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Model comparison

GPT-4o mini TTS vs GPT-5.6 Luna

Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-4o mini TTS

OpenAI

—

Evidence status unavailable

90% interval unavailable

GPT-5.6 Luna

OpenAI

66.8/100

Estimated · Public rank #23

90% interval 56.4–77.3

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Long documents

    Prompts that approach the documented context limit

    GPT-5.6 Luna

    GPT-5.6 Luna has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GPT-4o mini TTS does not fit this workload in one request. GPT-4o mini TTS has no comparable published API token rate.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GPT-4o mini TTS does not fit this workload in one request. GPT-4o mini TTS has no comparable published API token rate.

    Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
0
GPT-4o mini TTS only
0
GPT-5.6 Luna only
22
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable
GPT-4o mini TTS
Not measured
GPT-5.6 Luna
84.1
Weighted basis
0 vs 2 rows
Reading
Not comparable

Coding

Not comparable
GPT-4o mini TTS
Not measured
GPT-5.6 Luna
62.7
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-4o mini TTS
Not measured
GPT-5.6 Luna
59.5
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-4o mini TTS
Not measured
GPT-5.6 Luna
92.3
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
GPT-4o mini TTS
Not measured
GPT-5.6 Luna
73.6
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-4o mini TTS
Not measured
GPT-5.6 Luna
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-4o mini TTS
Not measured
GPT-5.6 Luna
78.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-4o mini TTS
Not measured
GPT-5.6 Luna
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

GPT-4o mini TTS
API rate not published
Fits in one request
GPT-5.6 Luna
$0.0008
Fits in one request

GPT-4o mini TTS has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-4o mini TTS
API rate not published
Does not fit in one request
GPT-5.6 Luna
$0.0136
Fits in one request

GPT-4o mini TTS does not fit this workload in one request. GPT-4o mini TTS has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GPT-4o mini TTS
API rate not published
Does not fit in one request
Cached-input rate unavailable
GPT-5.6 Luna
$0.02
Fits in one request

GPT-4o mini TTS does not fit this workload in one request. GPT-4o mini TTS has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

Maximum documented context; output-token limits may be lower.

GPT-4o mini TTS

2K

GPT-5.6 Luna

1.05M

OpenAI model catalog

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GPT-4o mini TTS

No comparable hosted API rate

GPT-5.6 Luna

$0.02 per 1M cached input tokens

OpenAI pricing

Provider availability

GPT-4o mini TTS

Not sourced

GPT-5.6 Luna

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

GPT-4o mini TTS

Non-Reasoning

GPT-5.6 Luna

Reasoning

Weight access

GPT-4o mini TTS

Proprietary

GPT-5.6 Luna

Proprietary

License

GPT-4o mini TTS

Proprietary

GPT-5.6 Luna

Proprietary

Release date

GPT-4o mini TTS

2025-03-20

GPT-5.6 Luna

2026-07-09

If you already use one of these models
Deployment change
Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
GPT-5.6 Luna has the larger documented window (1.05M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence22 rows

Agentic

  • Terminal-Bench 2.0

    GPT-4o mini TTS—
    GPT-5.6 Luna84.7%
    Source

    Not directly comparable

  • BrowseComp

    GPT-4o mini TTS—
    GPT-5.6 Luna83.3%
    Source

    Not directly comparable

  • OSWorld 2.0

    GPT-4o mini TTS—
    GPT-5.6 Luna45.6%
    Source

    Not directly comparable

  • CyberGym

    GPT-4o mini TTS—
    GPT-5.6 Luna77.9%
    Source

    Not directly comparable

  • ExploitGym

    GPT-4o mini TTS—
    GPT-5.6 Luna12.4%
    Source

    Not directly comparable

  • Toolathlon

    GPT-4o mini TTS—
    GPT-5.6 Luna53.4%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GPT-4o mini TTS—
    GPT-5.6 Luna62.7%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-4o mini TTS—
    GPT-5.6 Luna84.7%
    Source

    Not directly comparable

  • deepSwe

    GPT-4o mini TTS—
    GPT-5.6 Luna67.2%
    Source

    Not directly comparable

  • FrontierCode 1.1 Extended

    GPT-4o mini TTS—
    GPT-5.6 Luna55.1%
    Source

    Not directly comparable

  • cursorBench32

    GPT-4o mini TTS—
    GPT-5.6 Luna61.1%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-4o mini TTS—
    GPT-5.6 Luna59.5%
    Source

    Not directly comparable

  • ARC-AGI-3

    GPT-4o mini TTS—
    GPT-5.6 Luna0.2%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GPT-4o mini TTS—
    GPT-5.6 Luna92.3%
    Source

    Not directly comparable

  • GPQA-D

    GPT-4o mini TTS—
    GPT-5.6 Luna92.3%
    Source

    Not directly comparable

  • HealthBench Professional

    GPT-4o mini TTS—
    GPT-5.6 Luna55.7%
    Source

    Not directly comparable

  • HealthBench Hard

    GPT-4o mini TTS—
    GPT-5.6 Luna32.0%
    Source

    Not directly comparable

Math

  • FrontierMath (legacy)

    GPT-4o mini TTS—
    GPT-5.6 Luna78.6%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-4o mini TTS—
    GPT-5.6 Luna78.600%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-4o mini TTS—
    GPT-5.6 Luna58.500%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-4o mini TTS—
    GPT-5.6 Luna78.4%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-4o mini TTS—
    GPT-5.6 Luna79.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GPT-4o mini TTS or GPT-5.6 Luna?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GPT-4o mini TTS or GPT-5.6 Luna?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, GPT-4o mini TTS or GPT-5.6 Luna?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, GPT-4o mini TTS or GPT-5.6 Luna?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GPT-4o mini TTS or GPT-5.6 Luna?

GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 2K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 4, 2026

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