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OPTERA LABS

GPT-5.6 Luna VS Claude Haiku 4.5

2026 Cost & Performance Comparison
Model A · OpenAI

GPT-5.6 Luna

gpt-5-6-luna

Intelligence Score85%
Cost / 1M Tokens$0.50

70% in · 30% out mix

Value Index(score÷cost)
170.0

Higher = better value

Speed

98/100

Context

1.1M

Tier

fast

Model B · Anthropic

Claude Haiku 4.5

claude-haiku-4-5

Intelligence Score82%
Cost / 1M Tokens$2.20

70% in · 30% out mix

Value Index(score÷cost)
37.3

Higher = better value

Speed

97/100

Context

200K

Tier

fast

IN-DEPTH ANALYSIS

GPT-5.6 Luna vs Claude Haiku 4.5: Detailed Comparison

GPT-5.6 Luna is OpenAI's lightweight-tier language model with a 1.1M-token context window, excelling at data extraction. Claude Haiku 4.5 from Anthropic is a lightweight-tier model supporting 200K tokens in context, with standout performance in data extraction.

GPT-5.6 Luna is both the cheaper and the stronger model here — it costs 77% less than Claude Haiku 4.5 on a typical prompt/completion mix and still leads on combined coding and reasoning by 4 points. There is no tradeoff to weigh on this pair: unless you need something specific from Claude Haiku 4.5, the cheaper model is simply the better one. GPT-5.6 Luna is priced at $0.20/M input tokens and $1.20/M output tokens. Claude Haiku 4.5 costs $1.00/M input and $5.00/M output.

In independent benchmark evaluations, GPT-5.6 Luna leads with coding scores of 84/100 and reasoning scores of 85/100, compared to Claude Haiku 4.5's 83/100 in coding and 82/100 in reasoning.

Capability breakdown

Across the five core benchmark categories, here is how GPT-5.6 Luna and Claude Haiku 4.5 stack up head to head:

coding
84
83
reasoning
85
82
data extraction
92
91
creative tasks
84
82
vision/multimodal
85
80

Best model by task

  • coding: GPT-5.6 Luna wins with 84/100
  • reasoning: GPT-5.6 Luna wins with 85/100
  • data extraction: GPT-5.6 Luna wins with 92/100
  • creative tasks: GPT-5.6 Luna wins with 84/100
  • vision/multimodal: GPT-5.6 Luna wins with 85/100

Estimated monthly cost at scale

At 10M + 2M per month, GPT-5.6 Luna runs about $4.40 while Claude Haiku 4.5 runs about $20.00 — GPT-5.6 Luna saves roughly $15.60 (78%) every month.

What actually decides it

GPT-5.6 Luna and Claude Haiku 4.5 come from different labs, which means different tokenizers, different API shapes, and a second vendor relationship. The same English text does not produce the same token count on both, so a price-per-million comparison understates the difference — measure your own prompts on each before treating the headline rates as the full story.

GPT-5.6 Luna and Claude Haiku 4.5 were released within 9 months of each other, so they are competing on the same evaluations under roughly the same conditions. That makes a direct benchmark comparison meaningful here in a way it usually is not — neither model has the advantage of being measured on a newer, easier set of tests.

The context gap is the largest single difference on this pair: GPT-5.6 Luna takes 1.1M tokens against 200K for Claude Haiku 4.5, roughly 5.3x. That is the difference between feeding in a whole repository or a full contract set and having to chunk it. If your work involves documents you cannot split cleanly, this decides it on its own.

Throughput is close enough to ignore — 98/100 versus 97/100. Neither model will feel noticeably quicker in an interactive product, so latency is not a reason to choose between them.

GPT-5.6 Luna wins this comparison outright — cheaper and stronger. Choose Claude Haiku 4.5 only if it has a specific capability you need; on price and benchmarks it is behind on both.

Benchmark Comparison

Head-to-head scores across 5 categories — sourced from official evals

CategoryGPT-5.6 LunaClaude HaikuWinner

Coding

84
83
A

Reasoning

85
82
A

Extraction

92
91
A

Creative

84
82
A

Vision

85
80
A
GPT-5.6 Luna: 5 wins
Claude Haiku 4.5: 0 wins
GPT-5.6 Luna leads overall

Speed Score

98/100vs97/100
GPT-5.6Claude

Context Window

1050Kvs200K
GPT-5.6Claude

What Is a Token?

Models don't read words — they process tokens.

A token is roughly 4 characters of English text (~¾ of a word). Your API bill is priced per million tokens — understanding this directly reduces your costs.

Short phrase

"Hello, world!"

