GPT-5.6 Luna vs GPT-5.6 Terra

Updated September 24, 2026. Rank says GPT-5.6 Terra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty. This is a same-family comparison, so migration details appear when the source data supports them.

Decision reading

GPT-5.6 Terra has the higher public score estimate, 72.58 versus 65.6, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 29 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GPT-5.6 Luna

OpenAI

65.6/100

Supported · Public rank #25

90% interval 60.5–70.7

Model B

GPT-5.6 Terra

OpenAI

72.58/100

Supported · Public rank #9

90% interval 68.5–76.7

Shared results: 29
GPT-5.6 Luna only: 0
GPT-5.6 Terra only: 3
Like-for-like categories: 5 / 8

Supported: GPT-5.6 Luna and GPT-5.6 TerraHow the comparison works

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.

Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.6 Terra
GPT-5.6 Terra leads on the public agentic lane, 59.5 to 55.3, with Supported evidence for both models, although the 90% intervals overlap.
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
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

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
No clear pick
The like-for-like coding result is a practical tie on the public lane (within 0.5 points).
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

64.5GPT-5.6 Luna64.7GPT-5.6 Terra

Like-for-like · BenchAlign v5.7

The like-for-like coding row is a practical tie.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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-2Reasoning
Normalized gap 24.4
GPT-5.6 Luna:59.5%
GPT-5.6 Terra:83.9%
Shared source
FrontierMath v2 (Tier 4)Math
Normalized gap 9.8
GPT-5.6 Luna:58.500%
GPT-5.6 Terra:68.300%
Shared source
FrontierMath v2 (Tiers 1-3)Math
Normalized gap 6.3
GPT-5.6 Luna:78.600%
GPT-5.6 Terra:84.900%
Shared source
OSWorld 2.0Agentic
Normalized gap 4.6
GPT-5.6 Luna:45.6%
GPT-5.6 Terra:50.2%
Shared source
BrowseCompAgentic
Normalized gap 4.2
GPT-5.6 Luna:83.3%
GPT-5.6 Terra:87.5%
Shared source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Like-for-like

GPT-5.6 Luna: 55.3; Supported · #28/105
GPT-5.6 Terra: 59.5; Supported · #18/105
Basis: BenchAlign v5.7 lane · 9 vs 9 public rows
Reading: GPT-5.6 Terra leads · intervals overlap

Coding

Like-for-like

GPT-5.6 Luna: 64.5; Supported · #9/135
GPT-5.6 Terra: 64.7; Supported · #8/135
Basis: BenchAlign v5.7 lane · 7 vs 8 public rows
Reading: Practical tie

Reasoning

Like-for-like

GPT-5.6 Luna: 54.7; #18/19
GPT-5.6 Terra: 65.4; #12/19
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: GPT-5.6 Terra leads

Multimodal

Like-for-like

GPT-5.6 Luna: 67.1; #22/50
GPT-5.6 Terra: 77.2; #16/50
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: GPT-5.6 Terra leads

Knowledge

Like-for-like

GPT-5.6 Luna: 64.6; Supported · #22/158
GPT-5.6 Terra: 71.1; Supported · #9/158
Basis: BenchAlign v5.7 lane · 6 vs 8 public rows
Reading: GPT-5.6 Terra leads · intervals overlap

Multilingual

Not comparable

GPT-5.6 Luna: Not ranked
GPT-5.6 Terra: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.6 Luna: Not ranked
GPT-5.6 Terra: 85.7; #35/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.6 Luna: 94.2; Unranked · 3 rankable rows
GPT-5.6 Terra: 96.8; Unranked · 3 rankable rows
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.6 Luna and GPT-5.6 Terra
Category	GPT-5.6 Luna	GPT-5.6 Terra	Basis	Reading
Agentic	55.3Supported · #28/105	59.5Supported · #18/105	Like-for-likeBenchAlign v5.7 lane · 9 vs 9 public rows	GPT-5.6 Terra leads · intervals overlap
Coding	64.5Supported · #9/135	64.7Supported · #8/135	Like-for-likeBenchAlign v5.7 lane · 7 vs 8 public rows	Practical tie
Reasoning	54.7#18/19	65.4#12/19	Like-for-likeProvisional lane · 2 vs 2 weighted rows	GPT-5.6 Terra leads
Multimodal	67.1#22/50	77.2#16/50	Like-for-likeProvisional lane · 1 vs 1 weighted rows	GPT-5.6 Terra leads
Knowledge	64.6Supported · #22/158	71.1Supported · #9/158	Like-for-likeBenchAlign v5.7 lane · 6 vs 8 public rows	GPT-5.6 Terra leads · intervals overlap
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	85.7#35/124	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	94.2Unranked · 3 rankable rows	96.8Unranked · 3 rankable rows	Not comparableProvisional lane · 2 vs 2 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

