GPT-4o vs Llama 3.1 70B (Bedrock): API Cost Comparison

Compare the API pricing, context windows, features, and real-world cost projections for GPT-4o (OpenAI) and Llama 3.1 70B (Bedrock) (AWS Bedrock). Use the interactive calculator below to compute your exact monthly cost based on your token usage and request volume.

Prices verified Mar 11, 2026

Interactive Cost Calculator

Select Models (2/10)

OpenAIGPT-4oAWS BedrockLlama 3.1 70B (Bedrock)

Use Case Preset

Fills in typical token counts for a workload type

Avg Input Tokens / Request

Tokens in each prompt sent to the model

Avg Output Tokens / Request

Tokens generated in each response

Monthly Requests

Total API calls per month

Cheapest: Llama 3.1 70B (Bedrock) at $32.30/mo — save 56.9% vs GPT-4o

							Alert
BestLlama 3.1 70B (Bedrock)	AWS Bedrock	$32.30	$0.003230	$19.50	$12.80	56.9%	Alerts coming soon
GPT-4o	OpenAI	$75.00	$0.007500	$25.00	$50.00	—	Alerts coming soon

Monthly Cost Comparison

Price Comparison at a Glance

All prices are in USD per 1 million tokens ($/M tokens). Lower is cheaper.

Pricing Type	GPT-4o	Llama 3.1 70B (Bedrock)	Cheaper
Input (standard)	$2.50/M	$1.95/M	Llama 3.1 70B (Bedrock)
Output	$10.00/M	$2.56/M	Llama 3.1 70B (Bedrock)
Cached input	$1.25/M	N/A	—
Batch input	$1.25/M	N/A	—
Batch output	$5.00/M	N/A	—

Prices last verified: 2026-03-06 – 2026-03-11

Cost Breakdown by Usage Volume

Estimated monthly costs at different request volumes, assuming 1,000 input tokens and 500 output tokens per request. Adjust in the calculator above for your specific use case.

Volume	GPT-4oMonthly	Llama 3.1 70B (Bedrock)Monthly	GPT-4oPer request	Llama 3.1 70B (Bedrock)Per request
1K requests/mo	$7.50	$3.23	$0.007500	$0.003230
10K requests/mo	$75.00	$32.30	$0.007500	$0.003230
100K requests/mo	$750.00	$323.00	$0.007500	$0.003230
1M requests/mo	$7,500.00	$3,230.00	$0.007500	$0.003230

Green values indicate the lower-cost option at each volume tier. Cost per request is calculated at 1,000 input + 500 output tokens using standard (non-batch, non-cached) pricing.

Price History

Input price per 1M tokens

When to Choose GPT-4o

by OpenAI

General chatbot
Code generation
Content & copywriting

Input / 1M tokens

$2.50/M

Output / 1M tokens

$10.00/M

Context window

128,000

Tier

premium

When to Choose Llama 3.1 70B (Bedrock)

by AWS Bedrock

General chatbot
Summarization
Rag Retrieval

Input / 1M tokens

$1.95/M

Output / 1M tokens

$2.56/M

Context window

128,000

Tier

mid

Key Differences Beyond Price

Cost is only one factor in choosing an AI model. Context window size, rate limits, supported features, and latency all affect whether a model fits your use case.

Capability	GPT-4o	Llama 3.1 70B (Bedrock)
Context window	128,000 tokens	128,000 tokens
Max output tokens	16,384 tokens	4,096 tokens
Performance tier	Premium	Mid
Vision / image input	Yes	No
Function calling	Yes	Yes
JSON mode	Yes	Yes
Prompt caching	Yes	No
Batch API (50% discount)	Yes	Yes
Extended reasoning	No	No
Fine-tuning	Yes	No
Rate limit (req/min)	10,000	Not published

GPT-4o notes

Snapshot versions available (gpt-4o-2024-11-20). Cached input requires cache_control.

