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Model comparison

GPT-5.4 Mini 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 Mini

Balanced

vs

OpenAI

GPT-5.6 Luna

Budget

AI model capability comparison
SpecificationGPT-5.4 MiniGPT-5.6 Luna
ProviderOpenAIOpenAI
TierBalancedBudget
Context window400KWinner: 1.05M
Max output128K128K
Input / 1M tokens$0.75Winner: $0.20
Output / 1M tokens$4.50Winner: $1.20
WeightsClosedClosed
ParametersNot disclosedUnverifiedcheapest GPT-5.6 tier
Reasoning levelsnone, low, medium, highnone, low, medium, high
Modalitiestext, imagetext, image
API model idgpt-5.4-minigpt-5.6-luna
ReleasedMarch 17, 2026July 9, 2026
Artificial Analysis Intelligence Index [high] (2026-08-14)46Winner: 47
SWE-bench Verified (2026-09-01)Not verifiedUnverified93
Terminal-Bench 2.1 (2026-07-09)Not verifiedUnverified84.7
LiveBench (2026-09-05)Not verifiedUnverified73.6

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 Mini: Standard $0.75/$4.50 per MTok; cached input $0.075/MTok; batch $0.375/$2.25. · 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.

BalancedRecord checked September 3, 2026

GPT-5.4 Mini

GPT-5.4 Mini is OpenAI's efficient mid-tier for high-volume, well-scoped tasks at $0.75/$4.50.

Best for

  • High-volume calls
  • Mixed workloads
  • Cost-sensitive production

Watch out

400K context (vs 1.05M on GPT-5.4/5.5/5.6); verify long-context needs.

BudgetRecord checked September 3, 2026

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 $4.50 for GPT-5.4 Mini — about 3.8×. 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 picker

Evidence 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 (scores match) — Measured on: Artificial Analysis Intelligence Index — scores are equivalent, so the benchmark does not separate the pair.
  • 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.

Related comparisons

Diving deeper on one model? GPT-5.4 Mini · GPT-5.6 Luna

Common questions

GPT-5.4 Mini vs GPT-5.6 Luna

Answered from the verified figures on this page rather than general guidance.

Is GPT-5.4 Mini or GPT-5.6 Luna cheaper for input?

GPT-5.6 Luna is cheaper at $0.20 per million input tokens, against $0.75 for GPT-5.4 Mini — roughly 3.8× 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 Mini has tiered pricing: Standard $0.75/$4.50 per MTok; cached input $0.075/MTok; batch $0.375/$2.25. 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.

Is GPT-5.4 Mini or GPT-5.6 Luna cheaper for output?

GPT-5.6 Luna is cheaper at $1.20 per million output tokens, against $4.50 for GPT-5.4 Mini — roughly 3.8× 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 Mini has tiered pricing: Standard $0.75/$4.50 per MTok; cached input $0.075/MTok; batch $0.375/$2.25. 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 Mini or GPT-5.6 Luna?

GPT-5.6 Luna accepts 1.05M tokens against 400K for GPT-5.4 Mini. This only matters if you routinely send very long documents or large codebases.

Do GPT-5.4 Mini 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 Mini or GPT-5.6 Luna?

GPT-5.4 Mini is the balanced tier and GPT-5.6 Luna the budget tier. The useful question is whether your hardest task actually fails on the cheaper one — most production volume such as classification, extraction and summarisation does not.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

  • Input price: GPT-5.6 Luna costs $0.20 per 1M tokens versus $0.75 for GPT-5.4 Mini — a 3.8x difference at the headline tier.
  • Context: GPT-5.6 Luna takes 1.05M against 400K for GPT-5.4 Mini — only decisive if your prompts approach the smaller window.
  • Measured capability: GPT-5.6 Luna leads Artificial Analysis Intelligence Index 47 to 46 (measured 2026-08-14).
  • Positioning: GPT-5.4 Mini sits in the balanced tier, GPT-5.6 Luna in the budget tier — most production volume (classification, extraction, summarisation) does not need the pricier tier.
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