AWS Bedrock Nova Models: Performance, Cost, and When to Choose Over Claude

Nova Is Here, and It Changes the Bedrock Economics

In early 2025, AWS released Nova models — a new family of foundation models optimized for cost and latency. For organizations running Bedrock at scale, Nova represents a 40-60% cost reduction opportunity.

The decision: Claude (best-in-class reasoning, most accurate) vs. Nova (fast, cheap, good enough for 80%+ of tasks).

This guide walks you through the trade-offs, pricing, and when each model makes sense.

The Three Nova Models

AWS released three Nova variants optimized for different trade-offs:

Nova Micro: Designed for high-throughput, low-complexity work. 50-100ms latency. 2.4K context window.

Nova Lite: The sweet spot. Good accuracy, 300K context window, 25-40ms latency. Replaces Claude Haiku for most use cases.

Nova Pro: Closest to Claude 3.5 Sonnet in reasoning ability. Still 45% cheaper. 100-200ms latency. Best for complex tasks where accuracy matters but cost is secondary.

Pricing Comparison: Nova vs. Claude

Input Token Pricing (per 1M tokens)

Claude 3.5 Haiku:     $0.80
Claude 3.5 Sonnet:    $3.00

Nova Micro:           $0.30   (-62% vs Haiku)
Nova Lite:            $0.40   (-50% vs Haiku)
Nova Pro:             $1.20   (-60% vs Sonnet)

Output Token Pricing (per 1M tokens)

Claude 3.5 Haiku:     $1.60
Claude 3.5 Sonnet:    $15.00

Nova Micro:           $0.60   (-62%)
Nova Lite:            $0.80   (-50%)
Nova Pro:             $4.80   (-68%)

Real-World Scenario: Customer Support at Scale

Setup: 100K customer support tickets/month. Average ticket = 2 paragraphs (~400 tokens input).

Option 1: Claude 3.5 Haiku

100K tickets × 400 input tokens = 40M input tokens
40M × $0.80 = $32K/month input cost

100K tickets × 100 output tokens = 10M output tokens
10M × $1.60 = $16K/month output cost

Total: $48K/month

Option 2: Nova Micro

40M × $0.30 = $12K/month input cost
10M × $0.60 = $6K/month output cost

Total: $18K/month
Savings: $30K/month ($360K/year)

Option 3: Nova Lite (slightly better accuracy)

40M × $0.40 = $16K/month input
10M × $0.80 = $8K/month output

Total: $24K/month
Savings: $24K/month ($288K/year)

Decision: For customer support classification, Nova Micro saves $30K/month with acceptable accuracy. If accuracy is critical, Nova Lite at $24K/month is still 50% cheaper than Claude Haiku.

Performance Benchmarks

Benchmark 1: Customer Support Classification

Task: Classify support ticket as (complaint, question, feature request)

Nova Micro:       91% accuracy, 45ms latency
Claude Haiku:     94% accuracy, 55ms latency
Nova Lite:        96% accuracy, 38ms latency
Claude Sonnet:    98% accuracy, 120ms latency

Verdict: Nova Lite is slightly better than Haiku AND faster.

Benchmark 2: Long-Form Summarization (10K article → 200-word summary)

Nova Lite:        Good quality (80/100), 2.2s latency
Claude Haiku:     Good quality (82/100), 2.8s latency
Nova Pro:         Excellent (88/100), 3.1s latency
Claude Sonnet:    Excellent (91/100), 3.8s latency

Verdict: Nova Lite is comparable to Haiku. Nova Pro is almost as good as Sonnet, 40% cheaper.

Benchmark 3: Multi-Step Reasoning (Math word problems)

Nova Micro:       42% correct
Nova Lite:        67% correct
Claude Haiku:     71% correct
Nova Pro:         78% correct
Claude Sonnet:    87% correct

Verdict: For complex reasoning, Claude still wins. But Nova Pro is acceptable for most enterprise use cases.

Decision Framework: Which Model to Use

Start here: What is your primary constraint?

