshold.
1.5 The Tool Schema Token Gap
This is the most consequential documented limitation for agentic workloads.
When you include toolConfig in a CountTokens request using the converse input format, the API accepts the request and returns an inputTokens count. However, the returned count may undercount the actual inference tokens for tool-heavy requests. The reason: Claude processes tool definitions through a special XML-like formatting layer before tokenization. That formatted representation can be substantially larger than the raw JSON schema. A single tool definition with a detailed description and multi-property inputSchema commonly contributes 200–600 tokens to actual inference, but the CountTokens response reflects a lower count based on the raw schema bytes.
Empirical calibration pattern:
import boto3, json
bedrock = boto3.client("bedrock-runtime", region_name="us-west-2")
model_id = "anthropic.claude-sonnet-4-5-20251001-v1:0"
tools = [
{
"toolSpec": {
"name": "query_database",
"description": (
"Execute a read-only SQL query against the financial data warehouse. "
"Returns rows as a list of dicts. Maximum 10,000 rows per call. "
"Use for balance sheet, P&L, and cash flow lookups."
),
"inputSchema": {
"json": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "Valid SQL SELECT statement"},
"limit": {"type": "integer", "description": "Row limit, default 100"},
"timeout_ms": {"type": "integer", "description": "Query timeout in ms"},
},
"required": ["query"],
}
},
}
}
]
def calibrate_tool_overhead(model_id: str, tools: list, n_samples: int = 5) -> float:
"""
Returns a correction multiplier for CountTokens when tools are present.
Run once per tool catalog change; cache the result.
"""
base_message = [{"role": "user", "content": [{"text": "What is 2+2?"}]}]
# Count without tools
r_no_tools = bedrock.count_tokens(
modelId=model_id,
input={"converse": {"messages": base_message}}
)
# Count with tools
r_with_tools = bedrock.count_tokens(
modelId=model_id,
input={"converse": {"messages": base_message, "toolConfig": {"tools": tools}}}
)
count_diff = r_with_tools["inputTokens"] - r_no_tools["inputTokens"]
# Run a real inference call to get actual tool schema token consumption
resp = bedrock.converse(
modelId=model_id,
messages=base_message,
toolConfig={"tools": tools},
inferenceConfig={"maxTokens": 5},
)
actual_tool_tokens = (
resp["usage"]["inputTokens"] - r_no_tools["inputTokens"]
)
multiplier = actual_tool_tokens / count_diff if count_diff > 0 else 1.0
print(f"CountTokens tool diff: {count_diff}, actual: {actual_tool_tokens}, multiplier: {multiplier:.2f}")
return multiplier
# Typical result: multiplier between 1.3 and 2.1 depending on tool verbosity
TOOL_CORRECTION_FACTOR = calibrate_tool_overhead(model_id, tools)
Store TOOL_CORRECTION_FACTOR per model and tool catalog version. Apply it when computing the adjusted estimate:
def adjusted_token_estimate(raw_count: int, has_tools: bool, tool_correction: float) -> int:
if has_tools:
return int(raw_count * tool_correction)
return raw_count
1.6 Anthropic SDK Gap on Bedrock
The anthropic Python SDK’s AsyncAnthropicBedrock client does not expose client.messages.count_tokens() for Bedrock-routed requests as of mid-2026. Calling it raises:
UserError: AsyncAnthropicBedrock client does not support `count_tokens` api
Workaround: use the boto3 bedrock-runtime client directly, as shown above. The boto3 count_tokens() method is lower-level but complete.
2. Pre-Flight Cost Guardrails
2.1 The Pattern
Pre-flight cost checking intercepts a request before inference, calls CountTokens, computes an estimated cost, compares it against a per-request or per-team budget, and either allows or rejects the call.
request → count_tokens() → estimate_cost() → budget_check() → [proceed | reject]
This adds one RTT to the critical path. For typical Bedrock regions, CountTokens latency is 40–120ms. For latency-sensitive applications (sub-500ms SLA), this doubles the overhead on the non-inference path. For batch, analytics, and document-processing pipelines where accuracy matters more than latency, the trade-off is almost always worth it.
2.2 When Pre-Flight Makes Sense vs. Post-Hoc
| Scenario | Prefer Pre-Flight | Prefer Post-Hoc |
|---|---|---|
| Hard per-request budget cap | ✓ | |
| Large document ingestion pipeline | ✓ | |
| Interactive chat, latency < 300ms | ✓ | |
| Streaming with abort on overrun | ✓ (mid-stream) | |
| Post-hoc quota reconciliation | ✓ | |
| Anomaly detection on unusual prompts | ✓ | |
| Team cost allocation (soft limit) | ✓ |
2.3 LiteLLM Middleware with Pre-Flight CountTokens
LiteLLM’s proxy supports custom hooks. The following pattern implements a CustomLogger that intercepts async_pre_call_hook to run CountTokens before forwarding to Bedrock:
# bedrock_preflight_guard.py
import boto3
import json
import logging
from typing import Any, Optional
from litellm.integrations.custom_logger import CustomLogger
import litellm
logger = logging.getLogger(__name__)
BEDROCK_PRICE_PER_M = {
# input, output — USD per 1M tokens, as of Q2 2026
"anthropic.claude-sonnet-4-5-20251001-v1:0": (3.00, 15.00),
"anthropic.claude-3-5-haiku-20241022-v1:0": (0.80, 4.00),
"anthropic.claude-opus-4-20250514-v1:0": (15.00, 75.00),
}
DEFAULT_MAX_COST_USD = 0.50 # per-request hard limit
WARN_THRESHOLD = 0.80 # warn at 80% of limit
class BedrockPreflightGuard(CustomLogger):
"""
LiteLLM custom hook that runs CountTokens before each Bedrock inference call.
Rejects requests that exceed the per-request budget cap.
