GPT-5.6 Luna
gpt-5-6-luna
70% in · 30% out mix
Higher = better value
Speed
98/100
Context
1.1M
Tier
fast
Llama 3 70B
llama-3-70b
70% in · 30% out mix
Higher = better value
Speed
92/100
Context
8K
Tier
fast
IN-DEPTH ANALYSIS
GPT-5.6 Luna vs Llama 3 70B: Detailed Comparison
GPT-5.6 Luna is OpenAI's lightweight-tier language model with a 1.1M-token context window, excelling at data extraction. Llama 3 70B from Meta is a lightweight-tier model supporting 8K tokens in context, with standout performance in data extraction.
GPT-5.6 Luna is both the cheaper and the stronger model here — it costs 61% less than Llama 3 70B on a typical prompt/completion mix and still leads on combined coding and reasoning by 14 points. There is no tradeoff to weigh on this pair: unless you need something specific from Llama 3 70B, 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. Llama 3 70B costs $0.65/M input and $2.75/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 Llama 3 70B's 76/100 in coding and 79/100 in reasoning.
Capability breakdown
Across the five core benchmark categories, here is how GPT-5.6 Luna and Llama 3 70B stack up head to head:
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 Llama 3 70B runs about $12.00 — GPT-5.6 Luna saves roughly $7.60 (63%) every month.
What actually decides it
GPT-5.6 Luna and Llama 3 70B 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 Llama 3 70B are 27 months apart, which is more than one generation in this market. Benchmark comparisons across that gap flatter the older model: it was measured against the evaluations that existed at the time. Treat Llama 3 70B's scores as a floor for what it does well and be sceptical of a close-looking result.
The context gap is the largest single difference on this pair: GPT-5.6 Luna takes 1.1M tokens against 8K for Llama 3 70B, roughly 128.2x. 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 92/100. Neither model will feel noticeably quicker in an interactive product, so latency is not a reason to choose between them.
One practical asymmetry: GPT-5.6 Luna offers a batch API at 50% off standard rates, and Llama 3 70B does not. For anything that does not need an answer immediately — nightly enrichment, backfills, evaluation runs — that discount can be worth more than the difference in list price, and it is easy to overlook when comparing headline rates.
GPT-5.6 Luna wins this comparison outright — cheaper and stronger. Choose Llama 3 70B 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
Coding
Reasoning
Extraction
Creative
Vision
Speed Score
Context Window
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!"
- GPT-5.6 Luna
- $0.08
- Llama 3 70B
- $0.27
Business email
One typical email (~200 words)
- GPT-5.6 Luna
- $5.40
- Llama 3 70B
- $17.90
Code file
50-line Python script
- GPT-5.6 Luna
- $8.00
- Llama 3 70B
- $26.52
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 Llama 3 70B, 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_tokensEvery 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
GPT-5.6 Luna
$15.00/mo
$180.00/yr
Llama 3 70B
$38.40/mo
$460.80/yr
Annual Savings
$280.80 saved per year
GPT-5.6 Luna cheaper · $23.40/mo
Deep-Dive Audit — GPT-5.6 Luna & Llama 3 70B
Surgically Auditing: Deep Logic
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%
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 ROUTINGGPT-5 Nano
IQ Score: 72/100
$18.00/yr
o3-mini
IQ Score: 97/100
$277.20/yr
DeepSeek R1
IQ Score: 97/100
$59.184/yr
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)| Model | Benchmark | Input (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).
// 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);
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