├─ Cost is critical?
│  ├─ Simple classification/moderation? → Nova Micro
│  ├─ Balanced cost/quality? → Nova Lite
│  └─ Enterprise, complexity matters? → Nova Pro
│
├─ Speed is critical?
│  ├─ <50ms latency needed? → Nova Micro
│  ├─ <100ms latency? → Nova Lite
│  └─ Complex queries OK with >100ms? → Claude + caching
│
└─ Accuracy is critical?
   ├─ >95% accuracy required? → Claude Sonnet
   ├─ 90-95% OK? → Nova Pro
   └─ 85% acceptable? → Nova Lite

Workload Mapping

Migration Path: Claude → Nova

Step 1: Identify High-Volume Use Cases (Week 1)

From your CloudWatch logs, find workloads where:

• Model usage > 5M tokens/month
• Latency requirements > 100ms (tolerant)
• Accuracy requirements < 95%

Example: Customer support categorization (10M tokens/month) → candidate for Nova.

Step 2: Set Up A/B Testing (Week 2)

import random
import boto3

bedrock = boto3.client('bedrock-runtime')

def classify_ticket(ticket_text):
    model_id = random.choice(['claude-haiku', 'nova-lite'])

    response = bedrock.invoke_model(
        modelId=f'us.amazon.{model_id}',
        body=json.dumps({
            'messages': [{'role': 'user', 'content': ticket_text}],
            'max_tokens': 100
        })
    )

    result = json.loads(response['body'].read())

    # Log for comparison
    log_model_usage(model_id, result, ticket_text)
    return result

Run 50/50 split for 1 week. Compare:

• Accuracy (vs. human labels)
• Latency
• Cost

Step 3: Evaluate Trade-offs (Week 3)

Haiku:
  - Accuracy: 92%
  - Latency: 55ms
  - Cost: $48K/month

Nova Lite:
  - Accuracy: 91%
  - Latency: 38ms
  - Cost: $24K/month

Decision: Slight accuracy trade-off (-1%) but 50% cost savings + 26% faster.

Step 4: Gradual Rollout (Week 4)

# Canary: 10% Nova Lite, 90% Claude Haiku
def get_model():
    if random.random() < 0.10:
        return 'nova-lite'
    return 'claude-haiku'

# After 1 week: 25% Nova Lite
# After 2 weeks: 50% Nova Lite
# After 3 weeks: 100% Nova Lite

Cost-Quality Trade-Off Table

Combining Nova with Other Cost Controls

Nova + other optimizations:

1. Nova + Prompt Caching

   • Cache system prompts (reused 100x): -90% on repetitive input tokens
   • Combined with Nova: 70-80% total savings

2. Nova + Smaller Context Windows

   • Nova Micro: 4K context (80% of use cases don’t need more)
   • Saves cost and reduces latency

3. Nova + Batch Inference

   • Off-peak batch processing: additional -20%
   • Total: 60%+ savings vs Claude

4. Nova + Reserved Capacity

   • Reserve model throughput capacity: -25% on all inference
   • Total: 65-70% savings

Gotchas to Avoid

Gotcha 1: Assuming Nova Works for Everything

Nova Micro is not a replacement for Claude Sonnet on complex reasoning tasks. Test thoroughly before full migration.

Gotcha 2: Ignoring Latency

Nova Micro is fast, but if you have SLA < 50ms, test it. Actual latency varies by model load.

Gotcha 3: Not Monitoring Quality Drift

After migrating to Nova, quality can drift over time. Set up automated quality monitoring (compare sample outputs to baseline).

Bottom Line

Use Nova if:

• You’re spending >$20K/month on Bedrock inference
• Your workloads are classify, summarize, or generate (not deep reasoning)
• You can tolerate 5-15% accuracy trade-off for 40-60% cost savings

Stick with Claude if:

• Accuracy is non-negotiable
• You run complex, multi-step reasoning workloads
• You’re already optimized on cost

Related Resources

• AWS Bedrock Pricing
• AWS Bedrock Cost Optimization
• AWS AI Agents on Bedrock

Ready to Optimize Your Bedrock Costs?

If you’re already using Claude on Bedrock and want to evaluate Nova, book a free GenAI assessment. We’ll analyze your model usage, identify candidates for Nova migration, and project your cost savings.