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Model comparison

GPT-5.6 Luna vs GPT-5.6 Terra

Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload. This is a same-family comparison, so migration details appear when the source data supports them.

GPT-5.6 Luna

OpenAI

66.9/100

Estimated · Public rank #23

90% interval 56.4–77.3

GPT-5.6 Terra

OpenAI

72.3/100

Estimated · Public rank #12

90% interval 62.6–82.0

GPT-5.6 Terra has the higher public score estimate, 72.29 versus 66.87, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

22 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    GPT-5.6 Terra

    GPT-5.6 Terra leads on the same 1 weighted benchmark row.

    Confidence: limited

  • Agentic work

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

    GPT-5.6 Terra

    GPT-5.6 Terra leads on the same 2 weighted benchmark rows.

    Confidence: limited

  • 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

Show secondary and unsupported calls

  • 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. Costs use the listed standard API rates.

    Confidence: listed-rates

  • 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

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

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
22
GPT-5.6 Luna only
0
GPT-5.6 Terra only
0
Like-for-like categories
6 / 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

Like-for-like
GPT-5.6 Luna
84.1
GPT-5.6 Terra
87.4
Weighted basis
2 vs 2 rows
Reading
GPT-5.6 Terra leads

Coding

Like-for-like
GPT-5.6 Luna
62.7
GPT-5.6 Terra
63.4
Weighted basis
1 vs 1 rows
Reading
GPT-5.6 Terra leads

Reasoning

Like-for-like
GPT-5.6 Luna
59.5
GPT-5.6 Terra
83.9
Weighted basis
1 vs 1 rows
Reading
GPT-5.6 Terra leads

Knowledge

Like-for-like
GPT-5.6 Luna
92.3
GPT-5.6 Terra
92.9
Weighted basis
1 vs 1 rows
Reading
GPT-5.6 Terra leads

Math

Like-for-like
GPT-5.6 Luna
73.6
GPT-5.6 Terra
80.8
Weighted basis
2 vs 2 rows
Reading
GPT-5.6 Terra leads

Multimodal

Like-for-like
GPT-5.6 Luna
78.4
GPT-5.6 Terra
80.7
Weighted basis
1 vs 1 rows
Reading
GPT-5.6 Terra leads

Multilingual

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

Instruction following

Not comparable
GPT-5.6 Luna
Not measured
GPT-5.6 Terra
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.

  • ARC-AGI-2

    Reasoning

    GPT-5.6 Luna: 59.5%GPT-5.6 Terra: 83.9%Normalized gap 24.4Shared source

  • FrontierMath v2 (Tier 4)

    Math

    GPT-5.6 Luna: 58.500%GPT-5.6 Terra: 68.300%Normalized gap 9.8Shared source

  • FrontierMath v2 (Tiers 1-3)

    Math

    GPT-5.6 Luna: 78.600%GPT-5.6 Terra: 84.900%Normalized gap 6.3Shared source

  • BrowseComp

    Agentic

    GPT-5.6 Luna: 83.3%GPT-5.6 Terra: 87.5%Normalized gap 4.2Shared source

  • Terminal-Bench 2.0

    Agentic

    GPT-5.6 Luna: 84.7%GPT-5.6 Terra: 87.4%Normalized gap 2.7Shared source

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-5.6 Luna
$0.0008
Fits in one request
GPT-5.6 Terra
$0.008
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-5.6 Luna
$0.0136
Fits in one request
GPT-5.6 Terra
$0.136
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-5.6 Luna
$0.02
Fits in one request
GPT-5.6 Terra
$0.2
Fits in one request

GPT-5.6 Luna has the lower modeled cost

Costs use the listed standard API rates.

Specification differences

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

Cached-input rate

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

GPT-5.6 Luna

$0.02 per 1M cached input tokens

OpenAI pricing

GPT-5.6 Terra

$0.2 per 1M cached input tokens

OpenAI pricing

Reasoning profile

GPT-5.6 Luna

Reasoning

GPT-5.6 Terra

Reasoning

Weight access

GPT-5.6 Luna

Proprietary

GPT-5.6 Terra

Proprietary

License

GPT-5.6 Luna

Proprietary

GPT-5.6 Terra

Proprietary

Release date

GPT-5.6 Luna

2026-07-09

GPT-5.6 Terra

2026-07-09

If you are choosing between sibling variants
Deployment change
Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
GPT-5.6 Terra has the higher public score estimate, 72.29 versus 66.87, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0136 vs $0.136. Cache-heavy agent loop: $0.02 vs $0.2.
Context tradeoff
Both models list 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

