Llama 3.3 70B Costs

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By Vinayak Baranwal, Technical Writer

Technical Writer II

Comparing models for Llama 3.3 70B-class workloads requires considering output token rates, as most cost analyses focus solely on input token rates, which can be misleading.

This article explores the mechanics of output token pricing for Llama 3.3 70B, verified pricing, and a practical control framework for production agent workloads.

Output token pricing is the uncacheable floor of inference cost
The crossover ratio at which Llama 4 Maverick becomes pricier than flat-rate Llama 3.3 70B is 1.82
Gemma 4 is the cheapest model in the catalogue for this workload

How Output Token Costs Actually Accumulate

Why Output Tokens Cost More Than Input Tokens

Input processing evaluates all prompt tokens in a single parallelized forward pass, while output generation works serially, with each token requiring its own full forward pass, resulting in a premium on output tokens.

How the KV Cache Shifts the Cost Split Across a Session

The key-value cache stores computed attention states for already-processed tokens, reducing the need for recomputation and displacing fresh input as sessions lengthen.

Measured data shows the extent of this tilt

TraceLab: Session cost split across multi-agent tasks
CachedAttention: LLaMA-70B multi-turn KV cache hit rate
SwiftCache: Multi-turn conversation prefix hit rate

A per-turn curve from cold to warm is a useful mental model, but specific numbers for individual turn depths are modeled projections, not measured values.

Serverless billing-level prompt caching is distinct from engine-level KV or prefix caching in a self-hosted serving stack, and does not discount output on any model.

The Chain-of-Thought and Tool-Call Multiplier

Session averages obscure what happens inside a single reasoning step, with output tokens dominating cost in certain steps, even if they barely register at the session level.

The Reference Workload: A Five-Step Agent Task

The numbers below are a constructed illustrative estimate, built to total 7,870 input tokens and 3,000 output tokens per task, consistent with the DigitalOcean model cost table.

Token Estimates per Step

Why the Aggregate Ratio Misleads

The 0.38:1 aggregate ratio sits well below the Llama 3.3 70B/Llama 4 Maverick crossover at 1.82, with Maverick winning by $24.88 per 10,000 tasks at this ratio.

Llama 3.3 70B Pricing Within DigitalOcean's Model Catalogue

Model Comparison Table

All rates are per 1 million tokens, DigitalOcean Serverless Inference, last verified 1 Jul 2026, with cost columns using the reference workload of 7,870 input and 3,000 output tokens per task.

DigitalOcean hosts Llama 3.3 70B at a rare flat rate, while most models charge two to five times more for output, with rates subject to change.

Cost per task is computed as input_tokens times input_rate plus output_tokens times output_rate, using the reference workload.

Cost per Agent Task by Model

This function reproduces the cost-per-task column for any token counts and rates, allowing projection of workloads against the model table once the actual output:input ratio is measured.

def cost_per_task ( input_tokens : int , output_tokens : int , input_rate : float , # $/1M tokens output_rate : float , # $/1M tokens ) - > float : return ( input_tokens * input_rate + output_tokens * output_rate ) / 1_000_000 WORKLOAD_INPUT = 7_870 WORKLOAD_OUTPUT = 3_000 models = { "Llama 3.3 70B" : ( 0.65 , 0.65 ) , "Llama 4 Maverick" : ( 0.25 , 0.87 ) , "Qwen3-32B" : ( 0.25 , 0.55 ) , "Gemma 4" : ( 0.18 , 0.50 ) , "Kimi K2.5" : ( 0.375 , 2.025 ) , "GLM-5.2" : ( 1.05 , 4.40 ) , } from decimal import Decimal , ROUND_HALF_UP def usd ( value : float , places : str ) - > Decimal : # round half up so displayed cents match the hand-computed rate table return Decimal ( str ( value ) ) . quantize ( Decimal ( places ) , rounding = ROUND_HALF_UP ) for name , ( in_rate , out_rate ) in models . items ( ) : task_cost = cost_per_task ( WORKLOAD_INPUT , WORKLOAD_OUTPUT , in_rate , out_rate ) print ( f" { name } : $ { usd ( task_cost , &apos;0.000001&apos; ) } /task " f"($ { usd ( task_cost * 10_000 , &apos;0.01&apos; ) } /10k tasks)" )

