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GPT-5.6 Luna vs Microsoft-Decision-1

Updated October 20, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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Decision reading

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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
OpenAI logo
GPT-5.6 Luna

OpenAI

64.38/100

Supported · Public rank #40

90% interval 59.4–69.4

Model B
Microsoft logo
Microsoft-Decision-1

Microsoft

—

Evidence status unavailable

90% interval unavailable

Shared results
0
GPT-5.6 Luna only
34
Microsoft-Decision-1 only
0
Like-for-like categories
0 / 8
Supported: GPT-5.6 LunaHow 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.

  • Chat turn cost

    1K fresh input + 500 output tokens

    Microsoft-Decision-1

    Microsoft-Decision-1 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

    Microsoft-Decision-1

    Microsoft-Decision-1 has the lower estimated token cost for this stated workload. Microsoft-Decision-1 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

    Microsoft-Decision-1

    Microsoft-Decision-1 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

    Microsoft-Decision-1 is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    Microsoft-Decision-1 is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Not enough matched evidence

    A complete context comparison is not sourced.

    Confidence: limited

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.

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

61.6GPT-5.6 Luna—Microsoft-Decision-1

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

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

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.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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

Not comparable
GPT-5.6 Luna
55.9
Supported · #41/131
Microsoft-Decision-1
Not ranked
Basis
BenchAlign v5.8 lane · 10 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
GPT-5.6 Luna
61.6
Supported · #16/152
Microsoft-Decision-1
Not ranked
Basis
BenchAlign v5.8 lane · 8 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.6 Luna
58.2
#26/27
Microsoft-Decision-1
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.6 Luna
79.6
#19/55
Microsoft-Decision-1
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.6 Luna
64.8
Supported · #38/187
Microsoft-Decision-1
Not ranked
Basis
BenchAlign v5.8 lane · 6 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.6 Luna
Not ranked
Microsoft-Decision-1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.6 Luna
Not ranked
Microsoft-Decision-1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GPT-5.6 Luna
84.0
Unranked · 3 rankable rows
Microsoft-Decision-1
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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
Microsoft-Decision-1
$0.00004
Fit state unavailable

Microsoft-Decision-1 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
Microsoft-Decision-1
$0.0021
Fit state unavailable

Microsoft-Decision-1 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
Microsoft-Decision-1
$0.00924
Fit state unavailable
Cached input priced at the published list-input rate

Microsoft-Decision-1 has the lower modeled cost

Microsoft-Decision-1 has no published cached-input rate, so cached tokens use its listed input rate.

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.

Context window

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

GPT-5.6 Luna

1.05M

OpenAI model catalog

Microsoft-Decision-1

Not sourced

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

Microsoft-Decision-1

Not published

Microsoft-Decision-1 announcement

Provider availability

GPT-5.6 Luna

Generally Available · OpenAI Responses API

OpenAI model catalog

Microsoft-Decision-1

Not sourced

Reasoning profile

GPT-5.6 Luna

Reasoning

Microsoft-Decision-1

Unspecified

Weight access

GPT-5.6 Luna

Proprietary

Microsoft-Decision-1

Proprietary

License

GPT-5.6 Luna

Proprietary

Microsoft-Decision-1

Proprietary

Release date

GPT-5.6 Luna

2026-07-09

Microsoft-Decision-1

2026-10-12

If you already use one of these models

Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.0136 vs $0.0021. Cache-heavy agent loop: $0.02 vs $0.00924.
Context tradeoff
A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GPT-5.6 Luna or Microsoft-Decision-1?

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-5.6 Luna or Microsoft-Decision-1?

Microsoft-Decision-1 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, GPT-5.6 Luna or Microsoft-Decision-1?

Microsoft-Decision-1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GPT-5.6 Luna or Microsoft-Decision-1?

For the stated presets, chat costs $0.0008 on GPT-5.6 Luna and $0.00004 on Microsoft-Decision-1; repository review costs $0.0136 and $0.0021; the cache-heavy agent loop costs $0.02 and $0.00924. Microsoft-Decision-1 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-5.6 Luna or Microsoft-Decision-1?

A complete documented context-window comparison is not available.

Benchmark evidence

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

Browse raw public benchmark evidence34 rows

Agentic

  • Terminal-Bench 3.0

    GPT-5.6 Luna14.3%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • Terminal-Bench 2.1

    GPT-5.6 Luna84.7%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • BrowseComp

    GPT-5.6 Luna83.3%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • OSWorld 2.0

    GPT-5.6 Luna45.6%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • CyberGym

    GPT-5.6 Luna77.9%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • ExploitGym

    GPT-5.6 Luna12.4%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • Toolathlon

    GPT-5.6 Luna53.4%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GPT-5.6 Luna79.0%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • ApprenticeBench

    GPT-5.6 Luna7%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • MazeBench

    GPT-5.6 Luna0%
    Source
    Microsoft-Decision-1—

    Not directly comparable

Coding

  • SWE-bench Pro

    GPT-5.6 Luna62.7%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • Terminal-Bench 2.1

    GPT-5.6 Luna84.7%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • DeepSWE

    GPT-5.6 Luna67.2%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • FrontierCode 1.1 Extended

    GPT-5.6 Luna55.1%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • CursorBench 3.2

    GPT-5.6 Luna61.1%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • VulcanBench v3

    GPT-5.6 Luna85.5%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • SWE-bench (Vals)

    GPT-5.6 Luna93.0%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • CursorBench 4.0

    GPT-5.6 Luna35.9%
    Source
    Microsoft-Decision-1—

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.6 Luna59.5%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • ARC-AGI-3

    GPT-5.6 Luna0.2%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • ARC-AGI-1

    GPT-5.6 Luna88.00%
    Source
    Microsoft-Decision-1—

    Not directly comparable

Multimodal

  • ExtractBench (LlamaIndex)

    GPT-5.6 Luna91.16%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • ParseBench

    GPT-5.6 Luna68.34%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • MMMU-Pro

    GPT-5.6 Luna78.4%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.6 Luna79.5%
    Source
    Microsoft-Decision-1—

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.6 Luna92.3%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • GPQA-D

    GPT-5.6 Luna92.3%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • HealthBench Professional

    GPT-5.6 Luna55.7%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • HealthBench Hard

    GPT-5.6 Luna32.0%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • GPQA Diamond (Vals)

    GPT-5.6 Luna91.7%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • MMLU-Pro (Vals)

    GPT-5.6 Luna86.0%
    Source
    Microsoft-Decision-1—

    Not directly comparable

Math

  • FrontierMath (legacy)

    GPT-5.6 Luna78.6%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.6 Luna78.600%
    Source
    Microsoft-Decision-1—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.6 Luna58.500%
    Source
    Microsoft-Decision-1—

    Not directly comparable

34 public results · 0 shared

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Last updated October 20, 2026