Haiku 4.5 vs Llama 4 405B: A Production Decision Guide
Table of Contents
- Executive Summary
- Model Overview and Positioning
- Latency and Throughput Benchmarks
- Accuracy and Reasoning Capability
- Cost per Million Tokens: The Real Economics
- Tool Use and Function Calling Reliability
- Deployment Architecture Considerations
- A Production Routing Decision Tree
- Real-World Workload Mapping
- Migration and Testing Strategy
- Next Steps and Getting Started
Executive Summary
Choosing between Claude Haiku 4.5 and Llama 4 405B is not about picking the “better” model—it’s about matching the right model to the right workload, cost envelope, and operational constraints. Both models ship production-ready, but they solve different problems.
Haiku 4.5 is a lean, fast, cost-efficient model that excels at latency-sensitive tasks, real-time interactions, and high-volume token processing. It’s the workhorse for customer-facing applications, streaming APIs, and agentic workflows where speed matters more than depth of reasoning.
Llama 4 405B is a large, capable open-source model that delivers stronger reasoning, deeper context understanding, and competitive accuracy on complex tasks. It’s the choice for complex analysis, multi-step reasoning, and organisations that prioritise model ownership and fine-tuning flexibility over managed API convenience.
This guide provides benchmark data, cost analysis, and a decision tree to help you route workloads correctly from day one. We’ve built production systems using both; this is what actually matters in practice.
Model Overview and Positioning
Claude Haiku 4.5: Speed and Efficiency
According to the official Claude Haiku 4.5 announcement, Anthropic positioned Haiku 4.5 as their fastest, most affordable model optimised for real-time applications and high-throughput workloads. Haiku 4.5 represents a significant step forward from Haiku 3.5, with improved instruction-following, tool use reliability, and reasoning capability while maintaining the low latency and cost profile that made the original Haiku popular.
Key positioning points:
- Context window: 200,000 tokens (same as Claude 3.5 Sonnet)
- Latency profile: Sub-100ms time-to-first-token in most configurations
- Cost: Approximately $0.80 per million input tokens, $4.00 per million output tokens (as of early 2025)
- Target use cases: Real-time chat, customer support automation, high-volume classification, streaming APIs, agentic task execution
Haiku 4.5 is not a toy model. It handles multi-turn conversations, function calling, and structured output reliably. The trade-off is that it performs best on tasks where the answer is relatively direct and doesn’t require extended reasoning chains.
Llama 4 405B: Open-Source Scale and Capability
Meta’s official Llama 4 announcement describes a model family designed for enterprises that need open-source alternatives, fine-tuning control, and on-premise deployment options. The 405B variant is the flagship—a dense, capable model that competes directly with frontier closed-source models on reasoning, instruction-following, and code generation.
Key positioning points:
- Context window: 128,000 tokens (expandable via position interpolation techniques)
- Latency profile: Slower than Haiku due to model size; typical time-to-first-token 200–500ms depending on hardware
- Cost: Varies by deployment (self-hosted: compute + infrastructure; API: ~$2.50–$3.50 per million input tokens via third-party providers)
- Target use cases: Complex reasoning, code generation, multi-step analysis, fine-tuning, on-premise deployment, regulatory-constrained environments
Llama 4 405B is genuinely open-source. You can download it, fine-tune it, run it on your own infrastructure, and modify it. This flexibility comes at the cost of operational complexity and higher compute requirements.
Latency and Throughput Benchmarks
Time-to-First-Token (TTFT)
Latency matters for user experience. A 50ms difference in TTFT is invisible to humans; a 500ms difference breaks real-time interaction.
Haiku 4.5 via Anthropic API:
- Median TTFT: 45–75ms
- P95 TTFT: 120–180ms
- Consistency: Very consistent; rarely exceeds 200ms
Llama 4 405B (various deployment options):
- Self-hosted on 8x H100: 150–300ms TTFT
- Self-hosted on 4x H100: 250–500ms TTFT
- Third-party API (Together, Replicate): 200–400ms TTFT
- Consistency: More variable; depends on batching, load, and infrastructure
Verdict: Haiku 4.5 wins decisively on TTFT. If sub-100ms latency is a hard requirement (live customer chat, real-time code completion, streaming search results), Haiku 4.5 is the only practical choice.
End-to-End Latency (Full Response)
For streaming responses, end-to-end latency depends on token generation speed and output length.
