--- title: AWS Bedrock Nova Models: Performance, Cost, and When to Choose Over Claude description: AWS Nova models vs Claude: pricing comparison, performance benchmarks, and decision framework for choosing the right Bedrock model for your enterprise AI. url: https://www.joinhorizon.co/blog/aws-bedrock-nova-models-guide/ datePublished: 2026-04-08T00:00:00.000Z dateModified: 2026-10-28T00:00:00.000Z author: palaniappan-p category: Generative AI tags: amazon-bedrock, nova, claude, foundation-models, llm-comparison --- # AWS Bedrock Nova Models: Performance, Cost, and When to Choose Over Claude > AWS Nova models vs Claude: pricing comparison, performance benchmarks, and decision framework for choosing the right Bedrock model for your enterprise AI. > **AWS lifecycle notice (June 30, 2026):** Amazon Bedrock Agents is now **Bedrock Agents Classic**, in maintenance for new customers after **July 30, 2026**. Net-new agent builds should use [Bedrock AgentCore](https://www.joinhorizon.co/blog/amazon-bedrock-agentcore-production/). Full matrix: [lifecycle roundup](https://www.joinhorizon.co/blog/aws-service-lifecycle-updates-june-2026/). ## Successor: Amazon Nova 2 Lite (December 2, 2025) The current everyday Nova is **Nova 2 Lite**, with multimodal text, image, and video input and a **1M-token** context window (`amazon.nova-2-lite-v1:0`, plus `us.` / `eu.` / `jp.` / `global.` CRIS prefixes). **Nova 2 Sonic** covers speech. **Nova 2 Pro** is in preview, so do not treat it as general GA. **Previous approach:** this post modeled the **Nova 1** family (Micro / Lite / Pro / Premier) against Claude on published us-east-1 list rates. Those rates and the bakeoff below remain a Nova 1-era snapshot. **What AWS changed:** on **December 2, 2025**, [Nova 2 foundation models reached Bedrock](https://aws.amazon.com/about-aws/whats-new/2025/12/nova-2-foundation-models-amazon-bedrock/). Official [Nova 2 Lite model card](https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-amazon-nova-2-lite.html): 1M context, multimodal input. We do **not** quote unpublished Nova 2 list rates here. Verify at [Bedrock pricing](https://aws.amazon.com/bedrock/pricing/). Keep Nova Micro as the cheapest published text-only lane until you have a live 2 Lite quote. Route new volume multimodal traffic to Nova 2 Lite after a frozen-prompt bakeoff. Do not rewrite a Nova 1 pin overnight. ## Nova and Bedrock inference costs 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 is 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 covers the trade-offs, pricing, and when each model makes sense. --- ## The three Nova models AWS released three Nova variants optimized for different trade-offs: | Model | Context | Speed | Accuracy | Best For | Cost vs Claude Haiku | | -------------- | ------- | ---------- | -------- | ------------------------ | -------------------- | | **Nova Micro** | 4K | Ultra-fast | 75% | High-volume simple tasks | **-60%** | | **Nova Lite** | 300K | Fast | 85% | Balanced workloads | **-50%** | | **Nova Pro** | 300K | Moderate | 92% | Enterprise applications | **-45%** | **Nova Micro:** Designed for high-throughput, low-complexity work. 50-100ms latency. 2.4K context window. **Nova Lite:** Good accuracy, 300K context window, 25-40ms latency. It replaces Claude Haiku for most use cases. **Nova Pro:** Closest to Claude Sonnet 4.6 / Sonnet 5 in reasoning ability on many eval sets. Still ~45% cheaper than Sonnet-class pricing. 100-200ms latency. Best for complex tasks where accuracy matters but cost is secondary. --- ## Pricing comparison: Nova vs. Claude > **Pricing note:** Pricing as of July 2026. Model prices and availability change frequently. For current rates, verify at [AWS Bedrock Pricing](https://aws.amazon.com/bedrock/pricing/). ### Input token pricing (per 1M tokens) ``` Claude Haiku 4.5: $0.80 Claude Sonnet 5/4.6: $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 Haiku 4.5: $4.00 Claude Sonnet 5/4.6: $15.00 Nova Micro: $0.60 (-85% vs Haiku) Nova Lite: $0.80 (-80% vs Haiku) Nova Pro: $4.80 (-68% vs Sonnet) ``` --- ## Scenario: customer support at scale **Setup:** 100K customer support tickets/month. Average ticket = 2 paragraphs (~400 tokens input). ### Option 1: Claude Haiku 4.5 ``` 100K tickets × 400 input tokens = 40M input tokens 40M × $0.80 = $32/month input cost 100K tickets × 100 output tokens = 10M output tokens 10M × $4.00 = $40/month output cost Total: $72/month ``` ### Option 2: Nova Micro ``` 40M × $0.30 = $12/month input cost 10M × $0.60 = $6/month output cost Total: $18/month Savings: $54/month ($648/year) ``` ### Option 3: Nova Lite (slightly better accuracy) ``` 40M × $0.40 = $16/month input 10M × $0.80 = $8/month output Total: $24/month Savings: $48/month ($576/year) ``` **Decision:** For customer support classification, Nova Micro cuts the bill by 75% with acceptable accuracy. If accuracy is critical, Nova Lite at $24/month is still 67% cheaper than Claude Haiku. At 100K tickets