"""
def __init__(
self,
max_cost_usd: float = DEFAULT_MAX_COST_USD,
region: str = "us-west-2",
tool_correction_factor: float = 1.5,
):
self.max_cost_usd = max_cost_usd
self.tool_correction_factor = tool_correction_factor
self.bedrock = boto3.client("bedrock-runtime", region_name=region)
def _bedrock_model_id(self, litellm_model: str) -> Optional[str]:
"""Strip litellm 'bedrock/' prefix."""
if litellm_model.startswith("bedrock/"):
return litellm_model[len("bedrock/"):]
return None
def _count_tokens(self, model_id: str, messages: list, system: str, tools: list) -> int:
converse_input: dict = {"messages": self._convert_messages(messages)}
if system:
converse_input["system"] = [{"text": system}]
if tools:
converse_input["toolConfig"] = {"tools": tools}
try:
resp = self.bedrock.count_tokens(
modelId=model_id,
input={"converse": converse_input}
)
raw = resp["inputTokens"]
if tools:
raw = int(raw * self.tool_correction_factor)
return raw
except Exception as e:
logger.warning(f"CountTokens failed ({e}); skipping pre-flight check")
return 0
def _convert_messages(self, messages: list) -> list:
"""Convert LiteLLM message format to Bedrock Converse format."""
converted = []
for m in messages:
if m["role"] in ("user", "assistant"):
content = m.get("content", "")
if isinstance(content, str):
converted.append({
"role": m["role"],
"content": [{"text": content}]
})
elif isinstance(content, list):
converted.append({"role": m["role"], "content": content})
return converted
def _estimate_input_cost(self, model_id: str, input_tokens: int) -> float:
prices = BEDROCK_PRICE_PER_M.get(model_id)
if not prices:
return 0.0
input_price_per_m, _ = prices
return (input_tokens * input_price_per_m) / 1_000_000
async def async_pre_call_hook(
self,
user_api_key_dict,
cache,
data: dict,
call_type: str,
) -> dict:
model = data.get("model", "")
model_id = self._bedrock_model_id(model)
if not model_id:
return data # not a Bedrock call; pass through
messages = data.get("messages", [])
system = data.get("system", "")
tools = data.get("tools", [])
token_count = self._count_tokens(model_id, messages, system, tools)
if token_count == 0:
return data # CountTokens failed; fail open
estimated_cost = self._estimate_input_cost(model_id, token_count)
warn_at = self.max_cost_usd * WARN_THRESHOLD
logger.info(
f"PreFlight | model={model_id} | tokens={token_count} "
f"| estimated_input_cost=${estimated_cost:.4f} | cap=${self.max_cost_usd}"
)
if estimated_cost >= self.max_cost_usd:
raise ValueError(
f"Request rejected: estimated input cost ${estimated_cost:.4f} "
f"exceeds per-request cap ${self.max_cost_usd}. "
f"Token count: {token_count}. Reduce prompt size or raise budget."
)
if estimated_cost >= warn_at:
logger.warning(
f"PreFlight WARNING: estimated cost ${estimated_cost:.4f} "
f"is >{int(WARN_THRESHOLD*100)}% of cap ${self.max_cost_usd}"
)
# Attach token estimate to the request metadata for downstream logging
data.setdefault("metadata", {})["preflight_token_count"] = token_count
data["metadata"]["preflight_cost_usd"] = estimated_cost
return data
# Register with LiteLLM proxy
guard = BedrockPreflightGuard(max_cost_usd=0.25, tool_correction_factor=1.6)
litellm.callbacks = [guard]
config.yaml for the LiteLLM proxy:
model_list:
- model_name: claude-sonnet-bedrock
litellm_params:
model: bedrock/anthropic.claude-sonnet-4-5-20251001-v1:0
aws_region_name: us-west-2
litellm_settings:
callbacks: ["bedrock_preflight_guard.guard"]
max_budget: 100.0
budget_duration: 1d
2.4 Latency Impact Profile
A CountTokens call over the same Bedrock regional endpoint typically adds:
- 40–80ms for small requests (< 2,000 tokens)
- 80–150ms for medium requests (2,000–20,000 tokens)
- 150–300ms for large requests (20,000–100,000 tokens)
The latency scales with input size but sub-linearly. For a pipeline with a 2-second inference budget, a 100ms pre-flight adds 5% overhead — acceptable for cost-sensitive workloads. For streaming chat with a first-token latency target under 200ms, skip pre-flight and enforce budgets post-hoc via SpendLogs.
3. Client-Side Token Estimation Without CountTokens
3.1 Why Client-Side Estimation Is Necessary
CountTokens requires a network round trip. For high-frequency pre-filtering (e.g., context window overflow detection on every retrieved chunk before assembly), a local estimate is faster. The trade-off is accuracy.
3.2 tiktoken for Claude Approximation
Anthropic does not publish its tokenizer. The BPE vocabulary is not open-source. The closest approximation uses OpenAI’s tiktoken with cl100k_base encoding (GPT-4 tokenizer):
import tiktoken
_enc = tiktoken.get_encoding("cl100k_base")
def estimate_tokens_tiktoken(text: str) -> int:
"""
Approximate Claude token count using cl100k_base.
Accuracy: ±5–10% for prose. Worse for code, JSON, and structured data.
DO NOT use for billing-grade estimates.
"""
return len(_enc.encode(text))
Systematic errors:
- Code and JSON: tiktoken under-counts by 15–25%. Claude’s tokenizer handles structural characters differently.
- Non-English text: Under-counts by 20–40% for CJK, Arabic, and other non-Latin scripts.
- Tool schemas: tiktoken sees raw JSON; actual inference adds formatting overhead (see Section 1.5).
- Model generation gap: Claude Opus 4.7+ uses a new tokenizer that produces 30–35% more tokens. tiktoken does not track this.
3.3 Anthropic SDK Token Counting (Direct API)
When calling Anthropic’s API directly (not via Bedrock), the SDK exposes a synchronous token counting endpoint:
import anthropic
client = anthropic.Anthropic()
response = client.messages.count_tokens(
model="claude-opus-4-8",
system="You are a financial analyst.",
messages=[
{"role": "user", "content": "Summarize the last 10-K filing."}
],
)
print(response.input_tokens) # Authoritative count for direct API billing
This endpoint is free and rate-limited separately from inference. It does count tool schemas correctly when tools are passed:
response = client.messages.count_tokens(
model="claude-opus-4-8",
tools=[
{
"name": "get_weather",
"description": "Get current weather in a city",
"input_schema": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "City and state"}
},
"required": ["location"],
},
}
],
messages=[{"role": "user", "content": "What's the weather in Chicago?"}],
)
print(response.input_tokens) # 403 — includes tool schema overhead
Note: cache_control blocks are accepted but prompt caching is not applied during token counting — the count does not reflect cached vs. uncached token splits.
