Embed v3 vs Llama 4 Scout (Fireworks): API Cost Comparison
Compare the API pricing, context windows, features, and real-world cost projections for Embed v3 (Cohere) and Llama 4 Scout (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
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Showing costs for 2 models. Cheapest: Embed v3 at $1.50/month.
| Alert | |||||||
|---|---|---|---|---|---|---|---|
BestEmbed v3 | Cohere | $1.50 | $0.000150 | $1.00 | $0.50 | 50.0% | Alerts coming soon |
Llama 4 Scout (Fireworks) | Fireworks AI | $3.00 | $0.000300 | $2.00 | $1.00 | — | Alerts coming soon |
Monthly Cost Comparison
Price Comparison at a Glance
All prices are in USD per 1 million tokens ($/M tokens). Lower is cheaper.
| Pricing Type | Embed v3 | Llama 4 Scout (Fireworks) | Cheaper |
|---|---|---|---|
| Input (standard) | $0.10/M | $0.20/M | Embed v3 |
| Output | $0.10/M | $0.20/M | Embed v3 |
| Cached input | N/A | $0.10/M | — |
| Batch input | N/A | $0.10/M | — |
| Batch output | N/A | $0.10/M | — |
Prices last verified: 2026-03-06 – 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.
| Volume | Embed v3Monthly | Llama 4 Scout (Fireworks)Monthly | Embed v3Per request | Llama 4 Scout (Fireworks)Per request |
|---|---|---|---|---|
| 1K requests/mo | $0.15 | $0.30 | $0.000150 | $0.000300 |
| 10K requests/mo | $1.50 | $3.00 | $0.000150 | $0.000300 |
| 100K requests/mo | $15.00 | $30.00 | $0.000150 | $0.000300 |
| 1M requests/mo | $150.00 | $300.00 | $0.000150 | $0.000300 |
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 Embed v3
by Cohere
- RAG / Semantic search
Input / 1M tokens
$0.10/M
Output / 1M tokens
$0.10/M
Context window
512
Tier
budget
When to Choose Llama 4 Scout (Fireworks)
by Fireworks AI
- Customer support
- RAG / Semantic search
- General chatbot
Input / 1M tokens
$0.20/M
Output / 1M tokens
$0.20/M
Context window
10,000,000
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 | Embed v3 | Llama 4 Scout (Fireworks) |
|---|---|---|
| Context window | 512 tokens | 10,000,000 tokens |
| Max output tokens | 1,024 tokens | 16,384 tokens |
| Performance tier | Budget | Budget |
| Vision / image input | No | Yes |
| Function calling | No | Yes |
| JSON mode | No | Yes |
| Prompt caching | No | Yes |
| Batch API (50% discount) | No | Yes |
| Extended reasoning | No | No |
| Fine-tuning | No | No |
Embed v3 notes
Embedding model only. Output tokens represent embedding dimensions, not text. Multilingual variant also available.
Llama 4 Scout (Fireworks) notes
Fireworks serverless pricing. Cached input 50% discount. Batch inference at 50% of serverless rates. 10M context window.
Frequently Asked Questions
Is Embed v3 cheaper than Llama 4 Scout (Fireworks)?
At standard usage (1,000 input tokens, 500 output tokens, 100,000 requests/month), Embed v3 costs $15.00/month versus $30.00/month for Llama 4 Scout (Fireworks) — a 50% 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, Embed v3 or Llama 4 Scout (Fireworks)?
Llama 4 Scout (Fireworks) has a larger context window at 10,000,000 tokens, compared to 512 tokens for Embed v3. A larger context window is important for processing long documents, multi-turn conversations, or large codebases without truncation.
Do Embed v3 and Llama 4 Scout (Fireworks) support the Batch API?
Llama 4 Scout (Fireworks) supports the Batch API (50% discount for async processing), while Embed v3 does not. If your workload tolerates up to 24-hour latency, routing to Llama 4 Scout (Fireworks) with batch pricing could significantly cut costs versus Embed v3's standard rate.
Which model offers better prompt caching?
Llama 4 Scout (Fireworks) supports prompt caching at $0.10/M for cached input, while Embed v3 does not offer prompt caching. For RAG applications or chatbots with large, repeated context, Llama 4 Scout (Fireworks)'s caching capability can substantially reduce effective costs.
What are the best use cases for Embed v3 vs Llama 4 Scout (Fireworks)?
Both models are well-suited for RAG / Semantic search. Embed v3 is particularly strong for overlapping tasks. Llama 4 Scout (Fireworks) is favored for Customer support, 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 Embed v3 vs Llama 4 Scout (Fireworks)?
At 1,000 input tokens and 500 output tokens per request — a typical conversational workload — Embed v3 costs $0.000150 per request and Llama 4 Scout (Fireworks) costs $0.000300 per request. At 100,000 requests/month, that translates to $15.00 and $30.00 respectively. Use the interactive calculator to adjust these parameters for your actual workload.
Embed v3 vs Llama 4 Scout (Fireworks): Summary
When comparing Embed v3 and Llama 4 Scout (Fireworks) for API cost, the right choice depends on your workload's token profile, required features, and tolerance for latency. Embed v3 offers lower total cost at standard usage volumes (1,000 input + 500 output tokens per request at 100,000 requests/month) at $15.00/month, compared to $30.00/month for Llama 4 Scout (Fireworks).
Both models are priced in USD per million tokens, the standard unit across all major AI API providers. Embed v3 charges $0.10/M for input tokens and $0.10/M for output tokens. Llama 4 Scout (Fireworks) charges $0.20/M input and $0.20/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 4 Scout (Fireworks) but not Embed v3. 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 4 Scout (Fireworks) supports up to 10,000,000 tokens in a single request, versus 512 tokens for Embed v3. 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 Embed v3 or Llama 4 Scout (Fireworks) is recommended.