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

SpecificationGPT-5.6 LunaGPT-5.6 Terra

Context window

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

GPT-5.6 Luna

1.05M

OpenAI model catalog

GPT-5.6 Terra

1.05M

OpenAI model catalog

API model ID

GPT-5.6 Luna

gpt-5.6-luna

OpenAI model catalog

GPT-5.6 Terra

gpt-5.6-terra

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-5.6 Luna

$0.02 per 1M cached input tokens

OpenAI pricing

GPT-5.6 Terra

$0.2 per 1M cached input tokens

OpenAI pricing

Documented inputs

GPT-5.6 Luna

text, image

OpenAI model catalog

GPT-5.6 Terra

text, image

OpenAI model catalog

Documented outputs

GPT-5.6 Luna

text

OpenAI model catalog

GPT-5.6 Terra

text

OpenAI model catalog

Provider availability

GPT-5.6 Luna

Generally Available · OpenAI Responses API

OpenAI model catalog

GPT-5.6 Terra

Generally Available · OpenAI Responses API

OpenAI model catalog

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, 72.58 versus 65.6, 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.

Questions

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

GPT-5.6 Terra has the higher public score estimate, 72.58 versus 65.6, 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?

The like-for-like coding row is a practical tie on the public lane, 64.5 against 64.7, inside the 0.5-point band BenchLM treats as level.

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

GPT-5.6 Terra leads the public agentic tasks lane, 59.5 to 55.3, with Supported evidence for both models, although the 90% intervals overlap.

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.

Benchmark evidence

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

Browse raw public benchmark evidence32 rows

Agentic

Terminal-Bench 3.0
Shared source
GPT-5.6 Luna14.3%
GPT-5.6 Terra20.8%
GPT-5.6 Terra leads this result
Terminal-Bench 2.1
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
Terminal-Bench 2.1 (Vals)
GPT-5.6 Luna79.0%
Source
GPT-5.6 Terra77.5%
Source
GPT-5.6 Luna leads this result
ApprenticeBench
Shared source
GPT-5.6 Luna7%
GPT-5.6 Terra16%
GPT-5.6 Terra 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 2.1
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
VulcanBench v3
Shared source
GPT-5.6 Luna85.5%
GPT-5.6 Terra87.0%
GPT-5.6 Terra leads this result
SWE-bench (Vals)
GPT-5.6 Luna93.0%
Source
GPT-5.6 Terra95.4%
Source
GPT-5.6 Terra leads this result
LiveCodeBench (Vals)
GPT-5.6 Luna—
GPT-5.6 Terra85.9%
Source
Not directly comparable

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

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

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
GPQA Diamond (Vals)
GPT-5.6 Luna91.7%
Source
GPT-5.6 Terra90.9%
Source
GPT-5.6 Luna leads this result
MMLU-Pro (Vals)
GPT-5.6 Luna86.0%
Source
GPT-5.6 Terra86.7%
Source
GPT-5.6 Terra leads this result
HLE-Verified
GPT-5.6 Luna—
GPT-5.6 Terra51.1%
Source
Not directly comparable
LABBench2
GPT-5.6 Luna—
GPT-5.6 Terra81.2%
Source
Not directly comparable

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

32 public results · 29 shared

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Last updated September 24, 2026

GPT-5.6 Luna vs GPT-5.6 Terra

Decision reading

Which one for your work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

Long documents

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you are choosing between sibling variants

Questions

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

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

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

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

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

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch GPT-5.6 Luna vs GPT-5.6 Terra