3.4 Accuracy Comparison Matrix
| Estimation Method | Text Accuracy | Tool Schema | Images | Documents | Speed |
|---|---|---|---|---|---|
| tiktoken (cl100k_base) | ±5–10% | 30–50% under | Not supported | Not supported | ~0ms |
| Anthropic SDK count_tokens (direct API) | Exact* | Exact | Exact | Exact | 40–100ms RTT |
| Bedrock boto3 count_tokens | Exact* | See §1.5 | Exact | Exact | 40–150ms RTT |
| Rule of thumb (4 chars/token) | ±20–30% | N/A | N/A | N/A | ~0ms |
*“Exact” means the count matches inference billing. Small differences (±1–5 tokens) can appear due to system optimization tokens added by Anthropic, which are counted but not billed.
3.5 The New Tokenizer Problem (Opus 4.7+)
Models from Claude Opus 4.7 onward (including Claude Fable 5 and Mythos 5) use a new tokenizer. The Anthropic documentation notes this produces “roughly 30% more tokens” for the same text. To measure the impact on your workloads:
# Count the same prompt under both tokenizer generations
old_model = "anthropic.claude-3-5-sonnet-20241022-v2:0"
new_model = "anthropic.claude-opus-4-20250514-v1:0"
bedrock = boto3.client("bedrock-runtime", region_name="us-west-2")
prompt_body = json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": 1,
"messages": [{"role": "user", "content": YOUR_PRODUCTION_PROMPT}]
})
r_old = bedrock.count_tokens(
modelId=old_model,
input={"invokeModel": {"body": prompt_body.encode()}}
)
r_new = bedrock.count_tokens(
modelId=new_model,
input={"invokeModel": {"body": prompt_body.encode()}}
)
ratio = r_new["inputTokens"] / r_old["inputTokens"]
print(f"Token ratio new/old: {ratio:.2f}x") # Typically 1.28–1.35x
Do not reuse token counts measured on pre-Opus-4.7 models when migrating to newer models. The context window fit calculations and cost estimates will be wrong.
4. Cost Estimation Formulas
4.1 Bedrock Claude Pricing Reference (Q2 2026)
| Model | Input ($/M) | Output ($/M) | Cache Write 5m ($/M) | Cache Write 1h ($/M) | Cache Read ($/M) |
|---|---|---|---|---|---|
| Claude Opus 4 | $15.00 | $75.00 | $18.75 | $30.00 | $1.50 |
| Claude Sonnet 4.6 | $3.00 | $15.00 | $3.75 | $6.00 | $0.30 |
| Claude Haiku 4.5 | $0.80 | $4.00 | $1.00 | $1.60 | $0.08 |
Batch inference: 50% off all on-demand rates. Cross-region inference profiles: +10% surcharge.
4.2 Standard Text Cost Formula
def cost_standard_text(
input_tokens: int,
output_tokens: int,
model: str,
price_table: dict = None,
) -> float:
"""
Returns estimated USD cost for a standard (no caching, no tools) text call.
price_table keys: model_id -> (input_per_m, output_per_m)
"""
PRICES = price_table or {
"anthropic.claude-opus-4-20250514-v1:0": (15.00, 75.00),
"anthropic.claude-sonnet-4-5-20251001-v1:0": (3.00, 15.00),
"anthropic.claude-3-5-haiku-20241022-v1:0": (0.80, 4.00),
}
input_price, output_price = PRICES.get(model, (3.00, 15.00))
return (input_tokens * input_price + output_tokens * output_price) / 1_000_000
4.3 With Prompt Caching
Prompt caching introduces four token classes per request:
from dataclasses import dataclass
@dataclass
class CacheTokenUsage:
cache_creation_input_tokens_5m: int = 0 # writing a 5-min cache
cache_creation_input_tokens_1h: int = 0 # writing a 1-hour cache
cache_read_input_tokens: int = 0 # reading from cache (any TTL)
input_tokens: int = 0 # standard (uncached) input
output_tokens: int = 0
def cost_with_caching(usage: CacheTokenUsage, model: str) -> float:
"""
Full cost formula when prompt caching is active.
Cache write tokens are billed at 1.25x (5m) or 2.0x (1h) the standard input rate.
Cache read tokens are billed at 0.1x the standard input rate.
"""
PRICES = {
"anthropic.claude-opus-4-20250514-v1:0": {
"input": 15.00, "output": 75.00,
"cache_write_5m": 18.75, "cache_write_1h": 30.00, "cache_read": 1.50
},
"anthropic.claude-sonnet-4-5-20251001-v1:0": {
"input": 3.00, "output": 15.00,
"cache_write_5m": 3.75, "cache_write_1h": 6.00, "cache_read": 0.30
},
"anthropic.claude-3-5-haiku-20241022-v1:0": {
"input": 0.80, "output": 4.00,
"cache_write_5m": 1.00, "cache_write_1h": 1.60, "cache_read": 0.08
},
}
p = PRICES.get(model, PRICES["anthropic.claude-sonnet-4-5-20251001-v1:0"])
cost = (
usage.cache_creation_input_tokens_5m * p["cache_write_5m"]
+ usage.cache_creation_input_tokens_1h * p["cache_write_1h"]
+ usage.cache_read_input_tokens * p["cache_read"]
+ usage.input_tokens * p["input"]
+ usage.output_tokens * p["output"]
) / 1_000_000
return cost
4.4 With Tool Use
Tool use adds three categories of tokens: the tool schema definition tokens (injected before user messages), message tokens, and tool result tokens returned from tool calls.
def cost_with_tools(
schema_tokens_per_turn: int,
message_tokens: int,
tool_result_tokens: int,
output_tokens: int,
model: str,
n_turns: int = 1,
) -> float:
"""
Agentic loop cost estimation.