Output Llama 3.3 70B: $0.007066/task ($70.66/10k tasks) Llama 4 Maverick: $0.004578/task ($45.78/10k tasks) Qwen3-32B: $0.003618/task ($36.18/10k tasks) Gemma 4: $0.002917/task ($29.17/10k tasks) Kimi K2.5: $0.009026/task ($90.26/10k tasks) GLM-5.2: $0.021464/task ($214.64/10k tasks)

Gemma 4 is cheapest for this workload at $29.17 per 10,000 tasks, followed by Qwen3-32B and Llama 4 Maverick, while flat-rate Llama 3.3 70B costs more due to the input-heavy nature of the workload.

The Latency Dimension

DigitalOcean Serverless Inference delivers sub-400ms time-to-first-byte on Llama 3.3 70B at $0.65 per million tokens, as measured in DigitalOcean's own model-selection tutorial.

How Caching Changes the Comparison

Prompt caching discounts cached reads of input tokens, but output tokens are always billed at the full output rate, regardless of cache state.

DigitalOcean does not list a cache-read discount for Llama 3.3 70B specifically, so cached input bills at the standard rate on that model.

Three Assumptions That Break at Scale

Assumption 1: Benchmark Token Ratios Apply to Your Workload

Artificial Analysis computes a single headline blended price per model, using a default 7:2:1 weighting of cached-read, input, and output tokens, which may not apply to every workload.

Measure your per-step token distribution before using any blended price as a production cost baseline.

Assumption 2: The Lowest Input Rate Is the Cheapest Model

This crossover is worked entirely within DigitalOcean's own catalogue, comparing Llama 4 Maverick against flat-rate Llama 3.3 70B, with costs equalizing at an output:input ratio of 1.82.

Costs equalize when the output:input ratio reaches 1.82, below which Maverick is cheaper, and above which flat-rate Llama 3.3 70B is cheaper.

0.65 × I + 0.65 × O = 0.25 × I + 0.87 × O 0.40 × I = 0.22 × O O / I = 0.40 / 0.22 = 1.82

Three worked checks confirm this, using the reference workload steps from the table above.

Maverick's low input rate wins on the aggregate, input-heavy workload, but its 3.48x output premium makes plain Llama 3.3 70B cheaper on output-heavy steps.

Assumption 3: Chain-of-Thought Tokens Are a Fixed Overhead

CoT token counts vary with prompt framing, instruction verbosity, temperature, and problem complexity, with the Router measurements showing a 42:1 range.

Cost Control Mechanisms for Output-Heavy Workloads

Set Output Token Budgets

Set max_tokens to your p95 output token count plus a 10 to 15% buffer, and handle finish_reason: length explicitly to avoid truncated responses.

import os import openai client = openai . OpenAI ( base_url = "https://inference.do-ai.run/v1" , api_key = os . environ [ "MODEL_ACCESS_KEY" ] , ) response = client . chat . completions . create ( model = "llama3.3-70b-instruct" , messages = [ { "role" : "user" , "content" : "List the steps to configure prefix caching." } ] , max_tokens = 512 , ) print ( response . choices [ 0 ] . finish_reason ) print ( response . usage . completion_tokens ) if response . choices [ 0 ] . finish_reason == "length" : raise ValueError ( f"Response truncated at { response . usage . completion_tokens } tokens; " "increase max_tokens or split the prompt." )

Output stop 341

Route by Expected Output Profile

Route steps below 1.82 to a low-input-rate model, and steps above 1.82 to flat-rate Llama 3.3 70B, using the inference router article for dispatch thresholds.

