GPT-5.4 Mini vs Nano vs Claude Haiku: Which Cheap AI Model Should You Actually Use?

Budget AI models used to mean "worse." In March 2026, they mean "specialized."

OpenAI just dropped GPT-5.4 Mini and Nano — two new models designed not to compete with frontier models, but to power the workers under them. Anthropic's Claude Haiku has been doing this for months. Together, these three models represent the emerging "sub-agent tier" of AI: fast, cheap, and good enough for the tasks that don't need a $15-per-million-token brain.

If you're building multi-agent workflows, delegating sub-tasks from an orchestrator, or just trying to keep your API bill under control — this is the comparison you need. We'll break down pricing, context windows, capabilities, and give you a clear decision framework for when to use each one.

The Sub-Agent Economy Is Real

Here's the shift that makes this comparison matter: modern AI development isn't one model doing everything. It's an orchestrator (GPT-5.4, Claude Opus 4, Gemini 2.0 Ultra) delegating dozens or hundreds of sub-tasks to cheaper models. The orchestrator reasons about what to do. The sub-agents do it.

This is the pattern behind Claude Code, OpenAI's Codex, and every serious agent framework. And the economics are brutal — if your orchestrator spawns 50 sub-agent calls per user request, you need those calls to cost fractions of a cent. That's what Mini, Nano, and Haiku are built for.

Pricing Comparison: The Numbers That Matter

Let's start with what hits your wallet:

What jumps out:

• Nano is absurdly cheap. At $0.20/$1.25 per million tokens, it's roughly 4x cheaper than Mini and Haiku on input, 3x cheaper on output. For high-volume repetitive tasks, this adds up fast.
• Mini and Haiku are near-identical on price. Mini is slightly cheaper on input ($0.75 vs $0.80), Haiku is slightly cheaper on output ($4.00 vs $4.50). The difference is negligible — your choice should be about capability, not cost.
• Context window is the real differentiator. Mini and Nano both offer 400K tokens — double Haiku's 200K. If your sub-tasks involve large documents or long conversation histories, OpenAI's models have a structural advantage.

Capability Breakdown: What Each Model Actually Does Well

Price is table stakes. What matters is whether the model can actually do the job you're delegating to it. Here's where they diverge.

GPT-5.4 Mini — The Capable Middle Manager

Mini is the "smart sub-agent." It's not frontier-class, but it can handle genuinely complex tasks that require reasoning, tool use, and multi-step planning. Think of it as the model you trust with the tasks that are too hard for a template but not worth burning full GPT-5.4 on.

Sweet spots:

• Computer use and UI parsing — Mini is specifically optimized for reading dense UIs at speed. Navigating web interfaces, extracting structured data from complex pages, filling forms. This is its killer feature over Haiku.
• Complex sub-task delegation — Code review of individual files, summarizing research papers, analyzing customer feedback with nuanced categorization.
• Multi-step tool use — Chaining API calls, querying databases, processing results. Mini handles tool-use workflows reliably.
• Coding tasks on Codex — At 30% quota, Mini is the default workhorse for Codex-based development workflows. Write tests, refactor functions, generate boilerplate.

Where it struggles:

• Extended creative writing (use a frontier model)
• Novel problem-solving requiring breakthrough reasoning
• Tasks where 400K context still isn't enough (rare, but possible with massive codebases)

GPT-5.4 Nano — The Tireless Intern

Nano is built for one thing: doing a lot of simple work, very cheaply. It's the model you point at a list of 10,000 items and say "process all of these." The per-task cost is so low that you can afford to be aggressive with parallelization and retry strategies.

Sweet spots:

• Bulk data processing — Classification, tagging, entity extraction across thousands of records. Nano handles this at a cost that makes batch processing economically viable.
• Long, repetitive tasks — Reformatting data, translating templates, generating variations. The 400K context window means you can stuff a lot of context in without hitting limits.
• Grunt work in agent pipelines — The first-pass filter in a multi-stage pipeline. Nano screens 1,000 items, passes the 50 interesting ones to Mini or Haiku for deeper analysis.
• Simple Q&A and lookup — Answering straightforward questions from provided context. Not sophisticated reasoning, but reliable extraction.
• Log parsing and monitoring — Processing application logs, extracting error patterns, formatting alerts.

Where it struggles:

• Anything requiring nuanced reasoning or judgment
• Complex code generation beyond boilerplate
• Tasks where errors are expensive (Nano's error rate is higher — plan for retries)
• Computer use / UI interaction (not supported)

Claude Haiku — The Precise Specialist

Haiku takes a different approach than OpenAI's models. Where Mini is a generalist sub-agent and Nano is a bulk processor, Haiku is optimized for quality per token. Anthropic's emphasis on instruction-following and safety means Haiku tends to produce cleaner, more predictable output — even at the budget tier.