schema_tokens_per_turn: tokens consumed by tool definitions per inference call
(use calibrated value from §1.5, not raw CountTokens)
message_tokens: conversation message tokens excluding schema
tool_result_tokens: tokens from tool call results fed back as user messages
n_turns: number of model calls in the agentic loop
"""
PRICES = {
"anthropic.claude-sonnet-4-5-20251001-v1:0": (3.00, 15.00),
"anthropic.claude-3-5-haiku-20241022-v1:0": (0.80, 4.00),
}
ip, op = PRICES.get(model, (3.00, 15.00))
# Schema tokens are paid on every turn; result tokens grow with tool calls
total_input = (schema_tokens_per_turn + message_tokens + tool_result_tokens) * n_turns
total_cost = (total_input * ip + output_tokens * n_turns * op) / 1_000_000
return total_cost
# Example: 5-turn agentic loop, 4-tool catalog
schema_tokens = 1_800 # calibrated from §1.5 calibration
message_tokens = 600
tool_result_tokens = 400
output_tokens_per_turn = 300
cost = cost_with_tools(
schema_tokens_per_turn=schema_tokens,
message_tokens=message_tokens,
tool_result_tokens=tool_result_tokens,
output_tokens=output_tokens_per_turn,
model="anthropic.claude-sonnet-4-5-20251001-v1:0",
n_turns=5,
)
print(f"Estimated agentic loop cost: ${cost:.4f}")
Key insight: in a 5-turn loop with a 4-tool catalog, schema tokens can represent 50–60% of total input token costs. Tool catalog pruning (Section 8) directly addresses this.
4.5 Vision: Tile-Based Token Formula
Claude counts image tokens based on image dimensions. The formula:
tokens ≈ (width_px × height_px) / 750
Images are auto-resized before tokenization: if either dimension exceeds 1,568px, the image is scaled down proportionally. Maximum dimensions per image are 8,000×8,000px on the API (5MB on Bedrock).
def image_token_estimate(width_px: int, height_px: int) -> int:
"""
Estimate Claude token count for an image.
Applies auto-resize logic consistent with Claude's vision processing.
"""
MAX_LONG_EDGE = 1568
MAX_PIXELS_FOR_STANDARD_LIMIT = 1600 # ~1600 tokens
# Apply auto-resize
if max(width_px, height_px) > MAX_LONG_EDGE:
scale = MAX_LONG_EDGE / max(width_px, height_px)
width_px = int(width_px * scale)
height_px = int(height_px * scale)
tokens = (width_px * height_px) / 750
return max(int(tokens), 1)
# Examples
print(image_token_estimate(800, 600)) # ~640 tokens
print(image_token_estimate(1920, 1080)) # resized to 1568×882 → ~1847 tokens
print(image_token_estimate(100, 100)) # ~13 tokens
def cost_vision_request(
images: list[tuple[int, int]], # list of (width, height)
text_tokens: int,
output_tokens: int,
model: str,
) -> float:
"""Cost for a request containing images."""
ip, op = {
"anthropic.claude-opus-4-20250514-v1:0": (15.00, 75.00),
"anthropic.claude-sonnet-4-5-20251001-v1:0": (3.00, 15.00),
"anthropic.claude-3-5-haiku-20241022-v1:0": (0.80, 4.00),
}.get(model, (3.00, 15.00))
total_image_tokens = sum(image_token_estimate(w, h) for w, h in images)
total_input = text_tokens + total_image_tokens
return (total_input * ip + output_tokens * op) / 1_000_000
4.6 Document/PDF Token Estimation
PDF token counts depend on content density. Anthropic’s guidance and empirical observation suggest:
- Dense text PDF: ~1,500–2,500 tokens per page
- Sparse/mixed-content PDF: ~800–1,500 tokens per page
- Scanned image PDF (OCR via vision): treated as image tokens per page
def estimate_pdf_tokens(n_pages: int, density: str = "medium") -> int:
"""
Rough estimate for PDF document tokens.
Density options: 'sparse' | 'medium' | 'dense'
For billing-grade estimates, use count_tokens with the actual PDF bytes.
"""
TOKENS_PER_PAGE = {
"sparse": 1_000,
"medium": 1_800,
"dense": 2_400,
}
return n_pages * TOKENS_PER_PAGE.get(density, 1_800)
# For accuracy: pass the actual PDF to count_tokens
# The Anthropic SDK example (direct API):
#
# import base64
# pdf_b64 = base64.b64encode(open("report.pdf","rb").read()).decode()
# response = client.messages.count_tokens(
# model="claude-opus-4-8",
# messages=[{"role":"user","content":[
# {"type":"document","source":{"type":"base64","media_type":"application/pdf","data":pdf_b64}},
# {"type":"text","text":"Summarize this."}
# ]}]
# )
# # A 10-page dense PDF typically returns 18,000–24,000 tokens
5. LiteLLM Cost Estimation
5.1 Core Functions
import litellm
# Post-call cost from a completed response
response = litellm.completion(
model="bedrock/anthropic.claude-sonnet-4-5-20251001-v1:0",
messages=[{"role": "user", "content": "Hello"}],
)
cost = litellm.completion_cost(completion_response=response)
print(f"Actual cost: ${cost:.6f}")
# Pre-call estimate from token counts
prompt_cost, completion_cost = litellm.cost_per_token(
model="bedrock/anthropic.claude-sonnet-4-5-20251001-v1:0",
prompt_tokens=500,
completion_tokens=150,
)
print(f"Input cost: ${prompt_cost:.6f}, Output cost: ${completion_cost:.6f}")
total = prompt_cost + completion_cost
print(f"Total: ${total:.6f}")
5.2 Known Gaps with Bedrock
1. Stale prices in the community registry. LiteLLM’s price map is community-maintained. Bedrock price changes (like the April 2026 Claude pricing update) may lag by weeks. Always verify against the Bedrock pricing page.
2. Tool schema tokens not counted. completion_cost() reads from response.usage, which is what Bedrock reports. If Bedrock’s reported input_tokens doesn’t fully reflect tool schema overhead (see §1.5), LiteLLM inherits the undercount.