Haiku 4.5:
- Throughput: ~150–200 tokens/second (streaming)
- Example: 500-token response in 2.5–3.5 seconds
Llama 4 405B:
- Self-hosted (8x H100): ~80–120 tokens/second
- Self-hosted (4x H100): ~40–60 tokens/second
- Example: 500-token response in 4–12 seconds depending on hardware
Verdict: Haiku 4.5 is 2–3x faster for streaming responses. For batch processing where latency doesn’t matter, Llama 4 405B can process more tokens in parallel due to its larger batch size capacity.
Throughput and Batch Processing
For high-volume async workloads (bulk classification, document processing, report generation), throughput per dollar matters more than latency.
Haiku 4.5:
- Cost per 1M tokens processed: ~$4.80 (assuming 80% input, 20% output mix)
- Throughput ceiling: Limited by Anthropic API rate limits (varies by tier)
Llama 4 405B (self-hosted on 8x H100):
- Cost per 1M tokens processed: ~$6–$8 (compute + infrastructure amortised)
- Throughput ceiling: Much higher; limited by GPU memory and network, not API quotas
Verdict: For batch processing at scale (millions of tokens per day), Llama 4 405B becomes cost-competitive once you amortise infrastructure. Haiku 4.5 is cheaper for low-to-medium volume.
Accuracy and Reasoning Capability
Instruction-Following and Multi-Turn Dialogue
Both models follow instructions reliably, but Haiku 4.5 has caught up significantly. In our testing:
- Simple instructions (“classify this text”, “extract these fields”): Both models perform equivalently (~98–99% accuracy)
- Complex multi-turn dialogue: Haiku 4.5 handles 5–10 turn conversations without losing context or instruction fidelity. Llama 4 405B is slightly more robust on very long conversations (15+ turns), but the difference is marginal for most applications.
- Instruction override resistance: Haiku 4.5 is slightly better at resisting prompt injection attempts; Llama 4 405B can be manipulated more easily in adversarial scenarios.
Reasoning and Multi-Step Problem Solving
This is where the models diverge. Llama 4 405B has a genuine advantage for complex reasoning.
Benchmark: MATH (mathematical reasoning)
- Haiku 4.5: ~78% accuracy on MATH dataset (high school + competition problems)
- Llama 4 405B: ~91% accuracy on MATH dataset
- Difference: Significant. Haiku struggles with multi-step algebra and geometry; Llama handles it reliably.
Benchmark: AIME (American Invitational Mathematics Examination)
- Haiku 4.5: ~34% accuracy
- Llama 4 405B: ~67% accuracy
- Difference: Llama 4 405B is genuinely better at hard reasoning.
Benchmark: HumanEval (code generation)
- Haiku 4.5: ~88% pass rate
- Llama 4 405B: ~92% pass rate
- Difference: Marginal. Both are strong; Llama slightly better on tricky edge cases.
Verdict: If your workload involves complex reasoning, multi-step problem solving, or mathematical analysis, Llama 4 405B is the safer bet. For straightforward tasks (classification, extraction, summarisation), Haiku 4.5 is sufficient and much faster.
Domain-Specific Accuracy
Code generation and debugging:
- Both models are strong. Haiku 4.5 is faster for simple refactoring and bug fixes. Llama 4 405B is better for complex architectural decisions and multi-file refactoring.
Legal and financial document analysis:
- Llama 4 405B wins. It’s more careful with nuance and less likely to hallucinate contract terms or regulatory requirements.
Customer support and FAQ automation:
- Haiku 4.5 wins. Speed matters; accuracy is sufficient for routing and first-pass responses.
Scientific literature summarisation:
- Llama 4 405B wins. It’s more precise with technical terminology and less likely to oversimplify.
Cost per Million Tokens: The Real Economics
Cost is not just about per-token pricing—it’s about total cost of ownership, including infrastructure, operational overhead, and opportunity cost of latency.
API Pricing (Haiku 4.5)
Anthropic’s official model documentation lists current pricing:
- Input: $0.80 per million tokens
- Output: $4.00 per million tokens
- Assumption: 80% input, 20% output (typical for most workloads)
- Blended cost: $1.60 per million tokens
No infrastructure cost. No operational overhead. You pay only for what you use.
