API cost decision in 10 seconds

NewLaguna M.1 (free) vs Nova Premier 1.0

Pick Laguna M.1 (free) for lower cost; pick Nova Premier 1.0 only if the larger context window matters more.

Pricing data updated:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Laguna M.1 (free) for lower cost; pick Nova Premier 1.0 only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Laguna M.1 (free) is estimated at $0 vs $8.75 for Nova Premier 1.0, saving $8.75 (100% lower).

Cost-first pickLaguna M.1 (free)
Context-first pickNova Premier 1.0
Sample savings$8.75100%
10x traffic gap$87.5

Nova Premier 1.0 has more context, but Laguna M.1 (free) saves $8.75 on the standard workload. At 10x that traffic, the same price gap is about $87.5. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Laguna M.1 (free) stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickLaguna M.1 (free)Nova Premier 1.0
Input-heavy / RAG5M input + 500K outputLaguna M.1 (free)$0$18.75
Balanced workload1M input + 1M outputLaguna M.1 (free)$0$15
Output-heavy chatbot1M input + 5M outputLaguna M.1 (free)$0$65
Cheaper input Laguna M.1 (free) $0 vs $2.5 / 1M

Laguna M.1 (free) is free for input tokens while Nova Premier 1.0 costs $2.5 per 1M tokens.

Cheaper output Laguna M.1 (free) $0 vs $12.5 / 1M

Laguna M.1 (free) is free for output tokens while Nova Premier 1.0 costs $12.5 per 1M tokens.

Larger context Nova Premier 1.0 131.07K vs 1M

Nova Premier 1.0 has 868.93K more context (7.63x larger).

Sample workload Laguna M.1 (free) $0 vs $8.75

Laguna M.1 (free) is free for the standard workload while the other model is estimated at $8.75.

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Laguna M.1 (free) Calculating… Estimated API cost
Nova Premier 1.0 Calculating… Estimated API cost
Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

Laguna M.1 (free) has the lower input price; Laguna M.1 (free) has the lower output price; Nova Premier 1.0 offers the larger context window. For the 1M input plus 500K output sample, Laguna M.1 (free) is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0 for Laguna M.1 (free) and $8.75 for Nova Premier 1.0.

Best Fit

Choose Laguna M.1 (free) when you care most about lower input-token price, and lower output-token price.

Choose Nova Premier 1.0 when you care most about larger context window.

Decision Notes
  • On the standard 1M input plus 500K output workload, Laguna M.1 (free) is estimated at $0 vs $8.75 for Nova Premier 1.0, saving $8.75 (100% lower).
  • Laguna M.1 (free) is free for the standard workload while the other model is estimated at $8.75.
  • Laguna M.1 (free) is free for input tokens while Nova Premier 1.0 costs $2.5 per 1M tokens.
  • Laguna M.1 (free) is free for output tokens while Nova Premier 1.0 costs $12.5 per 1M tokens.
  • Nova Premier 1.0 has 868.93K more context (7.63x larger).
Head-to-Head Specs
FeatureNewLaguna M.1 (free)
(Poolside)
Nova Premier 1.0
(Amazon)
Input Price
prompt tokens per 1M
$0$2.5
Completion Price
per 1M tokens
$0$12.5
Sample Workload Cost
1M input + 500K output
$0$8.75
Context Window131.07K1M
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionLaguna M.1 (free)On the standard 1M input plus 500K output workload, Laguna M.1 (free) is estimated at $0 vs $8.75 for Nova Premier 1.0, saving $8.75 (100% lower).
High-volume input processingLaguna M.1 (free)Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsLaguna M.1 (free)Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workNova Premier 1.0A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps
  • Nova Micro 1.0 can replace Nova Premier 1.0 when lower sample workload cost matters most: $0.11.
  • Nova Lite 1.0 can replace Nova Premier 1.0 when lower sample workload cost matters most: $0.18.
  • Nova 2 Lite can replace Nova Premier 1.0 when lower sample workload cost matters most: $1.55.
  • Nova Pro 1.0 can replace Nova Premier 1.0 when lower sample workload cost matters most: $2.4.
Larger context near this budget
  • Llama 4 Scout offers 10M context with $0.23 sample workload cost.
  • Grok 4.20 Multi-Agent offers 2M context with $5 sample workload cost.
  • Grok 4.20 offers 2M context with $2.5 sample workload cost.
  • GPT-5.4 offers 1.05M context with $10 sample workload cost.
Popular competitors
  • No popular competitor is currently available.

Cheaper alternatives

Review low-cost models sorted by a standard 1M input plus 500K output workload.

Open cheapest models

Larger context alternatives

Find models with larger context windows for RAG, long documents, and codebase review.

Open largest context models

Provider catalogs

Compare models within provider hubs before choosing a final API vendor.

Open provider hubs

Poolside catalog

Review all tracked Poolside models before deciding whether this matchup is the right shortlist.

Open Poolside models

Amazon catalog

Check other Amazon models with comparable pricing, context, or release timing.

Open Amazon models
Laguna M.1 (free)

Laguna M.1 is the flagship coding agent model from [Poolside](https://poolside.ai), optimized for complex software engineering tasks. Designed for agentic coding workflows, it supports tool calling and reasoning, with a 128K...

Nova Premier 1.0

Amazon Nova Premier is the most capable of Amazon’s multimodal models for complex reasoning tasks and for use as the best teacher for distilling custom models.