Llama 3.1 70B (Bedrock) vs Llama 3.3 70B Instruct (Fireworks): API Cost Comparison
Compare the API pricing, context windows, features, and real-world cost projections for Llama 3.1 70B (Bedrock) (AWS Bedrock) and Llama 3.3 70B Instruct (Fireworks) (Fireworks AI). 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
Fills in typical token counts for a workload type
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Showing costs for 2 models. Cheapest: Llama 3.3 70B Instruct (Fireworks) at $13.50/month.
| Alert | |||||||
|---|---|---|---|---|---|---|---|
BestLlama 3.3 70B Instruct (Fireworks) | Fireworks AI | $13.50 | $0.001350 | $9.00 | $4.50 | 58.2% | Alerts coming soon |
Llama 3.1 70B (Bedrock) | AWS Bedrock | $32.30 | $0.003230 | $19.50 | $12.80 | — | Alerts coming soon |
Monthly Cost Comparison
Price Comparison at a Glance
All prices are in USD per 1 million tokens ($/M tokens). Lower is cheaper.
| Pricing Type | Llama 3.1 70B (Bedrock) | Llama 3.3 70B Instruct (Fireworks) | Cheaper |
|---|---|---|---|
| Input (standard) | $1.95/M | $0.90/M | Llama 3.3 70B Instruct (Fireworks) |
| Output | $2.56/M | $0.90/M | Llama 3.3 70B Instruct (Fireworks) |
| Cached input | N/A | $0.45/M | — |
| Batch input | N/A | $0.45/M | — |
| Batch output | N/A | $0.45/M | — |
Prices last verified: 2026-03-10 – 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 | Llama 3.1 70B (Bedrock)Monthly | Llama 3.3 70B Instruct (Fireworks)Monthly | Llama 3.1 70B (Bedrock)Per request | Llama 3.3 70B Instruct (Fireworks)Per request |
|---|---|---|---|---|
| 1K requests/mo | $3.23 | $1.35 | $0.003230 | $0.001350 |
| 10K requests/mo | $32.30 | $13.50 | $0.003230 | $0.001350 |
| 100K requests/mo | $323.00 | $135.00 | $0.003230 | $0.001350 |
| 1M requests/mo | $3,230.00 | $1,350.00 | $0.003230 | $0.001350 |
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 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
When to Choose Llama 3.3 70B Instruct (Fireworks)
by Fireworks AI
- Code generation
- Document summarization
- General chatbot
Input / 1M tokens
$0.90/M
Output / 1M tokens
$0.90/M
Context window
131,072
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 | Llama 3.1 70B (Bedrock) | Llama 3.3 70B Instruct (Fireworks) |
|---|---|---|
| Context window | 128,000 tokens | 131,072 tokens |
| Max output tokens | 4,096 tokens | 8,192 tokens |
| Performance tier | Mid | Mid |
| Vision / image input | No | No |
| Function calling | Yes | Yes |
| JSON mode | Yes | Yes |
| Prompt caching | No | Yes |
| Batch API (50% discount) | Yes | Yes |
| Extended reasoning | No | No |
| Fine-tuning | No | Yes |
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.
Llama 3.3 70B Instruct (Fireworks) notes
Fireworks serverless pricing. Cached input 50% discount. Batch inference at 50% of serverless rates.
Frequently Asked Questions
Is Llama 3.1 70B (Bedrock) cheaper than Llama 3.3 70B Instruct (Fireworks)?
At standard usage (1,000 input tokens, 500 output tokens, 100,000 requests/month), Llama 3.3 70B Instruct (Fireworks) costs $135.00/month versus $323.00/month for Llama 3.1 70B (Bedrock) — a 58% 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, Llama 3.1 70B (Bedrock) or Llama 3.3 70B Instruct (Fireworks)?
Llama 3.3 70B Instruct (Fireworks) has a larger context window at 131,072 tokens, compared to 128,000 tokens for Llama 3.1 70B (Bedrock). A larger context window is important for processing long documents, multi-turn conversations, or large codebases without truncation.
Do Llama 3.1 70B (Bedrock) and Llama 3.3 70B Instruct (Fireworks) support the Batch API?
Yes — both Llama 3.1 70B (Bedrock) and Llama 3.3 70B Instruct (Fireworks) 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?
Llama 3.3 70B Instruct (Fireworks) supports prompt caching at $0.45/M for cached input, while Llama 3.1 70B (Bedrock) does not offer prompt caching. For RAG applications or chatbots with large, repeated context, Llama 3.3 70B Instruct (Fireworks)'s caching capability can substantially reduce effective costs.
What are the best use cases for Llama 3.1 70B (Bedrock) vs Llama 3.3 70B Instruct (Fireworks)?
Both models are well-suited for General chatbot. Llama 3.1 70B (Bedrock) is particularly strong for Summarization, Rag Retrieval. Llama 3.3 70B Instruct (Fireworks) is favored for Code generation, Document summarization. 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 Llama 3.1 70B (Bedrock) vs Llama 3.3 70B Instruct (Fireworks)?
At 1,000 input tokens and 500 output tokens per request — a typical conversational workload — Llama 3.1 70B (Bedrock) costs $0.003230 per request and Llama 3.3 70B Instruct (Fireworks) costs $0.001350 per request. At 100,000 requests/month, that translates to $323.00 and $135.00 respectively. Use the interactive calculator to adjust these parameters for your actual workload.
Llama 3.1 70B (Bedrock) vs Llama 3.3 70B Instruct (Fireworks): Summary
When comparing Llama 3.1 70B (Bedrock) and Llama 3.3 70B Instruct (Fireworks) for API cost, the right choice depends on your workload's token profile, required features, and tolerance for latency. Llama 3.3 70B Instruct (Fireworks) offers lower total cost at standard usage volumes (1,000 input + 500 output tokens per request at 100,000 requests/month) at $135.00/month, compared to $323.00/month for Llama 3.1 70B (Bedrock).
Both models are priced in USD per million tokens, the standard unit across all major AI API providers. Llama 3.1 70B (Bedrock) charges $1.95/M for input tokens and $2.56/M for output tokens. Llama 3.3 70B Instruct (Fireworks) charges $0.90/M input and $0.90/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 Llama 3.3 70B Instruct (Fireworks) 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: Llama 3.3 70B Instruct (Fireworks) supports up to 131,072 tokens in a single request, versus 128,000 tokens for Llama 3.1 70B (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
See cost recommendations for workloads where Llama 3.1 70B (Bedrock) or Llama 3.3 70B Instruct (Fireworks) is recommended.