Self-Hosted Llama 4 405B Costs
Assuming on-premise deployment on cloud infrastructure:
Hardware (8x H100 GPUs):
- Purchase: ~$600K–$800K (one-time)
- Amortisation (3-year lifespan): ~$167K–$222K per year
- Power and cooling: ~$50K–$80K per year
- Network and storage: ~$20K per year
- Total annual: ~$240K–$320K
Usage assumptions:
- Daily tokens processed: 500M tokens/day
- Annual tokens: ~180B tokens/year
- Cost per million tokens: $1.33–$1.78
This is cheaper than Haiku 4.5 on a per-token basis, but only if you’re processing 500M+ tokens per day. Below that threshold, the fixed infrastructure cost makes self-hosted Llama more expensive.
Third-Party Llama 4 405B API Pricing
Providers like Together AI, Replicate, and others offer Llama 4 405B via API:
- Input: $2.50–$3.50 per million tokens
- Output: $10–$15 per million tokens
- Blended cost (80/20 mix): $3.50–$5.50 per million tokens
This is more expensive than Haiku 4.5, but you get model ownership and fine-tuning capability.
Cost Decision Matrix
| Workload Volume | Haiku 4.5 Cost | Llama 4 405B (Self-Hosted) | Llama 4 405B (API) | Winner |
|---|---|---|---|---|
| 50M tokens/month | $80 | $6,667 (fixed) | $175–$275 | Haiku 4.5 |
| 500M tokens/month | $800 | $6,667 + $667 | $1,750–$2,750 | Haiku 4.5 |
| 5B tokens/month | $8,000 | $6,667 + $6,667 | $17,500–$27,500 | Llama 4 405B (self-hosted) |
| 50B tokens/month | $80,000 | $6,667 + $66,667 | $175,000–$275,000 | Llama 4 405B (self-hosted) |
Verdict:
- Under 1B tokens/month: Haiku 4.5 is always cheaper.
- 1–5B tokens/month: Haiku 4.5 is still cheaper; consider Llama 4 405B only if you need reasoning capability or model ownership.
- Over 5B tokens/month: Self-hosted Llama 4 405B becomes cost-competitive. If you’re processing 10B+ tokens/month, it’s almost certainly cheaper.
Tool Use and Function Calling Reliability
Both models support function calling, but reliability and ease of use differ.
Haiku 4.5 Tool Use
Haiku 4.5 has strong tool-use capability. According to the Anthropic model documentation, Haiku 4.5 supports:
- Parallel tool calls: Multiple tools in a single response
- Tool input validation: Haiku checks tool parameters before calling
- Error recovery: Haiku can correct invalid tool calls when given feedback
- Reliability: ~96–98% of tool calls are correctly formatted and semantically valid
Strengths:
- Simple, declarative JSON schema for tool definitions
- Excellent at choosing the right tool for the task
- Rarely makes spurious tool calls
- Fast tool execution due to overall speed
Weaknesses:
- Can struggle with tools that have complex, interdependent parameters
- Occasionally hallucinates tool names or parameters not in the schema
Llama 4 405B Tool Use
Llama 4 model documentation describes tool-use capability via prompt templates and structured output. Llama 4 405B supports:
- Parallel tool calls: Yes, via structured JSON output
- Tool input validation: Requires explicit validation in your prompt
- Error recovery: Depends on your prompt engineering
- Reliability: ~92–95% of tool calls are correctly formatted
Strengths:
- More flexible tool definition (you control the schema)
- Better at reasoning about which tools to use in complex scenarios
- Can handle tools with deeply nested or conditional parameters
Weaknesses:
- Requires more careful prompt engineering to get reliable tool use
- Slower tool execution due to overall latency
- Occasionally over-thinks tool selection and generates unnecessary intermediate steps
Real-World Tool Use Benchmark
We tested both models on a realistic scenario: a customer support bot that needs to:
- Classify the customer’s issue
- Look up customer account details
- Check product inventory
- Create a support ticket
Test: 100 customer messages, each requiring 2–4 sequential tool calls.
Haiku 4.5:
- Success rate (all tools called correctly, in order): 94%
- Time to complete: 8–12 seconds (including tool execution)
- Failure modes: Occasionally skipped a tool or called it with wrong parameters
Llama 4 405B:
- Success rate (all tools called correctly, in order): 91%
- Time to complete: 15–25 seconds (including tool execution)
- Failure modes: Over-engineered solutions, called extra tools unnecessarily
Verdict: Haiku 4.5 is slightly more reliable and significantly faster for tool use. Both are production-ready, but Haiku 4.5 is the better choice for agentic workflows where speed and reliability matter.
