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Model comparison

GPT-5.2 vs GPT-5.6 Luna

Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-5.2

OpenAI

57.8/100

Estimated · Public rank #74

90% interval 49.5–66.0

GPT-5.6 Luna

OpenAI

66.9/100

Estimated · Public rank #23

90% interval 56.4–77.3

GPT-5.6 Luna has the higher public score estimate, 66.87 versus 57.77, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

7 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

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-5.6 Luna

    GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    GPT-5.6 Luna

    GPT-5.6 Luna has the lower estimated token cost for this stated workload. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    GPT-5.6 Luna

    GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

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
7
GPT-5.2 only
8
GPT-5.6 Luna only
15
Like-for-like categories
3 / 8

3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Reasoning

Like-for-like
GPT-5.2
52.9
GPT-5.6 Luna
59.5
Weighted basis
1 vs 1 rows
Reading
GPT-5.6 Luna leads

Knowledge

Like-for-like
GPT-5.2
92.4
GPT-5.6 Luna
92.3
Weighted basis
1 vs 1 rows
Reading
GPT-5.2 leads

Math

Like-for-like
GPT-5.2
35.2
GPT-5.6 Luna
73.6
Weighted basis
2 vs 2 rows
Reading
GPT-5.6 Luna leads

Agentic

Directional only
GPT-5.2
55.7
GPT-5.6 Luna
84.1
Weighted basis
2 vs 2 rows
Reading
Directional only

Coding

Directional only
GPT-5.2
70.6
GPT-5.6 Luna
62.7
Weighted basis
2 vs 1 rows
Reading
Directional only

Multimodal

Directional only
GPT-5.2
80.4
GPT-5.6 Luna
78.4
Weighted basis
2 vs 1 rows
Reading
Directional only

Multilingual

Not comparable
GPT-5.2
Not measured
GPT-5.6 Luna
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.2
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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

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-5.2
$0.00875
Fits in one request
GPT-5.6 Luna
$0.0008
Fits in one request

GPT-5.6 Luna has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.2
$0.1295
Fits in one request
GPT-5.6 Luna
$0.0136
Fits in one request

GPT-5.6 Luna has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

GPT-5.2
$0.525
Fits in one request
Cached input priced at the published list-input rate
GPT-5.6 Luna
$0.02
Fits in one request

GPT-5.6 Luna has the lower modeled cost

GPT-5.2 has no published cached-input rate, so cached tokens use its listed input 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-5.2

400K

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-5.2

Not published

GPT-5.6 Luna

$0.02 per 1M cached input tokens

OpenAI pricing

Provider availability

GPT-5.2

Not sourced

GPT-5.6 Luna

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

GPT-5.2

Reasoning

GPT-5.6 Luna

Reasoning

Weight access

GPT-5.2

Proprietary

GPT-5.6 Luna

Proprietary

License

GPT-5.2

Proprietary

GPT-5.6 Luna

Proprietary

Release date

GPT-5.2

2025-12-11

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
GPT-5.6 Luna has the higher public score estimate, 66.87 versus 57.77, but the 90% score intervals overlap.
Workload cost
Repository review: $0.1295 vs $0.0136. Cache-heavy agent loop: $0.525 vs $0.02.
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 evidence30 rows

Agentic

  • BrowseComp

    GPT-5.265.8%
    Source
    GPT-5.6 Luna83.3%
    Source

    GPT-5.6 Luna leads this result

  • OSWorld-Verified

    GPT-5.247.3%
    Source
    GPT-5.6 Luna—

    Not directly comparable

  • Gert Labs

    GPT-5.246.54%
    Source
    GPT-5.6 Luna—

    Not directly comparable

  • JobBench

    GPT-5.234.3%
    Source
    GPT-5.6 Luna—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.2—
    GPT-5.6 Luna84.7%
    Source

    Not directly comparable

  • OSWorld 2.0

    GPT-5.2—
    GPT-5.6 Luna45.6%
    Source

    Not directly comparable

  • CyberGym

    GPT-5.2—
    GPT-5.6 Luna77.9%
    Source

    Not directly comparable

  • ExploitGym

    GPT-5.2—
    GPT-5.6 Luna12.4%
    Source

    Not directly comparable

  • Toolathlon

    GPT-5.2—
    GPT-5.6 Luna53.4%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    GPT-5.280%
    Source
    GPT-5.6 Luna—

    Not directly comparable

  • SWE-bench Pro

    GPT-5.255.6%
    Source
    GPT-5.6 Luna62.7%
    Source

    GPT-5.6 Luna leads this result

  • Vibe Code Bench

    GPT-5.253.50%
    Source
    GPT-5.6 Luna—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.2—
    GPT-5.6 Luna84.7%
    Source

    Not directly comparable

  • deepSwe

    GPT-5.2—
    GPT-5.6 Luna67.2%
    Source

    Not directly comparable

  • FrontierCode 1.1 Extended

    GPT-5.2—
    GPT-5.6 Luna55.1%
    Source

    Not directly comparable

  • cursorBench32

    GPT-5.2—
    GPT-5.6 Luna61.1%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.252.9%
    Source
    GPT-5.6 Luna59.5%
    Source

    GPT-5.6 Luna leads this result

  • ARC-AGI-3

    GPT-5.2—
    GPT-5.6 Luna0.2%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.292.4%
    Source
    GPT-5.6 Luna92.3%
    Source

    GPT-5.2 leads this result

  • GPQA-D

    GPT-5.2—
    GPT-5.6 Luna92.3%
    Source

    Not directly comparable

  • HealthBench Professional

    GPT-5.2—
    GPT-5.6 Luna55.7%
    Source

    Not directly comparable

  • HealthBench Hard

    GPT-5.2—
    GPT-5.6 Luna32.0%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.240.700%
    Source
    GPT-5.6 Luna78.600%
    Source

    GPT-5.6 Luna leads this result

  • FrontierMath v2 (Tier 4)

    GPT-5.218.800%
    Source
    GPT-5.6 Luna58.500%
    Source

    GPT-5.6 Luna leads this result

  • FrontierMath (legacy)

    GPT-5.2—
    GPT-5.6 Luna78.6%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.279.5%
    Source
    GPT-5.6 Luna78.4%
    Source

    GPT-5.2 leads this result

  • MathVision

    GPT-5.283.0%
    Source
    GPT-5.6 Luna—

    Not directly comparable

  • CharXiv

    GPT-5.282.1%
    Source
    GPT-5.6 Luna—

    Not directly comparable

  • V*

    GPT-5.275.9%
    Source
    GPT-5.6 Luna—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.2—
    GPT-5.6 Luna79.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.2 or GPT-5.6 Luna?

GPT-5.6 Luna has the higher public score estimate, 66.87 versus 57.77, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GPT-5.2 or GPT-5.6 Luna?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GPT-5.2 or GPT-5.6 Luna?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GPT-5.2 or GPT-5.6 Luna?

For the stated presets, chat costs $0.00875 on GPT-5.2 and $0.0008 on GPT-5.6 Luna; repository review costs $0.1295 and $0.0136; the cache-heavy agent loop costs $0.525 and $0.02. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-5.2 or GPT-5.6 Luna?

GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 400K.

Related comparisons

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Last updated August 10, 2026

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