3. Cached token billing bug (fixed Jan 2026). LiteLLM v1.83+ fixed a bug where cached tokens were billed at the standard input rate instead of the reduced cache-read rate. If running older LiteLLM versions, upgrade.
4. Missing model mappings. New Bedrock model IDs may not be in the registry, causing completion_cost() to return 0.0 silently. Check:
import litellm
model = "bedrock/anthropic.claude-opus-4-20250514-v1:0"
try:
p = litellm.get_model_cost_map(url="https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json")
if model not in p and model.replace("bedrock/","") not in p:
print(f"WARNING: {model} not in LiteLLM price registry — costs will be 0")
except Exception:
print("Could not fetch price registry")
5.3 Custom Pricing Override
When the registry is stale or a model is missing, override in config.yaml:
model_list:
- model_name: claude-sonnet-bedrock
litellm_params:
model: bedrock/anthropic.claude-sonnet-4-5-20251001-v1:0
aws_region_name: us-west-2
model_info:
input_cost_per_token: 0.000003 # $3.00 per million
output_cost_per_token: 0.000015 # $15.00 per million
# Prompt caching pricing
cache_creation_input_token_cost: 0.000003750 # 1.25x for 5-min cache write
cache_read_input_token_cost: 0.0000003 # 0.1x of input
- model_name: claude-opus-bedrock
litellm_params:
model: bedrock/anthropic.claude-opus-4-20250514-v1:0
aws_region_name: us-west-2
model_info:
input_cost_per_token: 0.000015
output_cost_per_token: 0.000075
cache_creation_input_token_cost: 0.00001875
cache_read_input_token_cost: 0.0000015
Python SDK override:
import litellm
litellm.register_model({
"bedrock/anthropic.claude-sonnet-4-5-20251001-v1:0": {
"input_cost_per_token": 3.00 / 1_000_000,
"output_cost_per_token": 15.00 / 1_000_000,
"max_tokens": 8192,
"max_input_tokens": 200_000,
}
})
6. Budget Enforcement Patterns
6.1 Three-Layer Enforcement Stack
┌─────────────────────────────────────────────────┐
│ Layer 1: Pre-call (CountTokens → reject/allow) │ Hard stop, adds latency
├─────────────────────────────────────────────────┤
│ Layer 2: Mid-stream (token counter → abort) │ Streaming only, cost-efficient
├─────────────────────────────────────────────────┤
│ Layer 3: Post-call (SpendLogs → debit budget) │ Soft enforcement, audit trail
└─────────────────────────────────────────────────┘
6.2 Pre-Call Hard Block
See Section 2.3 for the full LiteLLM middleware implementation. The critical path:
async def check_and_execute(
bedrock_client,
model_id: str,
messages: list,
max_cost_usd: float,
) -> dict:
"""Pre-flight check then inference."""
# Step 1: count tokens
count_resp = bedrock_client.count_tokens(
modelId=model_id,
input={"converse": {"messages": messages}}
)
token_count = count_resp["inputTokens"]
# Step 2: estimate cost (input only; output unknown pre-call)
ip, _ = BEDROCK_PRICE_PER_M.get(model_id, (3.00, 15.00))
est_cost = (token_count * ip) / 1_000_000
if est_cost > max_cost_usd:
raise BudgetExceededError(
f"Preflight: {token_count} tokens → ${est_cost:.4f} > cap ${max_cost_usd}"
)
# Step 3: run inference
return bedrock_client.converse(
modelId=model_id,
messages=messages,
inferenceConfig={"maxTokens": 1024},
)
6.3 Mid-Stream Abort
Streaming allows aborting before the full output is generated. This is the most cost-efficient pattern for output budget control, since pre-flight can’t predict output length:
import boto3
def stream_with_budget(
model_id: str,
messages: list,
max_output_tokens: int,
output_price_per_m: float = 15.00,
max_output_cost_usd: float = 0.10,
) -> str:
"""Stream response, abort if output token budget exceeded."""
bedrock = boto3.client("bedrock-runtime", region_name="us-west-2")
stream = bedrock.converse_stream(
modelId=model_id,
messages=messages,
inferenceConfig={"maxTokens": max_output_tokens},
)
output_tokens = 0
collected = []
abort_reason = None
for event in stream["stream"]:
if "contentBlockDelta" in event:
delta = event["contentBlockDelta"]["delta"].get("text", "")
# Rough streaming token estimate (no CountTokens equivalent for output)
output_tokens += len(delta.split()) # very rough; use for abort only
est_output_cost = (output_tokens * output_price_per_m) / 1_000_000
if est_output_cost > max_output_cost_usd:
abort_reason = f"Output budget ${max_output_cost_usd} exceeded at ~{output_tokens} tokens"
break
collected.append(delta)
if "messageStop" in event:
break
if abort_reason:
collected.append(f"\n[TRUNCATED: {abort_reason}]")
return "".join(collected)
6.4 Post-Call Debit via SpendLogs
LiteLLM’s proxy records actual usage in LiteLLM_SpendLogs after each call. The success_handler hook fires after a successful response, with access to actual token counts from the response metadata:
class SpendLogger(CustomLogger):
"""Debit team budgets in SpendLogs after each successful call."""
def __init__(self, db_url: str):
self.db_url = db_url # Postgres or SQLite connection string
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
usage = response_obj.get("usage", {})
model = kwargs.get("model", "unknown")
team = kwargs.get("metadata", {}).get("team_id", "default")
tags = kwargs.get("metadata", {}).get("tags", [])
input_tokens = usage.get("prompt_tokens", 0)
output_tokens = usage.get("completion_tokens", 0)
cost = litellm.completion_cost(completion_response=response_obj)
# Write to spend log
await self._record_spend(
team_id=team,
model=model,
input_tokens=input_tokens,
output_tokens=output_tokens,
cost_usd=cost,
tags=tags,
latency_ms=(end_time - start_time).total_seconds() * 1000,
)
6.5 Soft vs. Hard Limits
0% 80% 100%
| | |
▼ ▼ ▼
──────────[────────────[~~~~~~~~~~~~[×××××
Normal Warn Block
Soft limit (80%): log warning, notify team, continue serving
Hard limit (100%): raise BudgetExceededError, return HTTP 429 to caller
Implementation in LiteLLM proxy config:
litellm_settings:
# Hard limit: blocks at 100%
max_budget: 50.0 # USD per team per day
budget_duration: 1d
# Soft alert: fires at 80%
soft_budget: 40.0 # 80% of max_budget
# Requires LiteLLM Enterprise for email alerts
alerting: ["slack"]
alerting_threshold: 40.0
7. Batch Workload Cost Pre-Estimation
7.1 Bedrock Batch API Cost Structure
Bedrock Batch Inference offers 50% off on-demand pricing. There is no separate “cost estimation” endpoint for batch jobs — you estimate before submission using CountTokens on a sample.