Sweet spots:

• Instruction-following precision — Haiku excels at following complex, multi-constraint prompts. "Extract all dates, format as ISO 8601, exclude anything before 2025, output as JSON array." Haiku nails this more consistently than Mini.
• Writing and content tasks — Haiku inherits Anthropic's writing DNA. For sub-tasks that involve generating human-readable text — email drafts, content summaries, documentation snippets — Haiku produces noticeably better prose than Mini or Nano.
• Safety-critical sub-tasks — If your pipeline processes user-generated content, Haiku's safety training makes it less likely to pass through problematic content or follow injection attempts.
• Code review and analysis — Haiku is strong at reading and reasoning about code, identifying bugs, suggesting improvements. Anthropic's models have consistently punched above their weight on code tasks.
• Computer use — Haiku supports Anthropic's computer use capability, making it viable for UI automation tasks (though Mini is more optimized for this).

Where it struggles:

• Raw throughput on bulk tasks (more expensive than Nano by 4x on input)
• 200K context window limits document-heavy workflows
• No equivalent of the Codex quota discount
• Less competitive on purely mechanical, high-volume processing

Head-to-Head: Real-World Task Performance

Theory is one thing. Here's how they compare on actual tasks developers care about:

Task 1: Code Review (Single File, ~500 lines)

• Mini: Catches logical bugs, suggests architectural improvements, identifies security issues. Solid.
• Nano: Catches syntax issues and obvious bugs. Misses subtle logic errors. Adequate for a first pass.
• Haiku: Catches bugs, provides clear explanations, suggests fixes with good code style. Slightly more thorough than Mini on instruction-following.
• Winner: Haiku by a hair, Mini close second. Use Nano only as a pre-filter.

Task 2: Data Extraction from 1,000 Product Listings

• Mini: Accurate but expensive at scale. $0.75/MTok × 1,000 items adds up.
• Nano: Fast, cheap, and accurate enough for structured extraction. The clear choice.
• Haiku: More accurate than Nano, but at 4x the input cost. Not worth it for straightforward extraction.
• Winner: Nano. This is exactly what it's built for.

Task 3: Summarize a 100-Page Technical Document

• Mini: Handles it well within its 400K context. Good at identifying key themes and technical details.
• Nano: Can fit the document, but summaries are superficial. Misses nuance.
• Haiku: Context window is the bottleneck. May need chunking for very large documents, adding pipeline complexity.
• Winner: Mini. The 400K context + decent reasoning is the right combination.

Task 4: Generate API Documentation from Code

• Mini: Produces accurate, well-structured documentation. Handles edge cases in the code.
• Nano: Generates functional but bland documentation. Misses contextual explanations.
• Haiku: Produces the most readable documentation with clear explanations. Anthropic's writing quality shows.
• Winner: Haiku for developer-facing docs. Mini for internal/generated docs. Nano for bulk endpoint stubs.

Task 5: UI Automation — Fill Out a Multi-Step Web Form

• Mini: Built for this. Computer use capability handles complex forms reliably.
• Nano: Cannot do this. No computer use support.
• Haiku: Can do this via Anthropic's computer use, but Mini is more optimized for speed on dense UIs.
• Winner: Mini, decisively. This is its marquee feature.

The Decision Flowchart

Stop overthinking it. Here's how to choose:

START: What's the task?
│
├─ Is it simple, repetitive, and high-volume?
│  └─ YES → Use Nano ($0.20/$1.25)
│     Examples: data classification, log parsing, 
│     template filling, bulk extraction
│
├─ Does it require reading/interacting with UIs?
│  └─ YES → Use Mini ($0.75/$4.50)
│     Examples: web scraping, form filling,
│     screenshot analysis, computer use
│
├─ Does it require >200K context?
│  └─ YES → Use Mini ($0.75/$4.50) or Nano ($0.20/$1.25)
│     Mini for complex reasoning, Nano for simple processing
│
├─ Is writing quality important (user-facing text)?
│  └─ YES → Use Haiku ($0.80/$4.00)
│     Examples: documentation, email drafts,
│     content summaries, user notifications
│
├─ Is instruction-following precision critical?
│  └─ YES → Use Haiku ($0.80/$4.00)
│     Examples: structured extraction with strict formats,
│     safety-sensitive content processing
│
├─ Is it a complex reasoning task (but not frontier-level)?
│  └─ YES → Use Mini ($0.75/$4.50)
│     Examples: code review, research synthesis,
│     multi-step tool use, Codex tasks
│
└─ Still unsure?
   └─ Default to Mini. It's the safest all-around 
      sub-agent at a reasonable price point.