the absolute numbers are small; the percentages are what carry to larger volumes. --- ## 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 close to Sonnet and 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:** Claude still wins on complex reasoning, though 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 | Workload | Best Model | Why | | ----------------------------------- | ------------- | --------------------------------------- | | Content moderation | Nova Micro | High volume, binary decisions | | Email classification | Nova Lite | Good accuracy, fast, cheap | | Customer support (reply generation) | Nova Pro | Balance of quality and cost | | Code generation | Claude Sonnet | Accuracy matters most | | Document summarization | Nova Lite | Context window sufficient, cost matters | | Multi-step analysis | Claude Sonnet | Complex reasoning required | | RAG retrieval feedback | Nova Micro | Simple ranking, high volume | | Creative writing | Claude Sonnet | Quality non-negotiable | --- ## Migration path from Claude to 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) ```python 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: $72/month Nova Lite: - Accuracy: 91% - Latency: 38ms - Cost: $24/month Decision: Slight accuracy trade-off (-1%) but 67% cost savings + 26% faster. ``` ### Step 4: Gradual rollout (Week 4) ```python # 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 and quality trade-off table | Budget | Model Choice | Expected Accuracy | Monthly Savings | | ---------- | ------------- | ----------------- | -------------------- | | $10K/month | Nova Micro | 75-80% | vs Claude baseline | | $20K/month | Nova Lite | 85-92% | 50% vs Claude Haiku | | $50K/month | Nova Pro | 90-95% | 40% vs Claude Sonnet | | Unlimited | Claude Sonnet | 95%+ | Best accuracy | --- ## 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](https://aws.amazon.com/bedrock/pricing/) - [AWS Bedrock Cost Optimization](https://www.joinhorizon.co/blog/aws-bedrock-cost-optimization-token-budgets-model-selection/) - [AWS AI Agents on Bedrock](https://www.joinhorizon.co/blog/aws-bedrock-ai-agents-agentic-workflows/) - [Claude Fable 5 / 5.1 on AWS](https://www.joinhorizon.co/blog/claude-fable-5-aws-bedrock-mythos-class-guide-2026/): when long-running autonomous work beats Nova or Sonnet on cost-for-quality (Covered Model gate) --- ## Evaluating Nova for your Bedrock workloads If you're already using Claude on Bedrock and want to evaluate Nova, [book a free GenAI assessment](https://www.joinhorizon.co/services/generative-ai-on-aws/). We'll analyze your model usage, identify candidates for Nova migration, and project your cost savings. ## Related reading - [How to Build Multi-Tenant GenAI on AWS Bedrock](https://www.joinhorizon.co/blog/multi-tenant-genai-bedrock/) ## FAQ ### When should I use Nova instead of Claude? Use Nova when cost is the primary constraint and you need 80-90% of Claude's accuracy. Nova 2 Lite (December 2, 2025) is the current everyday Nova: multimodal, 1M context. Nova Micro remains the cheapest published text-only lane for high-volume, low-complexity tasks. Prior-generation Nova Pro still offers better Nova 1 reasoning at lower cost than Claude Sonnet 5. Use Claude Haiku 5.5 (October 7, 2026) for fast Claude-class volume (Haiku 4.5 remains the prior pin), Sonnet 5 for agentic/coding lanes, Fable 5.1 (September 1, 2026, Covered Model) for extended autonomous work after legal sign-off, or Opus 5 when accuracy and ZDR dominate cost. ### How much cheaper is Nova than Claude? Nova Micro: ~60% cheaper than Claude Haiku 4.5 on the published Nova 1 list rates this post modeled. Nova Pro: ~45% cheaper than Claude Sonnet 5 / Sonnet 4.6 class models. We do not quote unpublished Nova 2 Lite $/MTok here. Verify live rates at the Bedrock pricing page. For organizations running 10M+ tokens/month, Nova can reduce inference costs materially. ### Will my application work if I switch from Claude to Nova? Yes, with caveats. Nova understands the same prompt format and JSON schema instructions as Claude. However, Nova's reasoning capability is weaker for complex multi-step tasks. Best practice: test Nova on 10% of your traffic first, monitor quality metrics (accuracy, latency, errors), then decide whether to migrate fully. ### What tasks are Nova Micro best for? Micro (2.4K context) excels at customer support replies, content moderation, text classification, sentiment analysis, and simple summarization. It is not suitable for long-form generation, multi-document analysis, or complex reasoning. ### What is the context window for each Nova model? Nova 2 Lite: 1M tokens (current everyday Nova). Nova Micro: 4K tokens. Nova Lite: 300K tokens. Nova Pro: 300K tokens. Context is the amount of text the model can consider when generating responses. More context lets the model handle larger documents and longer conversations. --- *Source: https://www.joinhorizon.co/blog/aws-bedrock-nova-models-guide/*