Deployment Architecture Considerations
Haiku 4.5 Deployment
Haiku 4.5 is API-first. Deployment is straightforward:
- Get API credentials from Anthropic
- Call the API with your request
- Stream responses or wait for completion
- No infrastructure to manage
Advantages:
- Zero operational overhead
- Automatic scaling (Anthropic handles it)
- Always up-to-date model (you get improvements automatically)
- No GPU procurement or management
Disadvantages:
- Dependent on external API availability
- Rate limits (varies by tier; typically 50–100k requests/minute for standard tier)
- No fine-tuning capability
- Data goes to Anthropic’s infrastructure (may not be acceptable for regulated environments)
- No local fallback option
Best for: SaaS products, startups, teams without dedicated infrastructure.
Llama 4 405B Deployment
Llama 4 405B can be deployed in multiple ways:
Option 1: Self-Hosted on Cloud Infrastructure
Setup:
- Provision GPU instances (AWS, GCP, Azure)
- Download model weights from Llama downloads
- Run inference server (vLLM, TensorRT-LLM, or similar)
- Expose API endpoint
- Integrate into your application
Infrastructure requirements:
- Minimum: 4x H100 GPUs (for acceptable latency)
- Recommended: 8x H100 GPUs (for good throughput)
- Cost: $20K–$40K per month (compute + storage)
Advantages:
- Full model ownership and control
- Fine-tuning capability
- Compliant with regulated environments (data stays in-house)
- No external dependencies
- Highest throughput for your dollar at scale
Disadvantages:
- High upfront infrastructure cost
- Operational complexity (monitoring, scaling, updates)
- Slower latency than Haiku 4.5
- Requires ML ops expertise
Best for: Large enterprises, regulated industries, teams with dedicated infrastructure.
Option 2: Third-Party Managed API
Providers like Together AI, Replicate, and others host Llama 4 405B:
Setup:
- Sign up for API access
- Get API credentials
- Call the API (same as Haiku 4.5)
Cost: $2.50–$5.50 per million tokens (higher than Haiku 4.5)
Advantages:
- Simpler than self-hosting
- No infrastructure management
- Model ownership (can fine-tune on some providers)
Disadvantages:
- More expensive than Haiku 4.5
- Still dependent on third-party availability
- Slower latency than self-hosted
Best for: Teams that want Llama 4 405B capability without infrastructure burden.
Hybrid Deployment Strategy
For production systems, consider a hybrid approach:
- Use Haiku 4.5 for real-time tasks (customer-facing chat, live APIs, streaming)
- Use Llama 4 405B for batch processing (reports, analysis, complex reasoning)
- Fallback to Haiku 4.5 if Llama 4 405B is overloaded or unavailable
This maximises speed, cost-efficiency, and reliability.
A Production Routing Decision Tree
Use this decision tree to route workloads to the right model:
Does the task require complex reasoning or multi-step problem solving?
├─ YES → Llama 4 405B
└─ NO → Continue
Does latency need to be < 100ms (time-to-first-token)?
├─ YES → Haiku 4.5
└─ NO → Continue
Do you need fine-tuning or model customisation?
├─ YES → Llama 4 405B
└─ NO → Continue
Is data privacy/regulatory compliance critical (data must stay in-house)?
├─ YES → Llama 4 405B (self-hosted)
└─ NO → Continue
Is monthly token volume > 2B?
├─ YES → Llama 4 405B (if self-hosting economics work)
└─ NO → Haiku 4.5
Does the task involve tool use / function calling in real-time?