Cost Modeling: What This Means for Real Workloads

Let's make this concrete. Imagine an agent workflow that processes 100 customer support tickets:

Pipeline: Classify ticket → Extract entities → Generate response draft → Review draft

Processing 100 customer tickets for under a dollar. That's the sub-agent economy.

Compare this to running the same pipeline on GPT-5.4 (full): roughly $7.80 for the same 100 tickets. The tiered approach is 10x cheaper with minimal quality degradation, because each tier is matched to the complexity of its task.

Integration Patterns for Multi-Agent Workflows

If you're building with these models in a multi-agent architecture, here are the patterns that work:

Pattern 1: Fan-Out with Nano, Aggregate with Mini

# Nano processes each item cheaply
results = await asyncio.gather(*[
    nano.process(item) for item in items  # $0.20/MTok
])

# Mini synthesizes the results
summary = await mini.synthesize(results)  # $0.75/MTok

This is the most common pattern. Nano does the embarrassingly parallel work, Mini does the synthesis that requires actual reasoning. Works for: search result processing, document analysis, competitive intelligence gathering.

Pattern 2: Haiku as Quality Gate

# Mini generates the output
draft = await mini.generate(task)  # Fast, capable

# Haiku reviews for quality and safety
review = await haiku.review(draft, criteria)  # Precise, careful
if review.passed:
    return draft
else:
    return await mini.revise(draft, review.feedback)

Haiku's instruction-following precision makes it an excellent reviewer. This pattern catches edge cases that Mini might miss, at a fraction of the cost of using a frontier model for review.

Pattern 3: Codex Quota Optimization

# On Codex: Mini uses 30% quota vs 100% for GPT-5.4
# Strategy: Use Mini for all coding sub-tasks
for task in coding_tasks:
    if task.complexity < THRESHOLD:
        result = await codex_mini.execute(task)   # 30% quota
    else:
        result = await codex_full.execute(task)    # 100% quota

On Codex specifically, Mini's 30% quota usage means you can run approximately 3x more coding tasks within the same plan limits. For teams running Codex-heavy workflows, this is the single biggest optimization available.

Provider Lock-In: The Hidden Cost

Here's the thing nobody talks about: mixing OpenAI and Anthropic models in the same pipeline creates real engineering overhead.

Same-provider advantages:

• Unified API client and authentication
• Consistent error handling and retry logic
• Single billing relationship and usage dashboard
• Shared prompt formatting and tool-use conventions

Cross-provider costs:

• Two API client libraries to maintain
• Different prompt formats (system/user/assistant vs Messages API)
• Different tool-use schemas
• Two separate billing systems to monitor
• Different rate-limiting behavior and error codes

The practical advice: If your orchestrator is GPT-5.4, use Mini and Nano as sub-agents. If your orchestrator is Claude Opus 4, use Haiku. The per-token price differences between Mini and Haiku are pennies — the integration simplicity is worth more.

The exception: if you have a specific task where one model dramatically outperforms the others (e.g., Mini for computer use, Haiku for instruction-following), the cross-provider integration cost is justified.

What About Open-Source Alternatives?

It's worth noting that Llama 4 Scout, Qwen 3, and Mistral's latest models are competitive with these budget tiers — often at lower cost when self-hosted or run through providers like Together AI, Fireworks, or Groq.

The tradeoff: open-source models require more infrastructure management, lack the polished tool-use integrations of OpenAI and Anthropic's APIs, and may need more prompt engineering to achieve equivalent quality. For teams with the engineering capacity, they're worth evaluating. For everyone else, Mini/Nano/Haiku are the pragmatic choice.

The Bottom Line

The sub-agent tier has converged on a price point: roughly $0.75-$0.80 per million input tokens for capable models, $0.20 for bulk processing. This isn't accidental — it's the market finding the price where multi-agent workflows become economically viable at scale.

Choose Nano when the task is simple and the volume is high. Classification, extraction, formatting, filtering. The 5x cost advantage over Mini/Haiku compounds fast at scale.

Choose Mini when the task requires real reasoning, tool use, or computer interaction. It's the versatile middle tier that handles most sub-agent work competently. The Codex 30% quota makes it even more attractive for coding workflows.

Choose Haiku when output quality matters — user-facing text, precise instruction-following, safety-critical processing. Anthropic's models produce cleaner output at the budget tier, and if your stack is already on Claude, the integration simplicity seals the deal.

The real insight: Stop thinking about which budget model is "best." Start thinking about which combination matches your workload. The winning strategy isn't picking one — it's building a harness that routes each task to the right tier automatically.

Watch the hands-on breakdown of GPT-5.4 Mini and Nano:

Building multi-agent systems and want to go deeper? Read our full comparison of Claude vs ChatGPT vs Gemini for the frontier tier, or check out the best AI APIs for developers for a complete overview of what's available.