4 tokens
GPT-5.6 Luna
$0.08
Claude Haiku 4.5
$0.43

Business email

One typical email (~200 words)

~270 tokens
GPT-5.6 Luna
$5.40
Claude Haiku 4.5
$29.16

Code file

50-line Python script

~400 tokens
GPT-5.6 Luna
$8.00
Claude Haiku 4.5
$43.20

Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both GPT-5.6 Luna and Claude Haiku 4.5, so the number only becomes meaningful at production volume. Input tokens only; add your output volume in the calculator below.

How to check your token usage

response.usage.total_tokens

Every API response includes a usage object. Sum total_tokens across all calls to get your monthly figure, then use the calculator below.

Your Cost Calculator

Enter your actual monthly token usage to see real savings

Quick Presets

30.0M TOKENS
Prompt 70%Completion 30%
CHEAPER

GPT-5.6 Luna

$15.00/mo

$180.00/yr

$0.2/M in$1.2/M out

Claude Haiku 4.5

$66.00/mo

$792.00/yr

$1/M in$5/M out

Annual Savings

$612.00 saved per year

GPT-5.6 Luna cheaper · $51.00/mo

Deep-Dive Audit — GPT-5.6 Luna & Claude Haiku 4.5

SURGICAL AUDIT LAB188568E2

Surgically Auditing: Deep Logic

Leakage Detected

3-YEAR STRATEGIC LOSS PROJECTION

-$38.196

Without optimization protocols, current model choices will result in -$12.732 capital loss per year.

EFFICIENCY SCORE

85%

Deep Logic

This model achieves a 85 benchmark score in this category.

CATEGORY GAP

15 pts

Distance from Leader

Competitive Landscape Analysis

Source: MMLU-Pro + GPQA Diamond (Apr 2026)

Category Champion: Claude Fable 5

According to MMLU-Pro + GPQA Diamond (Apr 2026) data, Claude Fable 5 provides the optimum balance for Deep Logic tasks.

Market Score

%100

Savings Rate

%-76

Operational Prescription

  • Implement model cascading to optimize token spend.
  • Analyze complex_reasoning data to leverage local semantic caching.

COST AUDIT PROTOCOL

Overkill Detected

"GPT-5.6 Luna is overpriced for this task type. Claude Fable 5 scores 100 in this category at a fraction of the cost."

Categorical Alternative Opportunity

"Claude Fable 5 leads this category with 100 points according to MMLU-Pro + GPQA Diamond (Apr 2026) data."

Inertia Tax Detected

"85% of traffic can be routed to cheaper models. Fast tier (GPT-5 Nano) and Smart tier (o3-mini) can save $-1.06/month."

3-Tier Intelligent Routing Architecture

-76% SAVINGS VIA ROUTING
Fast Tier
50%

GPT-5 Nano

IQ Score: 72/100

$18.00/yr

Smart Tier
35%

o3-mini

IQ Score: 97/100

$277.20/yr

Power Tier
15%

DeepSeek R1

IQ Score: 97/100

$59.184/yr

Fast Tier 50%Smart Tier 35%Power Tier 15%

Without tiered routing, you pay the 'Inertia Tax' — routing all traffic to the most expensive model regardless of task complexity. Tiered cascade eliminates $0.00/year in avoidable overhead.

Deep Logic — Model Cost / Quality Matrix

Source: MMLU-Pro + GPQA Diamond (Apr 2026)
ModelBenchmarkInput (per M)Output (per M)Annual Cost*Value Index
DeepSeek V3
91/100
$0.28$0.42$8.40
45/100
GPT-5.6 LunaSELECTED
85/100
$0.20$1.20$16.80
21/100
DeepSeek V3.2
83/100
$0.26$0.38$7.68
45/100
Claude Haiku 4.5
82/100
$1.00$5.00$72.00
5/100
Gemini 2.0 Flash
81/100
$0.10$0.40$6.00
56/100
Claude 3.5 Haiku
80/100
$0.80$4.00$57.60
6/100
Llama 3 70B
79/100
$0.65$2.75$40.80
8/100
GPT-4o Mini
78/100
$0.15$0.60$9.00
36/100
Gemini 1.5 Flash
76/100
$0.07$0.30$4.50
70/100
GPT-5 NanoBEST VALUE
72/100
$0.10$0.15$3.00
100/100
Claude 3 Haiku
70/100
$0.25$1.25$18.00
16/100

* Annual cost for given volumes. Value Index = Score / Cost (Higher = Best Value).

Tactical Code Gen
// iOPTERA Surgical Routing Wrapper
const auditModel = async (prompt: string) => {
  const complexity = measureComplexity(prompt);
  
  // Tactical Cascade Logic
  if (complexity < 0.45) {
    // Redirect simple tasks to efficient model
    return await llm.call("iOPTERA Optimization", prompt); 
  }
  
  // High-latency routing for complex reasoning
  return await llm.call("Claude Fable 5", prompt);
};
READY TO DEPLOY IN Vercel Edge OR AWS Lambda

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