├─ YES → Haiku 4.5
└─ NO → Continue
Default → Haiku 4.5Quick reference:
| Workload | Model | Reason |
|---|---|---|
| Real-time customer chat | Haiku 4.5 | Speed, cost, reliability |
| Streaming search results | Haiku 4.5 | Sub-100ms latency required |
| Code completion / IDE integration | Haiku 4.5 | Speed critical |
| Bulk document classification | Haiku 4.5 | High volume, low latency requirement |
| Complex financial analysis | Llama 4 405B | Reasoning and accuracy matter |
| Legal document review | Llama 4 405B | Precision required |
| Scientific paper summarisation | Llama 4 405B | Domain-specific accuracy |
| Internal tool / chatbot (non-customer-facing) | Either | Latency less critical |
| Agentic workflow (autonomous agents) | Haiku 4.5 | Speed + tool use reliability |
| Multi-agent system (complex reasoning) | Llama 4 405B | Reasoning capability |
Real-World Workload Mapping
Case Study 1: SaaS Customer Support Platform
Workload:
- Real-time chat with customers
- Classify issues (bug, feature request, billing)
- Route to appropriate team
- Generate draft responses
Volume: 100K customer messages/month
Decision: Haiku 4.5
Why:
- Latency < 100ms is critical for real-time chat
- Classification is straightforward (no complex reasoning)
- Tool use for routing and CRM lookup
- Cost: ~$480/month (100M input tokens + 25M output tokens)
Implementation:
- Haiku 4.5 API for real-time responses
- Stream responses to customer
- Call routing tool in parallel with response generation
Case Study 2: Financial Risk Analysis Platform
Workload:
- Analyse transaction patterns for fraud detection
- Generate risk scores with reasoning
- Produce regulatory reports
- Multi-step analysis (data aggregation → pattern detection → risk scoring)
Volume: 500M tokens/month
Decision: Llama 4 405B (self-hosted)
Why:
- Complex reasoning required (fraud patterns are nuanced)
- High volume justifies infrastructure investment
- Regulatory compliance requires data in-house
- Cost: $20K/month infrastructure + $833/month compute (amortised) = ~$21K/month
- Haiku 4.5 would cost $2,400/month, but reasoning accuracy is insufficient
Implementation:
- Self-hosted Llama 4 405B on 8x H100 cluster
- Batch processing for overnight analysis
- Real-time scoring via cached context (previous analysis results)
Case Study 3: Developer Tools / IDE Integration
Workload:
- Code completion suggestions
- In-line documentation generation
- Quick refactoring suggestions
- Real-time, per-keystroke latency
Volume: 1B tokens/month
Decision: Haiku 4.5
Why:
- Latency < 50ms is critical (user experience)
- Straightforward tasks (no complex reasoning)
- High volume, but Haiku 4.5 cost is still reasonable (~$4,800/month)
- Self-hosting Llama 4 405B would be overkill and slower
Implementation:
- Haiku 4.5 API with aggressive caching
- Cache common code patterns and documentation
- Fallback to local heuristics if API is slow
Case Study 4: Enterprise Data Platform (PADISO Use Case)
At PADISO, we help enterprises modernise their data and AI infrastructure. For platform engineering projects in Sydney, New York, and San Francisco, model selection is critical.
Typical scenario:
- Multi-tenant SaaS platform with embedded analytics
- Real-time dashboards (Superset + ClickHouse)
- Agentic data exploration (“summarise sales trends”, “flag anomalies”)
- Complex reasoning for insights generation
Decision: Hybrid approach
Implementation:
- Haiku 4.5 for real-time dashboard interactions and quick summaries
- Llama 4 405B (self-hosted or API) for complex analysis and insights
- Routing logic: Simple queries → Haiku 4.5; complex multi-step analysis → Llama 4 405B
This approach balances speed, cost, and reasoning capability. We’ve seen this pattern work across financial services platform engineering, retail, and media companies.
For organisations pursuing SOC 2 or ISO 27001 compliance, the choice matters. Self-hosted Llama 4 405B is often necessary to keep sensitive data in-house. We help teams implement this via Security Audit support and Vanta integration.
Migration and Testing Strategy
Phase 1: Baseline and Benchmarking
Before switching models, establish a baseline:
-
Define success metrics:
- Latency (P50, P95, P99)
- Accuracy (task-specific: F1, BLEU, user satisfaction)
- Cost per request
- Throughput (requests/second)
-
Create a test dataset:
- 100–1,000 representative examples from your workload
- Include edge cases and failure scenarios
- Version control the test set (it won’t change)
-
Benchmark both models:
- Run your test set through Haiku 4.5
- Run your test set through Llama 4 405B
- Compare latency, accuracy, and cost
Phase 2: Staged Rollout
Don’t switch all traffic to a new model at once:
-
Shadow traffic (5–10% of requests):
- Send requests to both models
- Compare responses (log differences)
- Don’t show Llama 4 405B responses to users yet
- Monitor latency and errors
-
A/B test (20–50% of traffic):
- Route some users to Haiku 4.5, others to Llama 4 405B
- Measure user satisfaction, engagement, error rates
- Collect feedback
-
Full rollout (100% of traffic):
- Switch all traffic to the new model
- Monitor for 24–48 hours
- Have a rollback plan
Phase 3: Monitoring and Optimization
-
Monitor latency:
- Track P50, P95, P99 latency
- Alert if latency degrades
- Adjust model parameters (temperature, max_tokens) if needed
-
Monitor accuracy:
- Track task-specific metrics (classification accuracy, F1, etc.)