Use Batch and Caching Where They Apply

Caching discounts only cached input reads, while batch inference discounts both input and output, with batch availability subject to change.

Monitor the Output Token Distribution

Track p50, p95, and p99 output token counts, and alert on the p99-to-p50 ratio to catch drift early and avoid cost overruns.

Decision Framework

Use these criteria to select a model within DigitalOcean's catalogue by workload shape, with the core rule being that the flat-rate model avoids the output premium on output-heavy steps.

No model wins both shapes, so model selection and cost control operate at the step level, not the session level.

Frequently Asked Questions

Why Do Output Tokens Cost More Than Input Tokens?

Input tokens are processed in a single parallelized forward pass, while output tokens are generated one at a time through autoregressive decoding, resulting in a premium on output tokens.

What Is the Cheapest Provider for Running Llama 3.3 70B in Production?

DigitalOcean Serverless Inference runs Llama 3.3 70B at a flat $0.65 per million tokens, with no output premium, making it the model to reach for on output-heavy or CoT-heavy production workloads.

Which Provider Is Cheapest for Llama 3 70B with Sub-Second Latency?

DigitalOcean Serverless Inference delivers sub-400ms time-to-first-byte on Llama 3.3 70B at $0.65 per million tokens, with no latency-versus-cost tradeoff.

How Do I Reduce LLM API Costs at Scale on Output-Heavy Workloads?

Four mechanisms in order of leverage: set per-step max_tokens, route high-output steps to flat-rate Llama 3.3 70B, use batch inference where available, and monitor output token counts by step type.

Do Token Ratios from Standard Benchmarks Predict Production Agent Costs?

Not reliably, as Artificial Analysis's default blending weight assumes roughly 70% cache hits, which may not apply to production agent workloads with significant CoT or tool-call output.

Does Prompt Caching Reduce Llama 3.3 70B Costs on DigitalOcean?

Prompt caching discounts cached input tokens only, with no discount on output tokens, and DigitalOcean does not list a cache-read discount for Llama 3.3 70B specifically.

What Output-to-Input Token Ratio Should I Expect for Agent Workloads?

At the session level, output accounts for 11.2% of session cost, but per-step ratios range from under 0.1:1 to above 6:1, depending on prompt structure and step composition.

How Do I Set a Safe Output Token Budget?

Sample a batch of requests, compute the p95 output token count per step type, add a 10 to 15% buffer, and set max_tokens to that value, handling finish_reason: length explicitly.

Output token pricing is the uncacheable floor of Llama 3.3 70B inference cost, driving the model-ranking flip that input-only comparisons miss.

Model selection and cost control operate at the step level, not the session level, with the inference router article covering dispatch and serverless inference metrics covering monitoring.

Before committing to a model at production volume, profile your own output:input ratio per step, then project it to the catalogue table using the cost function above, with rates subject to change.

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About the author, a full stack developer and system administrator with expertise in Linux, Cloud, and DevOps.

The author is a technical writer at DigitalOcean, with a passion for Docker, PostgreSQL, and Open Source, and experience with NLP and AI-TensorFlow.

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Table of contents
Introduction
TL;DR
How Output Token Costs Actually Accumulate
The Reference Workload: A Five-Step Agent Task
Llama 3.3 70B Pricing Within DigitalOcean's Model Catalogue
Three Assumptions That Break at Scale
Cost Control Mechanisms for Output-Heavy Workloads
Decision Framework
Frequently Asked Questions
Conclusion

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Knowledge Bases
GPU Droplets
Bare Metal GPUs
Inference Engine
Data & Learning
Evaluations
Model Library
Droplets
Kubernetes
Functions
App Platform
Load Balancers
Managed Databases
Spaces
Block Storage
Network File Storage
API
Uptime
Cloud Security Posture Management (CSPM)
Identity and Access Management (IAM)
Cloudways
View all Products

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VPS Hosting
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Table of Contents