7.2 Sample-Based Estimation Pattern
import boto3
import json
import random
import math
def estimate_batch_cost(
records: list[dict], # list of {"prompt": str} dicts
model_id: str,
output_tokens_per_record: int,
sample_fraction: float = 0.01,
confidence: float = 0.95,
batch_discount: float = 0.50,
) -> dict:
"""
Estimate total cost of a batch job before submission.
Uses CountTokens on a sample to extrapolate total token usage.
Returns point estimate and confidence interval.
sample_fraction: what % of records to count (0.01 = 1%)
confidence: for CI calculation (0.95 = 95% CI)
batch_discount: 0.50 for Bedrock batch pricing
"""
bedrock = boto3.client("bedrock-runtime", region_name="us-west-2")
ip, op = BEDROCK_PRICE_PER_M.get(model_id, (3.00, 15.00))
n_total = len(records)
n_sample = max(10, int(n_total * sample_fraction))
sample = random.sample(records, min(n_sample, n_total))
token_counts = []
for record in sample:
body = json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": 1,
"messages": [{"role": "user", "content": record["prompt"]}]
})
resp = bedrock.count_tokens(
modelId=model_id,
input={"invokeModel": {"body": body.encode()}}
)
token_counts.append(resp["inputTokens"])
# Point estimates
mean_input = sum(token_counts) / len(token_counts)
variance = sum((x - mean_input) ** 2 for x in token_counts) / len(token_counts)
std_dev = math.sqrt(variance)
# 95% CI using normal approximation (valid for n_sample >= 30)
z = 1.96 if confidence == 0.95 else 1.645
margin = z * std_dev / math.sqrt(len(token_counts))
total_input_low = (mean_input - margin) * n_total
total_input_high = (mean_input + margin) * n_total
total_input_est = mean_input * n_total
total_output = output_tokens_per_record * n_total
def calc_cost(total_in, total_out):
return (total_in * ip + total_out * op) / 1_000_000 * (1 - batch_discount)
return {
"n_records": n_total,
"n_sampled": len(token_counts),
"mean_input_tokens_per_record": round(mean_input, 1),
"std_dev_input_tokens": round(std_dev, 1),
"total_input_tokens_estimate": round(total_input_est),
"cost_estimate_usd": round(calc_cost(total_input_est, total_output), 4),
"cost_low_usd": round(calc_cost(total_input_low, total_output), 4),
"cost_high_usd": round(calc_cost(total_input_high, total_output), 4),
}
# Usage example: 10,000-record document summarization batch
records = [{"prompt": f"Summarize document {i}: ..."} for i in range(10_000)]
result = estimate_batch_cost(
records=records,
model_id="anthropic.claude-3-5-haiku-20241022-v1:0",
output_tokens_per_record=300,
sample_fraction=0.01, # count 100 records
)
print(f"Batch cost estimate: ${result['cost_estimate_usd']}")
print(f"95% CI: ${result['cost_low_usd']} – ${result['cost_high_usd']}")
7.3 When to Use Full-Batch vs. Sample CountTokens
| Criterion | Full Batch CountTokens | Sample CountTokens |
|---|---|---|
| Job value > $500 | ✓ | |
| High variance input lengths | ✓ | |
| < 1,000 records | ✓ | |
| > 100,000 records | ✓ (1% sample) | |
| Budget tight (< 5% tolerance) | ✓ | |
| SLA allows 30+ min pre-flight | ✓ |
For a 100,000-record batch, counting all records before submission adds ~2–4 hours of CountTokens API calls (at Tier 2 rate limits). Sample at 1% for a 95% CI within ±3% of actual cost — sufficient for most budget approval workflows.
7.4 Full-Batch CountTokens Pipeline
import asyncio
import aiofiles
from aiobotocore.session import get_session
async def count_all_tokens_async(
records: list[dict],
model_id: str,
region: str = "us-west-2",
concurrency: int = 50,
) -> list[int]:
"""
Asynchronously count tokens for all records in a batch.
Respects CountTokens RPM limits via semaphore.
At Tier 2 (2000 RPM), use concurrency=30-50 for safe headroom.
"""
session = get_session()
semaphore = asyncio.Semaphore(concurrency)
token_counts = []
async def count_one(record: dict) -> int:
async with semaphore:
async with session.create_client("bedrock-runtime", region_name=region) as client:
body = json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": 1,
"messages": [{"role": "user", "content": record["prompt"]}]
})
resp = await client.count_tokens(
modelId=model_id,
input={"invokeModel": {"body": body.encode()}}
)
return resp["inputTokens"]
tasks = [count_one(r) for r in records]
token_counts = await asyncio.gather(*tasks)
return list(token_counts)
8. Input Token Optimization Techniques
8.1 Prompt Compression: LLMLingua
Microsoft’s LLMLingua family uses a small “compressor” LM to iteratively prune low-information tokens from long prompts. Published benchmarks:
| Method | Max Compression Ratio | Quality Impact | Latency |
|---|---|---|---|
| LLMLingua (2023) | 20x | -1.5% on reasoning | High (requires compressor LM) |
| LLMLingua-2 (2024) | 5x (2-5x practical) | Negligible at 2-3x | 2.9x faster E2E |
| LongLLMLingua | 4x | +21.4% on long-context QA | Moderate |
| Selective Context | 32x | Negligible BERTScore loss | Low |
# pip install llmlingua
from llmlingua import PromptCompressor
compressor = PromptCompressor(
model_name="microsoft/llmlingua-2-bert-base-multilingual-cased-meetingbank",
use_llmlingua2=True,
device_map="cpu",
)
compressed = compressor.compress_prompt(
context=long_document_text,
instruction="Summarize the key financial metrics.",
question="What was Q4 revenue?",
target_token=512, # compress to 512 tokens
condition_compare=True,
reorder_context="sort",
)
print(f"Original: {compressed['origin_tokens']} tokens")
print(f"Compressed: {compressed['compressed_tokens']} tokens")
print(f"Ratio: {compressed['ratio']:.1f}x")
Production caveat: LLMLingua adds compressor LM inference latency (100–500ms on CPU). For real-time applications, pre-compress and cache the compressed version, or use it only in batch pipelines.
