GPT-5 nano vs Llama 3.1 8B (Bedrock): API Cost Comparison
Compare the API pricing, context windows, features, and real-world cost projections for GPT-5 nano (OpenAI) and Llama 3.1 8B (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
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Showing costs for 2 models. Cheapest: GPT-5 nano at $2.50/month.
| Alert | |||||||
|---|---|---|---|---|---|---|---|
BestGPT-5 nano | OpenAI | $2.50 | $0.000250 | $0.50 | $2.00 | 24.2% | Alerts coming soon |
Llama 3.1 8B (Bedrock) | AWS Bedrock | $3.30 | $0.000330 | $2.20 | $1.10 | — | Alerts coming soon |
Monthly Cost Comparison
Price Comparison at a Glance
All prices are in USD per 1 million tokens ($/M tokens). Lower is cheaper.
| Pricing Type | GPT-5 nano | Llama 3.1 8B (Bedrock) | Cheaper |
|---|---|---|---|
| Input (standard) | $0.05/M | $0.22/M | GPT-5 nano |
| Output | $0.40/M | $0.22/M | Llama 3.1 8B (Bedrock) |
| Cached input | $0.01/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-5 nanoMonthly | Llama 3.1 8B (Bedrock)Monthly | GPT-5 nanoPer request | Llama 3.1 8B (Bedrock)Per request |
|---|---|---|---|---|
| 1K requests/mo | $0.25 | $0.33 | $0.000250 | $0.000330 |
| 10K requests/mo | $2.50 | $3.30 | $0.000250 | $0.000330 |
| 100K requests/mo | $25.00 | $33.00 | $0.000250 | $0.000330 |
| 1M requests/mo | $250.00 | $330.00 | $0.000250 | $0.000330 |
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 tokensWhen to Choose GPT-5 nano
by OpenAI
- Text classification
- Data extraction
Input / 1M tokens
$0.05/M
Output / 1M tokens
$0.40/M
Context window
400,000
Tier
budget
When to Choose Llama 3.1 8B (Bedrock)
by AWS Bedrock
- Text classification
- Customer support
- Translation
Input / 1M tokens
$0.22/M
Output / 1M tokens
$0.22/M
Context window
128,000
Tier
budget
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-5 nano | Llama 3.1 8B (Bedrock) |
|---|---|---|
| Context window | 400,000 tokens | 128,000 tokens |
| Max output tokens | 128,000 tokens | 4,096 tokens |
| Performance tier | Budget | Budget |
| Vision / image input | Yes | No |
| Function calling | Yes | Yes |
| JSON mode | Yes | Yes |
| Prompt caching | Yes | No |
| Batch API (50% discount) | No | Yes |
| Extended reasoning | No | No |
| Fine-tuning | No | No |
| Rate limit (req/min) | 10,000 | Not published |
GPT-5 nano notes
Cheapest GPT-5 model. 1M context at budget pricing.
Llama 3.1 8B (Bedrock) notes
Meta Llama 3.1 8B via AWS Bedrock. Extremely cost-effective for bulk processing, routing, and classification tasks.
Frequently Asked Questions
Is GPT-5 nano cheaper than Llama 3.1 8B (Bedrock)?
At standard usage (1,000 input tokens, 500 output tokens, 100,000 requests/month), GPT-5 nano costs $25.00/month versus $33.00/month for Llama 3.1 8B (Bedrock) — a 24% 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-5 nano or Llama 3.1 8B (Bedrock)?
GPT-5 nano has a larger context window at 400,000 tokens, compared to 128,000 tokens for Llama 3.1 8B (Bedrock). A larger context window is important for processing long documents, multi-turn conversations, or large codebases without truncation.
Do GPT-5 nano and Llama 3.1 8B (Bedrock) support the Batch API?
Llama 3.1 8B (Bedrock) supports the Batch API (50% discount for async processing), while GPT-5 nano does not. If your workload tolerates up to 24-hour latency, routing to Llama 3.1 8B (Bedrock) with batch pricing could significantly cut costs versus GPT-5 nano's standard rate.
Which model offers better prompt caching?
GPT-5 nano supports prompt caching at $0.01/M for cached input, while Llama 3.1 8B (Bedrock) does not offer prompt caching. For RAG applications or chatbots with large, repeated context, GPT-5 nano's caching capability can substantially reduce effective costs.
What are the best use cases for GPT-5 nano vs Llama 3.1 8B (Bedrock)?
Both models are well-suited for Text classification. GPT-5 nano is particularly strong for Data extraction. Llama 3.1 8B (Bedrock) is favored for Customer support, Translation. 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-5 nano vs Llama 3.1 8B (Bedrock)?
At 1,000 input tokens and 500 output tokens per request — a typical conversational workload — GPT-5 nano costs $0.000250 per request and Llama 3.1 8B (Bedrock) costs $0.000330 per request. At 100,000 requests/month, that translates to $25.00 and $33.00 respectively. Use the interactive calculator to adjust these parameters for your actual workload.
GPT-5 nano vs Llama 3.1 8B (Bedrock): Summary
When comparing GPT-5 nano and Llama 3.1 8B (Bedrock) for API cost, the right choice depends on your workload's token profile, required features, and tolerance for latency. GPT-5 nano offers lower total cost at standard usage volumes (1,000 input + 500 output tokens per request at 100,000 requests/month) at $25.00/month, compared to $33.00/month for Llama 3.1 8B (Bedrock).
Both models are priced in USD per million tokens, the standard unit across all major AI API providers. GPT-5 nano charges $0.05/M for input tokens and $0.40/M for output tokens. Llama 3.1 8B (Bedrock) charges $0.22/M input and $0.22/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-5 nano but not Llama 3.1 8B (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: GPT-5 nano supports up to 400,000 tokens in a single request, versus 128,000 tokens for Llama 3.1 8B (Bedrock). 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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Relevant Use Cases
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