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Gemini 3.1 Pro vs Llama 3.3 70B Instruct (Fireworks): API Cost Comparison

Compare the API pricing, context windows, features, and real-world cost projections for Gemini 3.1 Pro (Google) 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 10, 2026

Interactive Cost Calculator

GoogleGemini 3.1 ProFireworks AILlama 3.3 70B Instruct (Fireworks)

Fills in typical token counts for a workload type

Tokens in each prompt sent to the model

Tokens generated in each response

Total API calls per month

Showing costs for 2 models. Cheapest: Llama 3.3 70B Instruct (Fireworks) at $13.50/month.

Cheapest: Llama 3.3 70B Instruct (Fireworks) at $13.50/mo — save 83.1% vs Gemini 3.1 Pro
Alert
BestLlama 3.3 70B Instruct (Fireworks)
Fireworks AI$13.50$0.001350$9.00$4.5083.1%Alerts coming soon
Gemini 3.1 Pro
Google$80.00$0.008000$20.00$60.00Alerts 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 TypeGemini 3.1 ProLlama 3.3 70B Instruct (Fireworks)Cheaper
Input (standard)$2.00/M$0.90/MLlama 3.3 70B Instruct (Fireworks)
Output$12.00/M$0.90/MLlama 3.3 70B Instruct (Fireworks)
Cached input$0.20/M$0.45/MGemini 3.1 Pro
Batch input$1.00/M$0.45/MLlama 3.3 70B Instruct (Fireworks)
Batch output$6.00/M$0.45/MLlama 3.3 70B Instruct (Fireworks)

Prices last verified: 2026-03-08 – 2026-03-10

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.

VolumeGemini 3.1 ProMonthlyLlama 3.3 70B Instruct (Fireworks)MonthlyGemini 3.1 ProPer requestLlama 3.3 70B Instruct (Fireworks)Per request
1K requests/mo$8.00$1.35$0.008000$0.001350
10K requests/mo$80.00$13.50$0.008000$0.001350
100K requests/mo$800.00$135.00$0.008000$0.001350
1M requests/mo$8,000.00$1,350.00$0.008000$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 tokens

When to Choose Gemini 3.1 Pro

by Google

  • Document summarization
  • RAG / Semantic search
  • Code generation

Input / 1M tokens

$2.00/M

Output / 1M tokens

$12.00/M

Context window

2,097,152

Tier

premium

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.

CapabilityGemini 3.1 ProLlama 3.3 70B Instruct (Fireworks)
Context window2,097,152 tokens131,072 tokens
Max output tokens65,536 tokens8,192 tokens
Performance tierPremiumMid
Vision / image inputYesNo
Function callingYesYes
JSON modeYesYes
Prompt cachingYesYes
Batch API (50% discount)YesYes
Extended reasoningYesNo
Fine-tuningNoYes
Rate limit (req/min)1,000Not published

Gemini 3.1 Pro notes

Preview model. Tiered pricing: prompts over 200K tokens billed at $4.00 input / $18.00 output.

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 Gemini 3.1 Pro 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 $800.00/month for Gemini 3.1 Pro — a 83% 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, Gemini 3.1 Pro or Llama 3.3 70B Instruct (Fireworks)?

Gemini 3.1 Pro has a larger context window at 2,097,152 tokens, compared to 131,072 tokens for Llama 3.3 70B Instruct (Fireworks). A larger context window is important for processing long documents, multi-turn conversations, or large codebases without truncation.

Do Gemini 3.1 Pro and Llama 3.3 70B Instruct (Fireworks) support the Batch API?

Yes — both Gemini 3.1 Pro 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?

Both Gemini 3.1 Pro and Llama 3.3 70B Instruct (Fireworks) support prompt caching. Gemini 3.1 Pro caches repeated input at $0.20/M (vs $2.00/M standard), while Llama 3.3 70B Instruct (Fireworks) caches at $0.45/M (vs $0.90/M standard). For workloads with large, repeated system prompts or document context, caching can reduce effective input costs by 60–90%.

What are the best use cases for Gemini 3.1 Pro vs Llama 3.3 70B Instruct (Fireworks)?

Both models are well-suited for Document summarization, Code generation. Gemini 3.1 Pro is particularly strong for RAG / Semantic search. Llama 3.3 70B Instruct (Fireworks) is favored for General chatbot. 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 Gemini 3.1 Pro vs Llama 3.3 70B Instruct (Fireworks)?

At 1,000 input tokens and 500 output tokens per request — a typical conversational workload — Gemini 3.1 Pro costs $0.008000 per request and Llama 3.3 70B Instruct (Fireworks) costs $0.001350 per request. At 100,000 requests/month, that translates to $800.00 and $135.00 respectively. Use the interactive calculator to adjust these parameters for your actual workload.

Gemini 3.1 Pro vs Llama 3.3 70B Instruct (Fireworks): Summary

When comparing Gemini 3.1 Pro 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 $800.00/month for Gemini 3.1 Pro.

Both models are priced in USD per million tokens, the standard unit across all major AI API providers. Gemini 3.1 Pro charges $2.00/M for input tokens and $12.00/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 both models. 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: Gemini 3.1 Pro supports up to 2,097,152 tokens in a single request, versus 131,072 tokens for Llama 3.3 70B Instruct (Fireworks). 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.

Explore other model comparisons from the same providers or performance tiers.

Related Provider Pages

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

Relevant Use Cases

See cost recommendations for workloads where Gemini 3.1 Pro or Llama 3.3 70B Instruct (Fireworks) is recommended.