8.2 Context Window Right-Sizing
Conversation history accumulates indefinitely in naïve implementations. For most tasks, only the last N turns are relevant:
def trim_conversation_history(
messages: list[dict],
max_history_tokens: int,
model_id: str,
bedrock_client,
) -> list[dict]:
"""
Trim conversation history from the oldest end to stay within token budget.
Always preserves the most recent user message.
"""
if len(messages) <= 2:
return messages
trimmed = list(messages)
while len(trimmed) > 2:
# Check current token count
resp = bedrock_client.count_tokens(
modelId=model_id,
input={"converse": {"messages": trimmed}}
)
if resp["inputTokens"] <= max_history_tokens:
break
# Drop oldest assistant+user pair (preserve system integrity)
trimmed = trimmed[2:]
return trimmed
For sliding window history, a simpler approach using tiktoken for speed:
import tiktoken
_enc = tiktoken.get_encoding("cl100k_base")
def fast_trim_history(
messages: list[dict],
max_tokens: int = 32_000,
) -> list[dict]:
"""Fast history trim using tiktoken approximation (±10% accuracy)."""
def approx_tokens(msg: dict) -> int:
content = msg.get("content", "")
if isinstance(content, str):
return len(_enc.encode(content)) + 4 # role overhead
return 100 # fallback for structured content
total = sum(approx_tokens(m) for m in messages)
trimmed = list(messages)
while total > max_tokens and len(trimmed) > 1:
removed = trimmed.pop(0)
total -= approx_tokens(removed)
return trimmed
8.3 System Prompt Deduplication with Prompt Caching
System prompts sent on every request are prime caching candidates. With a 1-hour cache TTL, the amortized cost of a 5,000-token system prompt across 200 requests drops from $0.075 (standard) to $0.006 (cached reads):
def build_cacheable_message(
system_prompt: str,
dynamic_context: str,
user_message: str,
) -> dict:
"""
Structure a request to maximize prompt cache hits.
Cache strategy:
- System prompt: cacheable (stable, large, expensive)
- Dynamic context: NOT cached (changes per request)
- User message: NOT cached
"""
return {
"system": [
{
"type": "text",
"text": system_prompt,
"cache_control": {"type": "ephemeral"}, # 1-hour TTL
}
],
"messages": [
{
"role": "user",
"content": [
{"type": "text", "text": dynamic_context},
{"type": "text", "text": user_message},
],
}
],
}
Rule of thumb: prompt caching breaks even vs. standard pricing when the same prefix is reused ≥ 2× (for 1h TTL write cost at 2.0x) or ≥ 1.25× (for 5m TTL write cost at 1.25x).
8.4 Tool Catalog Pruning
In a multi-capability agent, sending all available tools on every request inflates input tokens. A large tool catalog of 20 tools can add 3,000–8,000 tokens per inference call. Router-based pruning selects only the relevant subset:
from sentence_transformers import SentenceTransformer
import numpy as np
class ToolRouter:
"""
Select the minimum viable tool subset for a given user request.
Reduces tool schema token overhead by 60-80% on average.
"""
def __init__(self, all_tools: list[dict], model_name: str = "all-MiniLM-L6-v2"):
self.all_tools = all_tools
self.encoder = SentenceTransformer(model_name)
# Pre-encode tool descriptions
descriptions = [t["toolSpec"]["description"] for t in all_tools]
self.tool_embeddings = self.encoder.encode(descriptions, normalize_embeddings=True)
def select_tools(
self,
user_message: str,
top_k: int = 4,
score_threshold: float = 0.3,
) -> list[dict]:
"""
Return the top_k most semantically relevant tools for the user message.
Falls back to all tools if no tool scores above threshold.
"""
query_embedding = self.encoder.encode([user_message], normalize_embeddings=True)
scores = np.dot(self.tool_embeddings, query_embedding.T).flatten()
top_indices = np.argsort(scores)[::-1][:top_k]
selected = [
self.all_tools[i] for i in top_indices
if scores[i] >= score_threshold
]
return selected if selected else self.all_tools[:2] # minimum fallback
# Usage
router = ToolRouter(all_tools=FULL_TOOL_CATALOG)
relevant_tools = router.select_tools(
user_message="What were our gross margins last quarter?",
top_k=3,
)
# For a finance query: returns query_database, get_financials, run_report
# Omits: send_email, create_ticket, schedule_meeting → saves ~1,500 tokens
8.5 Token Reduction Decision Matrix
| Technique | Token Reduction | Quality Risk | Implementation Cost | Use When |
|---|---|---|---|---|
| Conversation trimming | 20–60% | Low | Low | All chat applications |
| System prompt caching | 0% (cost reduction) | None | Low | Stable system prompts |
| Tool catalog pruning | 40–80% of tool tokens | Low | Medium | > 5 tools in catalog |
| LLMLingua 2–3x | 50–67% of context | Negligible | High | Batch, RAG pipelines |
| LLMLingua 10x+ | 90% of context | Medium | High | Extreme token limits only |
| Selective Context | 50–95% | Low–Medium | Medium | Long retrieved context |
| RECOMP extractive | 50–78% | Low | Medium | RAG with multi-document |
9. End-to-End Production Pattern
Combining pre-flight estimation, mid-pipeline trimming, and post-call accounting into a single request handler:
import boto3
import json
import logging
import time
from dataclasses import dataclass, field
from typing import Optional
logger = logging.getLogger(__name__)
@dataclass
class RequestBudget:
max_input_tokens: int = 50_000
max_input_cost_usd: float = 0.20
warn_at_fraction: float = 0.80
tool_correction_factor: float = 1.5
@dataclass
class RequestResult:
content: str
input_tokens: int
output_tokens: int
actual_cost_usd: float
preflight_estimate_tokens: int
preflight_estimate_cost_usd: float
latency_ms: float
class BudgetedBedrockClient:
"""
Production wrapper combining pre-flight, history trimming,
tool routing, and post-call cost accounting.
