Model comparison
GPT-5.4 Nano vs GPT-5.6 Luna
Two OpenAI tiers compared on the figures that decide which one a workload actually needs.
Catalog record checked September 3, 2026Individual provider fields may changeEvidence confidence: High — see receipts below
OpenAI
GPT-5.4 Nano
Budget
OpenAI
GPT-5.6 Luna
Budget
| Specification | GPT-5.4 Nano | GPT-5.6 Luna |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Tier | Budget | Budget |
| Context window | 400K | Winner: 1.05M |
| Max output | 128K | 128K |
| Input / 1M tokens | $0.20 | $0.20 |
| Output / 1M tokens | $1.25 | Winner: $1.20 |
| Weights | Closed | Closed |
| Parameters | Not disclosedUnverified | cheapest GPT-5.6 tier |
| Reasoning levels | none, low, medium, high | none, low, medium, high |
| Modalities | text, image | text, image |
| API model id | gpt-5.4-nano | gpt-5.6-luna |
| Released | March 17, 2026 | July 9, 2026 |
| Artificial Analysis Intelligence Index [high] (2026-08-14) | 40 | Winner: 47 |
| SWE-bench Verified (2026-09-01) | Not verifiedUnverified | 93 |
| Terminal-Bench 2.1 (2026-07-09) | Not verifiedUnverified | 84.7 |
| LiveBench (2026-09-05) | Not verifiedUnverified | 73.6 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Benchmark receipts
- 2026-09-05: LiveBench official leaderboard (benchmark-owned), max effortContamination-resistant general capability across reasoning, coding, math, data analysis, and language, with monthly question refreshes. Comparability: comparable with caveat — Rolling question set: observations months apart measure different question mixes. Record the measurement date and compare within ~1 month windows.
- 2026-09-01: vals.ai SWE-bench Verified leaderboard (independent, mini-swe-agent bash-only) (independent; OpenAI did not report SWE-bench Verified for the GPT-5.6 family)Real GitHub issue resolution: does the model's patch pass the hidden tests. Comparability: comparable with caveat — Post-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.
- 2026-08-14: Artificial AnalysisComposite index blending reasoning, knowledge, and coding evals into one 0–100 score. Comparability: directly comparable — AA occasionally rebaselines the index scale between snapshots — a score captured on one date is only comparable to same-snapshot scores (check measuredAt).
- 2026-07-09: OpenAI GPT-5.6 announcement (vendor; chart transcribed by Vellum)Agentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
Pricing tiers: GPT-5.4 Nano: Standard $0.20/$1.25 per MTok; cached input $0.02/MTok; batch $0.10/$0.625. · GPT-5.6 Luna: Launch list was $1/$6; cut to $0.20/$1.20 on 2026-07-30. Cached input $0.02/MTok; batch $0.10/$0.60.
GPT-5.4 Nano
GPT-5.4 Nano is OpenAI's cheapest small tier for classification and extraction at $0.20/$1.25.
Best for
- Classification
- Summarisation
- High-volume chat
Watch out
Smallest tier; not for hard reasoning or coding.
GPT-5.6 Luna
GPT-5.6 Luna is OpenAI's cheapest tier — roughly 1/25 of Opus 5-class input cost, suitable where quality requirements are modest.
Best for
- Classification
- High-volume chat
- Latency-sensitive pipelines
Watch out
Smallest GPT-5.6 tier; verify quality holds before routing frontier work to it.
When the cheaper one wins
GPT-5.6 Luna is cheaper on output at $1.20 per million tokens against $1.25 for GPT-5.4 Nano — about 1.0×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. These are standard-tier API rates, excluding batch and cache discounts.
Run the model pickerEvidence confidence: High
How strong and complete the evidence behind this comparison is — not a prediction of which model is better.
- Pricing verified on both sides — Input and output rates are verified for both models.
- 4/5 core specs verified on both sides — Not published for at least one side: parameter count.
- 1 shared named benchmark with differing scores — Measured on: Artificial Analysis Intelligence Index.
- Verified within the last 90 days — Newest catalog check was 8 days ago.
- Both models carry source citations — Each side has at least two catalog sources on record.
Source receipts
Each catalog figure was checked against the provider or an independent second source on the date shown.
- GPT-5.4 Nano: OpenAI — GPT-5.4 Mini & Nano (accessed 2026-08-29)
- GPT-5.4 Nano: OpenAI API pricing (gpt-5.4-nano $0.20/$1.25) (accessed 2026-08-29)
- GPT-5.6 Luna: OpenAI — GPT-5.6 (accessed 2026-08-29)
- GPT-5.6 Luna: OpenAI API pricing (gpt-5.6-luna $0.20/$1.20) (accessed 2026-08-29)
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- GPT-5.6 Luna vs Grok 4.6
- Claude Haiku 4.5 vs GPT-5.4 Nano
Diving deeper on one model? GPT-5.4 Nano · GPT-5.6 Luna
Common questions
GPT-5.4 Nano vs GPT-5.6 Luna
Answered from the verified figures on this page rather than general guidance.
Is GPT-5.4 Nano or GPT-5.6 Luna cheaper for input?
Both cost $0.20 per million input tokens at standard rates, so input price is not a deciding factor between them.
Is GPT-5.4 Nano or GPT-5.6 Luna cheaper for output?
GPT-5.6 Luna is cheaper at $1.20 per million output tokens, against $1.25 for GPT-5.4 Nano — roughly 1.0× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.4 Nano has tiered pricing: Standard $0.20/$1.25 per MTok; cached input $0.02/MTok; batch $0.10/$0.625. GPT-5.6 Luna has tiered pricing: Launch list was $1/$6; cut to $0.20/$1.20 on 2026-07-30. Cached input $0.02/MTok; batch $0.10/$0.60.
Which has the larger context window, GPT-5.4 Nano or GPT-5.6 Luna?
GPT-5.6 Luna accepts 1.05M tokens against 400K for GPT-5.4 Nano. This only matters if you routinely send very long documents or large codebases.
Do GPT-5.4 Nano and GPT-5.6 Luna support the same reasoning levels?
Yes — both accept the same effort settings: "none", "low", "medium", "high". Higher effort costs more and takes longer, so start low and raise it only where output quality actually improves.
Should I use GPT-5.4 Nano or GPT-5.6 Luna?
Both sit in the budget tier, so the choice usually comes down to price and context rather than capability. GPT-5.4 Nano suits classification; GPT-5.6 Luna suits classification.
Next step
Choosing between them
The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.
- Context: GPT-5.6 Luna takes 1.05M against 400K for GPT-5.4 Nano — only decisive if your prompts approach the smaller window.
- Measured capability: GPT-5.6 Luna leads Artificial Analysis Intelligence Index 47 to 40 (measured 2026-08-14).
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.