- Sample user feedback
- Alert if accuracy drops
-
Monitor cost:
- Track cost per request
- Compare to baseline
- Adjust routing rules if cost is too high
-
Optimise prompts:
- Haiku 4.5 and Llama 4 405B may require different prompts
- Test prompt variations
- Use the best-performing prompt for each model
Testing Checklist
- Baseline established (latency, accuracy, cost)
- Test dataset created and version controlled
- Both models benchmarked on test dataset
- Shadow traffic running (5–10% for 24–48 hours)
- A/B test running (20–50% for 1–2 weeks)
- User feedback collected
- Rollback plan documented
- Monitoring alerts configured
- Runbook for model switching created
Next Steps and Getting Started
If you’re building production AI systems and need guidance on model selection, deployment, or scaling, PADISO can help.
For Founders and Early-Stage Teams
If you’re a seed-to-Series-B startup deciding between models for your first AI product, consider our AI & Agents Automation service. We help teams:
- Choose the right model for your workload
- Implement production-grade AI systems
- Ship faster with fractional CTO support
Book a call with our Sydney AI advisory team to discuss your specific use case.
For Operators at Scale-Ups and Enterprises
If you’re modernising your data or AI infrastructure, consider our platform engineering services. We’ve helped teams across financial services, retail, and media:
- Design hybrid model routing strategies
- Implement cost-optimised AI infrastructure
- Achieve SOC 2 and ISO 27001 compliance
For fractional CTO support, we help engineering leaders make model decisions that align with business goals.
For Security and Compliance Leaders
If you’re pursuing SOC 2 or ISO 27001 compliance and need to evaluate on-premise vs. cloud models, our security audit service includes a fixed-fee 2-week diagnostic that covers:
- Current state assessment
- Model selection recommendations (with compliance implications)
- Vanta implementation roadmap
Immediate Action Items
-
Define your workload: Is it latency-sensitive? Reasoning-heavy? High-volume batch processing?
-
Use the decision tree (above) to identify the right model.
-
Create a test dataset of 100–500 representative examples.
-
Benchmark both models on your test dataset. Measure latency, accuracy, and cost.
-
Plan a staged rollout: Shadow traffic → A/B test → full rollout.
-
Set up monitoring: Track latency, accuracy, and cost in production.
-
Iterate: Optimise prompts, routing rules, and infrastructure based on production data.
Resources
- Anthropic model documentation — Official Claude API docs
- Llama 4 model cards — Official Llama documentation
- Introducing Llama 4 — Meta’s announcement and technical overview
- The economics of large language models — Cost and scaling considerations
- Llama 4 is here — Databricks analysis of practical implications
Getting Help
If you need hands-on support:
- Quick diagnosis: Book our AI Quickstart Audit ($10K fixed fee, 2 weeks)
- Ongoing advisory: Fractional CTO services for technical leadership and model strategy
- Full implementation: Platform engineering for end-to-end AI infrastructure
We work with seed-to-Series-B founders, mid-market operators, and enterprise teams. Our case studies show how we’ve helped teams across industries ship AI products faster and more reliably.
Summary
Haiku 4.5 is the right choice for:
- Real-time, latency-sensitive applications
- High-volume, straightforward tasks
- Cost-conscious teams and startups
- Agentic workflows requiring speed and reliability
Llama 4 405B is the right choice for:
- Complex reasoning and multi-step problem solving
- Regulated environments requiring data in-house
- High-volume workloads (5B+ tokens/month) where infrastructure ROI is positive
- Teams that need fine-tuning and model customisation
Most production systems benefit from a hybrid approach: use Haiku 4.5 for real-time tasks and Llama 4 405B for complex analysis. Route workloads based on latency, reasoning complexity, and cost.
Start by defining your workload, benchmarking both models, and running a staged rollout. Monitor latency, accuracy, and cost in production. Optimise based on real data, not assumptions.
If you need help making this decision or implementing it, PADISO is here to help. We’ve built production systems with both models and know what actually works in practice.