"""
def __init__(
self,
model_id: str,
region: str = "us-west-2",
budget: Optional[RequestBudget] = None,
):
self.model_id = model_id
self.bedrock = boto3.client("bedrock-runtime", region_name=region)
self.budget = budget or RequestBudget()
ip, op = BEDROCK_PRICE_PER_M.get(model_id, (3.00, 15.00))
self.input_price_per_m = ip
self.output_price_per_m = op
def _preflight(self, messages: list, tools: list) -> tuple[int, float]:
"""Returns (estimated_tokens, estimated_cost_usd)."""
converse_input: dict = {"messages": messages}
if tools:
converse_input["toolConfig"] = {"tools": tools}
resp = self.bedrock.count_tokens(
modelId=self.model_id,
input={"converse": converse_input}
)
raw_tokens = resp["inputTokens"]
if tools:
raw_tokens = int(raw_tokens * self.budget.tool_correction_factor)
est_cost = (raw_tokens * self.input_price_per_m) / 1_000_000
return raw_tokens, est_cost
def converse(
self,
messages: list,
system: Optional[str] = None,
tools: Optional[list] = None,
max_output_tokens: int = 1024,
) -> RequestResult:
tools = tools or []
t0 = time.time()
# 1. Pre-flight check
est_tokens, est_cost = self._preflight(messages, tools)
warn_cost = self.budget.max_input_cost_usd * self.budget.warn_at_fraction
if est_cost >= self.budget.max_input_cost_usd:
raise ValueError(
f"Budget exceeded before inference: "
f"estimated ${est_cost:.4f} > cap ${self.budget.max_input_cost_usd}"
)
if est_cost >= warn_cost:
logger.warning(f"Cost warning: estimated ${est_cost:.4f}")
# 2. Build request
converse_kwargs: dict = {
"modelId": self.model_id,
"messages": messages,
"inferenceConfig": {"maxTokens": max_output_tokens},
}
if system:
converse_kwargs["system"] = [{"text": system}]
if tools:
converse_kwargs["toolConfig"] = {"tools": tools}
# 3. Inference
response = self.bedrock.converse(**converse_kwargs)
# 4. Post-call accounting
usage = response["usage"]
actual_input = usage.get("inputTokens", 0)
actual_output = usage.get("outputTokens", 0)
actual_cost = (
actual_input * self.input_price_per_m
+ actual_output * self.output_price_per_m
) / 1_000_000
content = response["output"]["message"]["content"][0].get("text", "")
latency_ms = (time.time() - t0) * 1000
logger.info(
f"Completed | input={actual_input} | output={actual_output} "
f"| cost=${actual_cost:.4f} | latency={latency_ms:.0f}ms"
)
return RequestResult(
content=content,
input_tokens=actual_input,
output_tokens=actual_output,
actual_cost_usd=actual_cost,
preflight_estimate_tokens=est_tokens,
preflight_estimate_cost_usd=est_cost,
latency_ms=latency_ms,
)
10. Key Constraints Summary
-
CountTokens is free, but rate-limited. At Tier 1 (100 RPM), pre-flight halves effective throughput on single-threaded pipelines.
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Tool schema tokens are undercounted by CountTokens. Calibrate a correction factor per model and tool catalog (typically 1.3–2.1x). Re-calibrate after any tool description changes.
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Anthropic SDK’s
AsyncAnthropicBedrockdoes not proxy CountTokens. Use boto3bedrock-runtimedirectly. -
Claude Opus 4.7+ tokenizer produces 30–35% more tokens than pre-Opus-4.7 models for the same text. Do not reuse old token counts or tiktoken estimates when migrating.
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LiteLLM price registry lags Bedrock price changes. Override via YAML
model_infofor production accuracy. -
Prompt caching breaks even at 2x reuse (1h TTL) or 1.25x reuse (5m TTL). System prompts and large tool schemas are the highest-value caching targets.
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Sample-based batch estimation at 1% achieves ±3% accuracy (95% CI, n≥30 sample). Use full CountTokens for jobs valued over $500 or with high input length variance.
Sources
- CountTokens API Reference — Amazon Bedrock
- count_tokens — Boto3 Bedrock Runtime Reference
- Count Tokens API GA Announcement — AWS What’s New, August 2025
- Token Counting — Anthropic Platform Docs
- Vision — Anthropic Platform Docs
- Token Counting Meets Amazon Bedrock — DEV Community / AWS
- How to Count Tokens Before Running Inference with Amazon Bedrock — Carlos Biagolini, Medium
- ttok4bedrock — danilop, GitHub
- LiteLLM Completion Token Usage and Cost
- LiteLLM Custom Pricing
- LiteLLM Spend Tracking
- LiteLLM Team Soft Budget Alerts
- Amazon Bedrock Pricing — AWS
- LLMLingua — Microsoft Research, GitHub
- LongLLMLingua: Accelerating LLMs in Long Context Scenarios via Prompt Compression — arXiv:2310.06839
- Characterizing Prompt Compression Methods for Long Context Inference — arXiv:2407.08892
- Support token counting for Anthropic Bedrock models — pydantic-ai Issue #5377
- Token Counting is not supported in Bedrock yet — anthropic-sdk-python Issue #1103
- LiteLLM cache token cost bug fix — Issue #19681
- AWS Bedrock Pricing 2026 — TokenMix