Llama 3.1 70B (Bedrock) notes

Meta Llama 3.1 70B via AWS Bedrock. Good balance of capability and cost for enterprise workloads requiring open-weight model governance.

Frequently Asked Questions

Is GPT-4o cheaper than Llama 3.1 70B (Bedrock)?

At standard usage (1,000 input tokens, 500 output tokens, 100,000 requests/month), Llama 3.1 70B (Bedrock) costs $323.00/month versus $750.00/month for GPT-4o — a 57% saving. Your actual savings will vary based on your token profile; output-heavy workloads amplify differences in output pricing.

Which model has a larger context window, GPT-4o or Llama 3.1 70B (Bedrock)?

Both GPT-4o and Llama 3.1 70B (Bedrock) have the same context window: 128,000 tokens.

Do GPT-4o and Llama 3.1 70B (Bedrock) support the Batch API?

Yes — both GPT-4o and Llama 3.1 70B (Bedrock) support batch API processing, which offers a 50% discount on input and output costs in exchange for up to 24-hour turnaround. Ideal for offline workloads like bulk document processing or nightly classification pipelines.

Which model offers better prompt caching?

GPT-4o supports prompt caching at $1.25/M for cached input, while Llama 3.1 70B (Bedrock) does not offer prompt caching. For RAG applications or chatbots with large, repeated context, GPT-4o's caching capability can substantially reduce effective costs.

What are the best use cases for GPT-4o vs Llama 3.1 70B (Bedrock)?

Both models are well-suited for General chatbot. GPT-4o is particularly strong for Code generation, Content & copywriting. Llama 3.1 70B (Bedrock) is favored for Summarization, Rag Retrieval. At the same quality level, the lower-cost model is usually preferable; use this page's calculator to compare total monthly spend at your volume.

What is the cost per request for GPT-4o vs Llama 3.1 70B (Bedrock)?

At 1,000 input tokens and 500 output tokens per request — a typical conversational workload — GPT-4o costs $0.007500 per request and Llama 3.1 70B (Bedrock) costs $0.003230 per request. At 100,000 requests/month, that translates to $750.00 and $323.00 respectively. Use the interactive calculator to adjust these parameters for your actual workload.

GPT-4o vs Llama 3.1 70B (Bedrock): Summary

When comparing GPT-4o and Llama 3.1 70B (Bedrock) for API cost, the right choice depends on your workload's token profile, required features, and tolerance for latency. Llama 3.1 70B (Bedrock) offers lower total cost at standard usage volumes (1,000 input + 500 output tokens per request at 100,000 requests/month) at $323.00/month, compared to $750.00/month for GPT-4o.

Both models are priced in USD per million tokens, the standard unit across all major AI API providers. GPT-4o charges $2.50/M for input tokens and $10.00/M for output tokens. Llama 3.1 70B (Bedrock) charges $1.95/M input and $2.56/M output. If your workload is output-heavy (more tokens generated than consumed as input), the model with the lower output price compounds cost savings significantly at scale.

Prompt caching is supported by GPT-4o but not Llama 3.1 70B (Bedrock). For workloads with large, repeated system prompts or document context — such as RAG pipelines or multi-turn conversations with a fixed knowledge base — prompt caching can reduce effective input costs by 60–90%, which may change the cost ranking between these two models at your specific usage pattern.

Context window capacity differs between the two: Both models support 128,000 tokens per request. A larger context window is essential for document summarization, large codebase analysis, and multi-document retrieval-augmented generation (RAG) applications.

Use the interactive calculator at the top of this page to enter your actual token usage and monthly request volume for a precise cost comparison tailored to your workload. Adjust for batch API discounts and prompt caching to find the most cost-effective option for your specific deployment.

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Related Provider Pages

View complete pricing tables and model lineups for the providers behind these models.

OpenAI API Pricing — All Models AWS Bedrock API Pricing — All Models

Relevant Use Cases

See cost recommendations for workloads where GPT-4o or Llama 3.1 70B (Bedrock) is recommended.

Code Generation Content & Copywriting General Chatbot