    Shared source
    GPT-5.6 Luna84.7%
    GPT-5.6 Terra87.4%

    GPT-5.6 Terra leads this result

  • BrowseComp

    Shared source
    GPT-5.6 Luna83.3%
    GPT-5.6 Terra87.5%

    GPT-5.6 Terra leads this result

  • OSWorld 2.0

    Shared source
    GPT-5.6 Luna45.6%
    GPT-5.6 Terra50.2%

    GPT-5.6 Terra leads this result

  • CyberGym

    Shared source
    GPT-5.6 Luna77.9%
    GPT-5.6 Terra81.8%

    GPT-5.6 Terra leads this result

  • ExploitGym

    Shared source
    GPT-5.6 Luna12.4%
    GPT-5.6 Terra23.2%

    GPT-5.6 Terra leads this result

  • Toolathlon

    Shared source
    GPT-5.6 Luna53.4%
    GPT-5.6 Terra53.1%

    GPT-5.6 Luna leads this result

Coding

  • SWE-bench Pro

    Shared source
    GPT-5.6 Luna62.7%
    GPT-5.6 Terra63.4%

    GPT-5.6 Terra leads this result

  • Terminal-Bench 2.0

    Shared source
    GPT-5.6 Luna84.7%
    GPT-5.6 Terra87.4%

    GPT-5.6 Terra leads this result

  • deepSwe

    Shared source
    GPT-5.6 Luna67.2%
    GPT-5.6 Terra69.6%

    GPT-5.6 Terra leads this result

  • FrontierCode 1.1 Extended

    Shared source
    GPT-5.6 Luna55.1%
    GPT-5.6 Terra55.8%

    GPT-5.6 Terra leads this result

  • cursorBench32

    Shared source
    GPT-5.6 Luna61.1%
    GPT-5.6 Terra64.9%

    GPT-5.6 Terra leads this result

Reasoning

  • ARC-AGI-2

    Shared source
    GPT-5.6 Luna59.5%
    GPT-5.6 Terra83.9%

    GPT-5.6 Terra leads this result

  • ARC-AGI-3

    Shared source
    GPT-5.6 Luna0.2%
    GPT-5.6 Terra0.8%

    GPT-5.6 Terra leads this result

Knowledge

  • GPQA

    Shared source
    GPT-5.6 Luna92.3%
    GPT-5.6 Terra92.9%

    GPT-5.6 Terra leads this result

  • GPQA-D

    Shared source
    GPT-5.6 Luna92.3%
    GPT-5.6 Terra92.9%

    GPT-5.6 Terra leads this result

  • HealthBench Professional

    Shared source
    GPT-5.6 Luna55.7%
    GPT-5.6 Terra57.7%

    GPT-5.6 Terra leads this result

  • HealthBench Hard

    Shared source
    GPT-5.6 Luna32.0%
    GPT-5.6 Terra32.7%

    GPT-5.6 Terra leads this result

Math

  • FrontierMath (legacy)

    Shared source
    GPT-5.6 Luna78.6%
    GPT-5.6 Terra84.9%

    GPT-5.6 Terra leads this result

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    GPT-5.6 Luna78.600%
    GPT-5.6 Terra84.900%

    GPT-5.6 Terra leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    GPT-5.6 Luna58.500%
    GPT-5.6 Terra68.300%

    GPT-5.6 Terra leads this result

Multimodal

  • MMMU-Pro

    Shared source
    GPT-5.6 Luna78.4%
    GPT-5.6 Terra80.7%

    GPT-5.6 Terra leads this result

  • MMMU-Pro w/ Python

    Shared source
    GPT-5.6 Luna79.5%
    GPT-5.6 Terra82%

    GPT-5.6 Terra leads this result

Frequently asked questions

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

GPT-5.6 Terra has the higher public score estimate, 72.29 versus 66.87, 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-5.6 Luna or GPT-5.6 Terra?

GPT-5.6 Terra leads the like-for-like coding comparison across 1 shared weighted benchmark row.

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

GPT-5.6 Terra leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.

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

For the stated presets, chat costs $0.0008 on GPT-5.6 Luna and $0.008 on GPT-5.6 Terra; repository review costs $0.0136 and $0.136; the cache-heavy agent loop costs $0.02 and $0.2. Costs use the listed standard API rates.

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

Both models list the same context window, 1.05M.

Related comparisons

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Last updated August 10, 2026

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